[B1] Adaptive Control of Systems with Actuator and Sensor Nonlinearities
[B2] Adaptive Control of Nonsmooth Dynamic Systems
[B3] Control of Sandwich Nonlinear Systems
[B4] Adaptive Control Design and Analysis
[B5] Adaptive Control of Systems with Actuator Failures
[B6] Advances in Control Systems Theory and Applications
Gang Tao and Petar Kokotovic
Imperfections of system components, especially those of actuators and sensors, are among the factors that severely limit the performance of feedback control loops, the vital parts of industrial automation, consumer electronics, and defense and transportation systems. Most often, a critical imperfection is a nonlinearity which is poorly known, increases with wear and tear, and varies from component to component. Components without such imperfections are costly to manufacture, and their maintenance usually requires specialized personnel.
It is appealing to think of more intelligent approaches to increase the accuracy achievable with imperfect, but sturdy and inexpensive components. Can the control system, after a period of learning or adaptation, recognize the imperfection and compensate for its harmful effects? With such adaptive controllers, the component specifications could be greatly relaxed, their cost reduced, and their reliability increased.
This book points to a direction in which this goal can be achieved for some of the most common component imperfections: dead-zone, backlash, and hysteresis. These ``hard'' nonlinearities are ubiquitous in a wide variety of components: mechanical, hydraulic, pneumatic, magnetic, piezoelectric, etc. They often serve as aggregate representations of more complex microscopic phenomena: friction, viscosity, elasticity, etc. While the ``hard'' nonlinearities have all but disappeared from the academic texts, they have become more common in engineering practice, because feedback controls have entered many new areas of applications. In particular, control systems have contributed to recent dramatic increases in fuel efficiency, drivability, and safety of passenger cars. Such successful applications show that it is more rational to improve performance with control algorithms than with more expensive mechanical components. The adaptive inverse methodology presented in this book is aimed in this direction.
The nonlinearities in this book are approximated by piecewise linear characteristics. A difficulty with such characteristics is that they have break-points, so that they are not differentiable. Existing adaptive control techniques are not applicable to such nonlinearities. However, a major advantage of the piecewise linear characteristics is that they admit linear parametrization with unknown break-point and slope parameters. This property is crucial for effective design and implementation of robust adaptive control, one of the main subjects of this book. The unifying theme of the book is its adaptive inverse approach. Not only are the nonlinear characteristics linear in their parameters, but so are their inverses, which, in the case of dead-zone and backlash, are discontinuous. While the inverses of the actuator nonlinearities are explicit, those of the sensors have a more complicated implicit form. The essence of the adaptive inverse approach is that, upon an adaptation transient, the inverse cancels the effects of the unknown nonlinear characteristic. In this way a significant improvement of accuracy and performance is achieved with inexpensive components. In other words, the adaptation in the controller has ``removed'' the imperfection of the component.
All the results in this book are new and have evolved from the recent journal papers of the authors. The style of presentation is aimed at an audience of practicing engineers and graduate students in electrical, mechanical, chemical, aeronautical, and computer engineering departments, as well as those pursuing interdisciplinary studies such as biomedical engineering. The assumed background is a standard course in control theory, while the required knowledge of model reference adaptive control is concisely presented in Appendix A.
Our interest in the problem of adaptive compensation of ``hard'' nonlinearities was ignited by Jim Winkelman and Doug Rhode, our colleagues at Ford Motor Company. Several years ago, they presented to us and Darrel Recker (then a Ph.D. student, now a researcher at Ford) a problem with a hydraulic valve dead-zone in an automotive suspension system. The dead-zone's purpose was to prevent the leakage and maintain the height when the car was parked and the engine was turned off. However, when the suspension was active, the effect of the dead-zone was harmful. In his Ph.D. thesis, Darrel Recker addressed the problem of using adaptation to remove the harmful effects of the dead-zone. His successful algorithms and experiments have encouraged us to pursue a broader investigation in this direction. We acknowledge with gratitude the pioneering contributions of Darrel Recker and his cooperation in this project. We also greatly benefited from the experience of Doug Rhode and Jim Winkelman. For our understanding of hydraulic components we are indebted to Vladimir Kokotovic, also at Ford. For many years we have been inspired and helped by Petros Ioannou, University of Southern California, without whose vast knowledge of robust adaptive control a project like this would not have been possible. With their patience and understanding our wives, Lanlin and Anna, generously contributed to the writing of this book.
Our research summarized in this book was not only initiated, but also
financially supported by, the Ford Motor Company. It was also supported by
the National Science Foundation grant ECS-9203491 and RIA ECS-9307545 and by
the Air Force Office of Scientific Research grant F-49620-92-J-0495.
Gang Tao
Charlottesville, Virginia
Petar Kokotovic
Santa Barbara, California
Chapter 1 shows the evolution of the new adaptive inverse approach.
Chapter 2 explains the importance and relevance of the control problem with
nonsmooth nonlinearities.
The key component of the proposed approach,
the inverse, is introduced in Chapter 3, for an actuator nonlinearity.
Control designs with a fixed inverse, exact or detuned, continuous-time or
discrete-time or hybrid, are developed in Chapter 4 for systems
with actuator nonlinearities.
Like neither an exact inverse which needs the
nonlinearity knowledge nor a detuned inverse which results in a compensation
error, an adaptive inverse, introduced in Chapter 5, is able to
adaptively cancel the effects of an unknown nonlinearity.
With such an
adaptive inverse, adaptive inverse controllers are designed in Chapter 6
in continuous time and in Chapter 7 in discrete time, for systems with
actuator nonlinearities.
A sensor nonlinearity is more difficult to deal
with, as indicated in Chapter 8, where a more sophisticated inverse design is
also presented to achieve the desired output matching.
Chapter 9 develops
adaptive inverse control designs for systems with sensor nonlinearities.
With partial system knowledge, the order of an adaptive control design
can be reduced and the performance can be improved, as shown in Chapter 10.
As a further development of the adaptive inverse approach, Chapter 11 has
the desired inverse control designs for a class of sandwich nonlinear systems,
those with both actuator and sensor nonlinearities.
Appendix A summarizes
the model reference adaptive control theory in a unified and compact form
for both the continuous-time and discrete-time designs with new proofs of
the desired stability and tracking properties.
The closed-loop signal
boundedness with an adaptive inverse controller is proved in Appendix B for
the continuous-time case, in Appendix C for the discrete-time
case, and in Appendix D for sensor nonlinearity cases.
Bibliography
has the most important references, in particular, the complete collection of
the recent results, in the related research areas.
Finally, Index helps
locating many new concept items used throughout the book.
Gang Tao and Frank F. Lewis, Eds.
Nonsmooth nonlinearities such as backlash, dead-zone, component failure,
friction, hysteresis, saturation and time delays are common in industrial
control systems. Such nonlinearities are usually poorly known and may vary
with time, and they often limit system performance. Control of systems with
nonsmooth nonlinearities is an important area of control systems
research. A desirable control design approach for such systems
should be able to accommodate system uncertainties. Adaptive methods for the
control of systems with unknown nonsmooth nonlinearities
are particularly attractive in many applications because adaptive control
designs are able to provide adaptation mechanisms to adjust controller
parameters in the presence of parametric, structural and environmental
uncertainties. Most adaptive or nonlinear control techniques reported in the
literature are for linear systems or for some classes of systems
with smooth nonlinearities, but not for nonsmooth nonlinearities. The need
for effective control methods to deal with nonsmooth nonlinear systems has
motivated growing research activities in adaptive control of systems with
such common practical nonsmooth nonlinearities. Recently, there have been
many encouraging new results on adaptive control problems with backlash,
dead-zone, failures, friction, hysteresis, saturation, and time delays. This
book, entitled Adaptive Control of Nonsmooth Dynamic Systems , is aimed
at reflecting the state of the art in designing, analyzing and implementing
adaptive control methods which are able to accommodate uncertain nonsmooth
nonlinearities in industrial control systems.
Backlash, dead-zone, component failure, friction, hysteresis, saturation, and
time delays are the most common nonsmooth nonlinearities in industrial
control systems. Backlash, a dynamic (with memory) characteristic, exists in
mechanical couplings such as gear trains, and always limits the accuracy of
servo-mechanisms. Dead-zone is a static input-output relationship which for a
range of input values gives no output; it also limits system
performance. Dead-zone characteristics are often present in amplifiers,
motors, hydraulic valves and even in biomedical actuation systems. Failures
of different types in actuators, sensors and other components of a control
system can cause major system performance deterioration. Friction exists
wherever there is motion or tendency for motion between two physical
components. Friction can cause a steady-state error or a limit cycle near the
reference position and stick-slip phenomenon at low speed in the conventional
linear control of positioning systems.
Hysteresis, another dynamic characteristic, exists in electromagnetic
and piezoelectric actuators which are used for micromotion control and
high-accuracy positioning. Saturation is always a potential problem for
actuators of control systems---all actuators do saturate at some
level. Actuator saturation affects the transient performance and even leads
to system instability. Time delays are also important factors to deal with in
order to improve control system performance such as for teleoperations and in
real-time computer control systems.
Although backlash, dead-zone, failure, friction, hysteresis, saturation, and
time delay characteristics are different, they are all nonsmooth in nature.
Therefore, most existing adaptive control methods are not
applicable. Unfortunately these nonlinearities can
severely limit the performance of feedback systems
if not compensated properly. Moreover, adaptive control of dynamic systems
with each of these nonsmooth characteristics is a control problem
that needs a systematic treatment. It makes the control problem even more
challenging when there are more than one nonlinear characteristic
present in the control system.
In this book it will be shown how nonsmooth nonlinear industrial
characteristics can be adaptively compensated and how desired system
performance is achieved in the presence of such nonlinearities. The book has
16 chapters on issues including system modeling, control design, analysis of
stability and robustness, simulation and implementation:
Chapter One: New Models and Identification Methods for Backlash and Gear
Play, by M. Nordin, P. Bodin and P.-O. Gutman
Chapter Two: Adaptive Dead Zone Inverses for Possibly Nonlinear Control
Systems, by E.-W. Bai
Chapter Three: Deadzone Compensation in Motion Control
Systems Using Augmented Multilayer Neural Networks,
by R. R. Selmic and F. L. Lewis
Chapter Four: On-line Fault Detection, Diagnosis, Isolation
and Accommodation of Dynamical Systems with Actuator Failures,
by M. A. Demetriou and M. M. Polycarpou
Chapter Five: Adaptive Control of Systems with Actuator Failures,
by G. Tao and S. M. Joshi
Chapter Six: Multi-mode System Identification,
by E. I. Verriest
Chapter Seven: On Feedback Control of Processes with ``Hard''
Nonlinearities,
by B. Friedland
Chapter Eight: Adaptive Friction Compensation for Servo Mechanisms,
by J. Wang, S. S. Ge and T. H. Lee
Chapter Nine: Relaxed Controls and a Class of Active Material
Actuator Models,
by A. Kurdila
Chapter Ten: Robust Adaptive Control of Nonlinear Systems with Dynamic
Backlash-like Hysteresis,
by C.-Y. Su, M. Oya and X.-K. Chen
Chapter Eleven: Adaptive Control of a Class of Time-delay Systems
in the Presence of Saturation,
by A. M. Annaswamy, S. Evesque, S.-I. Niculescu and A. P. Dowling
Chapter Twelve: Adaptive Control for Systems with Input Constraints:
A Survey,
by J.-W. John Cheng and Y.-M. Wang
Chapter Thirteen: Robust Adaptive Control of Input Rate Constrained
Discrete Time Systems,
by G. Feng
Chapter Fourteen: Adaptive Control of Linear Systems with Poles in the
Closed Left Half Plane with Constrained Inputs, by D. A. Suarez-Cerda and R. Lozano
Chapter Fifteen: Adaptive Control with Input Saturation Constraints,
by C.-S. Zhang
Chapter Sixteen: Adaptive Control of Linear Systems with Unknown Time
Delay, by C.-Y. Wen, Y.-C. Soh and Y. Zhang
The authors of the chapters in this book are experts in their areas of
interest and their chapters present new solutions to important
issues in adaptive control of industrial systems with
nonsmooth nonlinearities such as backlash, dead-zone, failure, friction,
hysteresis, saturation, and time delay. These solutions result from recent
work in these areas and are believed to
be attractive to people from both academia and industry. Adaptive control of
nonsmooth dynamical systems is theoretically challenging and practically
important. This book is the first book on adaptive control of such
systems, addressing all these nonsmooth nonlinear characteristics: backlash,
dead-zone, failure, friction, hysteresis, saturation and time delays. Such a
book is also aimed at motivating more research activities in the important
field of adaptive control of nonsmooth nonlinear industrial systems.
Recent advances in adaptive control of nonsmooth dynamic systems
have shown that those practical nonsmooth nonlinear characteristics such as
backlash, dead-zone, component failure, friction, hysteresis, saturation and
time delays can be adaptively compensated when their parameters are
uncertain, as is common in real-life control systems. Rigorous designs have
been given for
selecting desirable controller structures to meet the control objectives and
for deriving suitable algorithms to tune the controller parameters for
control of systems with uncertainties in dynamics and nonsmooth
nonlinearities. There have been increasing interest and activities in these
areas of research, as evidenced by recent conference invited
sessions and journal special issues on related topics. It is clear that
this is a promising direction of research and there have been many
encouraging results. Given the practical importance and theoretical
significance of such research, it is time to summarize, unify, and develop
advanced techniques for adaptive control of nonsmooth dynamic systems.
Since this book is about some important and new areas of adaptive control
research, its contents are intended for people from both academia and
industry, including professors, researchers, graduate students who will use
this book for research and advanced study, and engineers who
are concerned with the fast and precision control of motion systems with
imperfections (such as backlash, dead-zone, component failure, friction,
hysteresis, saturation and time delays) in mechanical connections, hydraulic
servovalves, piezoelectric translators, and electric servomotors, and
biomedical actuators systems. The book can be useful for people from
aeronautical, biomedical, civil, chemical, electrical, industrial, mechanical
and systems engineering, who are working on aircraft flight control,
automobile control,
high performance robots,
materials growth process control,
precision motor control,
radar and weapons system pointing platforms,
VLSI assembly.
The adaptive system theory developed
in this book is also of interest to people who work on communication systems,
signal processing, real-time computer system modeling and control, biosystem
modeling and control.
The first editor would like to gratefully acknowledge the partial support
from National Science Foundation under grant ECS-9619363 and National
Aeronautics and Space Administration under grant NCC-1342 to this project. He
would also like to thank his graduate student Xidong Tang for his editorial
assistance on this project. The second editor acknowledges the vital support
of the Army Research Office under grant DAAD19-99-1-0137.
Gang Tao
Charlottesville, Virginia
Frank L. Lewis
Fort Worth, Texas
Avinash Taware and Gang Tao
The control problem: control of sandwich nonlinear dynamic systems is
addressed in this monograph. Of interest are sandwiched nonsmooth
nonlinearities such as dead-zone, hysteresis and backlash between dynamic blocks. Some
continuous-time control designs are proposed. A framework for hybrid control is
developed to design control schemes for different cases of the control problem with required modifications. Friction
compensation is addressed for systems with sandwiched friction along with sandwiched dynamics. The
problem of control of sandwich nonlinear systems with uncertain actuator failures is introduced, and
an adaptive control solution scheme is developed for this problem. An optimal and nonlinear control
solution is proposed for control of multi-body, multi-input and multi-output nonlinear systems with
joint backlash, flexibility and damping.
The proposed hybrid control framework employs an inner-loop discrete-time feedback design and an
outer-loop continuous-time feedback design, combined with a nonlinearity inverse to cancel the
nonlinearity effect, for improving output tracking. The first control design using this framework is
a nominal one with an exact nonlinearity inverse, which establishes a basic solution to the stated
control problem. The second design is an adaptive one which employs an adaptive inverse to cancel the
unknown sandwiched nonlinearity effect for improving output tracking. The third one is also an
adaptive one using the framework
with a neural network based inverse compensator. The adaptive inverse is updated from an adaptive
law. The neural network based nonlinearity
compensator consists of two neural networks, one used as an estimator of the sandwiched nonlinearity
function and the other for the compensation itself. The compensator neural network has neurons that
can approximate jump functions such as a dead-zone inverse. The weights of the two neural networks
are tuned using a modified gradient algorithm. For an adaptive inverse or neural network based
inverse, a control error equation is derived based on which a desirable tracking error equation is
obtained for an adaptive update or tuning law design. Stability and tracking performance of the
closed-loop control system are analyzed. Simulations are used to illustrate the effectiveness of the
proposed hybrid control designs.
Friction compensation is addressed for a benchmark sandwich system having sandwiched friction between
linear dynamic blocks as illustrated by a two-body system with load friction plus joint flexibility
and
damping. Several non-adaptive and adaptive compensation designs are analyzed, based on a Model
Reference Adaptive Control (MRAC) scheme that uses static state feedback for control and dynamic
output feedback for parameter adaptation to achieve output tracking. When applied to the benchmark
system, necessary and sufficient output matching conditions are derived for friction compensation.
Approximate linear parametrizations of nonlinear friction are developed for adaptive friction
compensator designs. The control problem for a sandwich nonlinear system with friction sandwiched in
between linear and
nonlinear dynamics is also addressed. Whenever load velocity is nonzero, adaptive linearizing control
is designed for such an unknown system with unknown friction. This linearizing control has a
contributing adaptive term that compensates for the estimated friction. In the case the load velocity
is zero, a maximum-magnitude controller is employed to overcome static friction effects. The proposed
adaptive friction compensation control schemes promise to bring considerable improvements to the
system performance.
Adaptive tracking control of sandwich nonlinear systems with actuator
failures is formulated and several suitable control designs are developed, including an adaptive
state feedback control scheme to achieve state tracking, and an adaptive output feedback controller
for output tracking for linear time-invariant plants with input actuator nonlinearities and failures.
Conditions and controller structures for achieving plant-model state or output matching in the
presence of actuator failures and nonlinearities are presented. Adaptive laws are designed for
updating the controller parameters when both the plant parameters, actuator nonlinearities and
actuator failure parameters are unknown. Adaptive inverse compensation is employed for the unknown
actuator nonlinearities. The effectiveness of the proposed adaptive designs is illustrated with
simulation results.
An optimal and nonlinear solution scheme is proposed for control of
multi-body, multi-input and multi-output nonlinear systems with joint
backlash, flexibility and damping, represented by a gun turret-barrel model which consists of two
subsystems: two motors driving two loads (turret and barrel) coupled by nonlinear dynamics. The key
feature of such systems is that the backlash is between two dynamic blocks. Optimal control schemes
are employed for backlash compensation and nonlinear feedback control laws are used for control of
nonlinear dynamics. When one load is in contact phase and the other load is in backlash phase, a
feedback linearization design decouples the multivariable nonlinear dynamics so that backlash
compensation
and tracking control can be both achieved. Nonlinear zero dynamics systems caused by joint damping
are bounded-input, bounded state
stable so that feedback linearization control designs ensure that all
closed-loop signals are bounded and asymptotic tracking is achievable.
We wish to gratefully acknowledge the valuable help rendered by institutions and individuals in our
conducting the research presented in this book.
This research was supported in part by the National Science Foundation under grant ECS-9619363, by
Techno Sciences Inc. under a US Army subcontract grant, and by NASA Langley Research Center under
grant NCC-1342. We would like to thank their financial support that made this research possible. We
are also thankful to University of Virginia for a pleasant and supportive environment to do our research.
We would like to express our gratitude to Professor Petar Kokotovic for his encouragement, help and
support to this research. We are grateful to Dr. Carole Teolis at Techno-Sciences Inc. for her
collaboration and help in conducting this research. We would like to thank Professors Petros Ioannou
and Frank Lewis for their interest and comments to this work. We would also like to thank Professors
Zongli Lin, Steve Wilson and Jim Aylor for their help to our research. We should mention that the
research results on adaptive actuator failure compensation by Shuhao Chen and Xidong Tang, with the
valuable help of Dr. Suresh Joshi of NASA Langley Research Center, contributed to the framework used
in Chapter 9 of this book for actuator failure compensation schemes for systems with actuator
nonlinearities. We
would like to recognize the contribution of Xiaoli Ma and Yi Ling to the work reported in Chapter 10
on control of nonlinear systems with joint backlash, flexibility and damping (for which Dr. Kenan
Ezal's work also inspired our results), and the contribution of Nilesh Pradhan to the proposed
friction compensation designs in Chapters 7 and 8. We would also like to express our appreciation for
the helpful comments from anonymous reviewers on this book and our related journal and conference
papers which laid down the foundation for this manuscript.
Finally, we would like to thank our families for their love and support
without which this project would have never been possibly completed.
Avinash Taware
Schenectady, New York
Gang Tao
Charlottesville, Virginia
Gang Tao
Errata:
errata.pdf
Adaptive control is becoming popular in many fields of
engineering and science as concepts of adaptive systems are becoming more
attractive in developing advanced applications. Adaptive control theory is a
mature branch of control theories, and there is a vast amount of literature
on design and analysis of various adaptive control systems using rigorous
methods based on different performance criteria. Adaptive
control faces many important challenges, especially in
nontraditional applications, such as
real-time systems, which do not have precise classical models admissible to
existing control designs, or a physiological system with an
artificial heart, whose unknown parameters may change
at a heart beat rate which is also a controlled variable.
To meet the fast growth of adaptive control applications and theory
development, a systematic and unified understanding of
adaptive control theory is thus needed.
In an effort to introduce such an adaptive control theory, this book presents
and analyzes some common and effective adaptive control design approaches,
including model reference adaptive control, adaptive pole placement control,
and adaptive backstepping control. The book addresses both continuous-time
and discrete-time adaptive control designs and their analysis; deals with
both single-input, single-output and multi-input, multi-output systems; and
employs both state feedback and output feedback. Design and
analysis of various adaptive control systems are presented in a systematic
and unified framework. The book is a collection of lectures on system
modeling and stability, adaptive control formulation and design, stability
and robustness analysis, and adaptive system illustration and comparison,
aimed at reflecting the state of the art in adaptive control as well as at
presenting its fundamentals. It is a comprehensive book which can be used as
either an academic textbook or technical reference for graduate students,
researchers, engineers, and interested undergraduate students in the
fields of engineering, computer science, applied mathematics and others,
who have prerequisites in linear systems and feedback control at the
undergraduate level.
In this self-contained book, basic concepts and fundamental principles of
adaptive control design and analysis are covered in 10 chapters. As
a graduate textbook, it is suitable for a one-semester course: lectures plus
reading may cover most of the book without missing essential material. To
help in understanding the topics, at the end of each chapter, there are
problems related to that chapter's materials
as well as technical discussions beyond the covered topics.
A separate manual containing solutions to most of these problems is also
available. At the end of most chapters, there are also some advanced topics
for further study in adaptive control.
Chapter 1 compares different areas of control theory, introduces some basic
concepts of adaptive control, and presents
some simple adaptive control systems, including direct and indirect adaptive
control systems in both continuous and discrete time, as well
as an adaptive backstepping control design for a nonlinear system in
continuous time.
Chapter 2 presents some fundamentals of dynamic system theory,
including system models, system characterizations, signal measures, system
stability theory (including Lyapunov stability and input--output
operator stability), signal convergence lemmas, and operator norms. In
particular, it gives a thorough study of the Lyapunov direct method for
stability analysis, some time-varying feedback operator stability properties,
several important inequalities for system analysis, some detailed
input--output L^p stability results, various analytical L^p signal
convergence results, some simplified analytical tools for discrete-time
system stability, and multivariable operator norms. These results, whose
proofs are given in detail and are easy to understand, clarify several
important signal and system properties for adaptive control.
Chapter 3 addresses adaptive parameter estimation for a general linear
model illustrated by a parametrized linear time-invariant system in either
continuous or discrete time. Detailed design and analysis of a normalized
gradient algorithm and a normalized least-squares algorithm in either
continuous or discrete time are given, including structure, stability,
robustness, and convergence of the algorithms. A collection of
commonly used robust adaptive laws are presented which ensure robust
stability of the adaptive schemes in the presence of modeling errors.
An L^{1+alpha} (alpha >= 1) theory is developed for adaptive
parameter estimation for a linear model, revealing some important inherent
robustness properties of adaptive parameter estimation algorithms.
Chapter 4 develops two types of state feedback adaptive control schemes:
for state tracking and for output tracking
(and its discrete-time version). For both continuous- and discrete-time
systems, adaptive state feedback for output tracking control, based on a
simple controller structure under standard model reference adaptive control
assumptions, is used as an introduction to adaptive control of general linear
systems. Adaptive disturbance rejection under different conditions is
addressed in detail; in particular, adaptive output rejection of unmatched
input disturbance is developed based on a derived property of linear
systems. Another development is a derived parametrization of state feedback
using a full- or reduced-order state observer, leading to the
commonly used parametrized controller structures with output feedback.
Chapter 5 deals with continuous-time model reference adaptive
control using output feedback for output tracking.
The key components of model reference adaptive control
theory---a priori plant knowledge, controller structure, plant--model
matching, adaptive laws, stability, robustness, and robust adaptation---are
addressed in a comprehensive formulation and, in particular,
stability and robustness analysis is given in a simplified framework.
The plant--model matching equation for a standard model reference
controller structure is studied in a tutorial formula. Design and
analysis of model reference adaptive control schemes are given for plants
with relative degree 1 or larger, using a Lyapunov or
gradient method based on a standard quadratic or
nonquadratic cost function. For the relative degree 1 case, an
L^{1+alpha} (0 < alpha < 1) adaptive control design is proposed
for reducing output tracking errors. An L^{1+alpha} (alpha > = 1)
theory is developed for adaptive control with inherent robustness
with respect to certain modeling errors. Robust
adaptive control is formulated and solved in a compact framework. Assumptions
on plant unmodeled dynamics are clarified, and robust adaptive laws are
analyzed. Closed-loop signal boundedness and mean tracking error properties
are proved. To develop adaptive control schemes
without using the sign of the high frequency gain of the controlled
plant, a modified controller parametrization leads to a framework of
adaptive control using a Nussbaum gain for stable parameter adaptation and
closed-loop stability and asymptotic output tracking.
Chapter 6 develops a model reference adaptive control theory for
discrete-time linear time-invariant plants. A unique plant--model matching
equation is derived, with unique controller parameters specified
to ensure exact output tracking after a finite number of steps. A stable
adaptive control scheme is designed and analyzed which ensures closed-loop
signal boundedness and asymptotic output tracking. It is shown that the model
reference adaptive control system is robust with respect to L^2 modeling
errors and with modification is also robust with respect to L^{1+alpha}
(alpha > 1) modeling errors. Thus an L^{1 + alpha} (alpha > =
1) robustness theory is developed for discrete-time adaptive
control. Robust adaptive laws are derived for discrete-time adaptive control
in the presence of bounded disturbances.
Chapter 7 presents two typical designs (and their analysis) of indirect adaptive
control schemes: indirect model reference adaptive control and
indirect adaptive pole placement control in both continuous and
discrete time. Examples are used to illustrate the design procedures and
analysis methods. For indirect model reference adaptive control in
continuous or discrete time, a concise closed-loop error model is
derived based on which the proof of signal boundedness and asymptotic output
tracking is formed in a feedback and small-gain setting similar to
that for the direct model reference adaptive control scheme of Chapters 5 and
6. For indirect adaptive pole placement control, a singularity problem is
addressed, and closed-loop stability and output tracking are analyzed in a
unified framework for both continuous and discrete time.
As a comparison, a direct adaptive pole placement control scheme
is presented and discussed for its potential
to avoid the singularity problem.
Chapter 8 conducts a comparison study of several adaptive control
schemes applied to a benchmark two-body system with joint flexibility and
damping, including direct state feedback, direct output
feedback, indirect output feedback, direct--indirect state feedback, and
backstepping state feedback designs, with detailed design and analysis for
the last two designs. With different complexity, they all
ensure closed-loop signal boundedness and asymptotic output tracking.
The design and analysis of the direct--indirect adaptive control
scheme demonstrate some typical time-varying operations on signals in
time-varying systems.
Chapter 9 first gives the design and analysis of adaptive state feedback state
tracking control for multi-input systems. A multivariable state
feedback adaptive control scheme is derived using LDU decomposition of a
plant gain matrix. Multivariable adaptive control is applied to system
identification. This chapter then develops a unified theory for robust model
reference adaptive control of linear time-invariant multi-input, multi-output
systems in both continuous and discrete time.
Key issues such as a priori plant knowledge, plant and
controller parametrizations, design of adaptive laws, stability, robustness,
and performance are clarified and solved. In particular, an error model for a
coupled tracking error equation is derived, a robust adaptive law for
unmodeled dynamics is designed, a complete stability and robustness
analysis for a general multivariable case is given, and a unified
multivariable adaptive control theory is established in a form applicable in
both continuous and discrete time. The chapter presents some recent
results in reducing a priori plant knowledge for multivariable model
reference adaptive control using LDU parametrizations of the high frequency
gain matrix of the controlled plant. Model reference adaptive control designs
for multivariable systems with input or output time delays are also derived.
Different adaptive control schemes, including a variable structure
design, a backstepping design, and a pole placement control design for
multivariable systems, are presented. Finally, robust adaptive control theory
is applied to adaptive control of robot manipulator systems in the presence
of parameter variations and unmodeled dynamics.
Chapter 10 presents a general adaptive inverse approach for control of plants
with uncertain nonsmooth actuator nonlinearities such as dead-zone, backlash,
hysteresis, and other piecewise-linear characteristics which are common in
control systems and often limit system performance. An adaptive inverse is
employed for cancelling the effect of an actuator nonlinearity with unknown
parameters, and a linear or nonlinear feedback control law is used for
controlling a linear or smooth nonlinear dynamics following the actuator
nonlinearity. This chapter gives an overview of various state
feedback and output feedback control designs for linear, nonlinear,
single-input and single-output, and multi-input and multi-output plants
as well as open problems in this area of major theoretical and practical
relevance. A key problem is to develop linearly parametrized error models
suitable for developing adaptive laws to update the inverse and feedback
controller parameters, which is solved for various considered cases. The
chapter shows that control systems with commonly used linear or nonlinear
feedback controllers such as a model reference, PID, pole placement, feedback
linearization, or backstepping can be combined with an adaptive inverse to
handle actuator nonlinearities.
The book is focused on adaptive control of deterministic systems with
uncertain parameters, dynamics and disturbances. It can also be
useful for understanding the adaptive control algorithms for stochastic
systems (see references for ``Stochastic Systems'' in Section
1.4 for such algorithms). The material presented has been used and refined in
a graduate course on adaptive control which I have taught for the past ten
years at the University of Virginia to engineering, computer science, and
applied mathematics students. Comments and modifications to the book can be
found at
http://www.people.virginia.edu/~gt9s/wiley-book.
If used as a reference, this book can be followed in its chapter sequence
for both continuous- and discrete-time adaptive control system design and
analysis. The discrete-time contents are mainly in
Sections 1.5.3 (adaptive control system examples), 2.7 and 2.8
(systems and signals), 3.6 (adaptive parameter estimation), 3.7.2 (robustness
of parameter estimation), 3.8.2 (robust parameter estimation), 4.5 (state
feedback adaptive control), Chapter 6 (model reference adaptive control),
Sections 7.3 (indirect model reference adaptive control and adaptive pole
placement control), 9.2 (multivariable model reference adaptive control), and
10.2--10.5 (adaptive actuator nonlinearity inverse control) (both in
a unified continuous- and discrete-time framework). The rest of the book is
for continuous-time adaptive control design and analysis.
If used as a textbook for students with knowledge of linear
control systems, as a suggestion based on experience
at the graduate level, the instruction may start with Sections 1.4 and
1.5 as an introduction to adaptive control
(one or two lectures, 75 minutes each). Some basic knowledge of systems,
signals, and stability may be taken from Sections 2.1--2.6 (system modeling,
signal norms, Lyapunov stability, Gronwall-Bellman lemma, small-gain lemma,
strictly positive realness and Lefschetz-Kalman-Yakubovich lemma, signal
convergence lemmas including Lemmas 2.14, 2.15, and 2.16
(Barbalat lemma) for four or five lectures). Adaptive parameter
estimation can be taught using
Sections 3.1--3.6 in four or five lectures, including some reading
assignments of robustness results from Sections 3.7 and 3.8. The design and
analysis of adaptive control
schemes with state feedback are presented in Sections 4.1--4.4
(three lectures), while the discrete-time results in Section 4.5
can be used as reading materials. Continuous-time model reference adaptive
control in Chapter 5 can be covered in
seven or eight lectures (Sections 5.1--5.5, with Section 5.6 as a reading
assignment). Indirect adaptive control in Chapter 7 may need
four lectures. One lecture plus reading is recommended for Chapter
8. Chapters 9 and
10 are for advanced study as either extended reading or project assignments.
Further reading can be selected from the
included extensive list of references on adaptive systems and control.
In this book, for a unified presentation of continuous- and discrete-time
adaptive control designs in either the time or frequency domain,
the notation y(t) = G(D)[u](t) (or y(D) = G(D)u(D)) represents, as the
case may be, the time-domain output at time t (or frequency-domain output)
of a dynamic system characterized by a dynamic operator (or transfer
function) G(D) with input u(tau), tau < = t (or u(D)), where the
symbol D is used, in the continuous-time case, as the Laplace transform
variable or the time differentiation operator D[x](t) = dot{x}(t), t
in [0, infty), or, in the discrete-time case, as the z-transform variable
or the time advance operator D[x](t) = x(t + 1), t in {0, 1, 2, 3,
...}, with x(t) == x(tT) for a sampling period
T > 0.
Adaptive control as knowledge has no limit and as theory is
rigorous. Adaptive control is a field of science.
The universe is mysterious,
diverse, and vigorous. The world is complicated, uncertain, and unstable.
Adaptive control deals with complexity, uncertainty, and
instability of dynamic systems. Taoist philosophy emphasizes
simplicity, balance, and harmony of the universe. A goal of this book
is to give a simplified, balanced, and harmonious presentation of
the fundamentals of adaptive control theory, aimed at improving the
understanding of adaptive control, which, like other control methodologies,
brings more simplicity, balance, and harmony to the dynamic world.
This book has benefited from many people's help. First, I am
especially grateful to Professors Petros Ioannou and Petar Kokotovic.
I was introduced to the field of
adaptive control by Professor Ioannou, and
his continuous support and vigorous instruction were most helpful to my
study and research
in adaptive control. Professor Kokotovic has been a great
mentor, and his persistent enthusiasm and continual
encouragement have been most valuable to me in the writing of this book.
Their robust adaptive control theory
has been most influential to my research in adaptive control.
I would like to
particularly
acknowledge Professors
Karl Astrom,
Graham Goodwin,
Bob Narendra, and
Shankar Sastry
for their work on adaptive control, which inspired
me in research and in writing this book.
I would like
to thank
Professors Brian Anderson,
Anu Annaswamy,
Er-Wei Bai,
Bob Bitmead,
Stephen Boyd,
Marc Bodson,
Carlos Canudas de Wit,
Han-Fu Chen,
Aniruddha Datta,
Michael Demetriou,
Manuel De la Sen,
Gang Feng,
Li-Chen Fu,
Sam Shu-Zhi Ge,
Lei Guo,
Lui Hsu,
Alberto Isidori,
Zhong-Ping Jiang,
Dr. Ioannis Kanellakopoulos,
Professor Hassan Khalil,
Dr. Bob Kosut,
Professors Gerhard Kreisselmeier,
P. R. Kumar,
Yoan Landau,
Frank Lewis,
Wei Lin,
Lennart Ljung,
Rogelio Lozano,
Iven Mareels,
David Mayne,
Rick Middleton,
Steve Morse,
Romeo Ortega,
Marios Polycapou,
Laurent Praly,
Drs. Darrel Recker,
Doug Rhode,
Professors Gary Rosen,
Jack Rugh,
Ali Saberi,
Mark Spong,
Yu Tang,
T. J. Tarn,
David Taylor,
Chang-Yun Wen,
John Ting-Yung Wen, and
Erik Ydstie,
whose knowledge of adaptive systems and controls helped my
understanding of the field.
I especially thank
Professors Murat Arcak,
Ramon Costa,
Dr. Suresh Joshi,
Professor Miroslav Krstic,
Dr. Jing Sun, and
Professor Kostas Tsakalis
for their knowledge and comments, which helped me in writing this book.
I am thankful to my graduate students
Michael Baloh,
Lori Brown,
Jason Burkholder,
Shu-Hao Chen,
Tinya Coles,
Warren Dennis,
Emin Faruk Kececi,
Yi Ling,
Xiao-Li Ma,
Raul Torres Muniz,
Nilesh Pradhan,
Gray Roberson,
Min-Yan Shi,
Xi-Dong Tang,
Avinash Taware,
Ming Tian,
Timothy Waters, and
Xue-Rui Zhang, and to computer scientists Chen-Yang Lu and Ying Lu,
and engineer
Yi Wu, for their earnest study, stimulating discussion, and
interesting applications of adaptive control.
I would also like to express my thanks to my colleagues at the University of
Virginia for their support, in particular, to
Professors Milton Adams,
Paul Allaire,
Jim Aylor,
Zong-Li Lin,
Jack Stankovic,
Steve Wilson, and
Houston Wood,
for their
collaboration and help in my teaching and research.
Finally, I gratefully acknowledge that my study and research on adaptive
control, which led to many of the results in this book, were supported
by grants from the U.S. National Science Foundation and by a scholarship
from the Chinese Academy of Sciences.
Gang Tao
Charlottesville, Virginia
Gang Tao, Shuhao Chen, Xidong Tang, Suresh M. Joshi
Actuator failures in control systems may cause severe system performance
deterioration and even lead to catastrophic closed-loop system instability.
For example, many aircraft accidents were caused by operational failures in
the control surfaces, such as rudder and elevator.
For system safety and reliability, such actuator failures must be
appropriately accommodated. Actuator failure compensation is an important and
challenging problem for control systems research with both theoretical and
practical significance.
Despite substantial progress in the area of actuator failure compensation,
there are still many important open problems, in particular those involving
system uncertainties. The main difficulty is that the actuator failures are
uncertain in nature. Very often it is impossible to predict in advance
which actuators may fail during system operation, when the
actuator failures occur, what type and what values of the
actuator failures are. It may also be
impractical to determine such actuator failure parameters after a failure
occurs. It is appealing to develop control schemes that can accommodate
actuator failures without explicit knowledge of the occurrences of actuator
failures and the actuator failure values. Adaptive control, which is capable
of accommodating system parametric, structural, and environmental
uncertainties, is a suitable choice for such actuator failure compensation
schemes.
This book presents our recent research results in designing and analyzing
adaptive control schemes for systems with unknown actuator failures and
unknown parameters. The main feature of the adaptive actuator failure
compensation approach developed in this book is that no explicit fault
detection and diagnosis procedure is used for failure compensation.
An adaptive law automatically adjusts the controller parameters based on
system response errors, so that the remaining functional actuators can
be used to accommodate the actuator failures and systems parameter
uncertainties.
The book is in a
comprehensive and self-contained presentation, while the developed theory is
in a general framework readily applicable to specific practical
adaptive actuator failure compensation problems. The book can be used as a
technical reference for graduate students, researchers, and engineers from
fields of engineering, computer science, applied mathematics, and others who
have a background in linear systems and feedback control at the
undergraduate level. It can also be studied by interested undergraduate
students for their thesis projects.
This book is focused on adaptive compensation of actuator failures
characterized by the failure model that some unknown control inputs may get
stuck at some unknown fixed (or varying) values at unknown time instants and
cannot be influenced by the control signals. The type of fixed-value actuator
failures, referred to as ``lock-in-place'' actuator failures, is an important
type of actuator failures and is often encountered in many critical control
systems. For example, in aircraft flight control systems, the control
surfaces may be locked in some fixed places and hence lead to catastrophic
accidents. Varying value failures can occur, for example, due to hydraulics
failures that can produce unintended movements in the control surfaces of an
aircraft.
For actuator failure compensation, a certain redundancy of actuators is
needed. For a system with multiple actuators, one case is that all
actuators have the same physical characteristics; for example, they are
segments of a multiple-segment rudder or elevator for an aircraft. For this
case, a reasonable (natural) design for the applied control inputs
is one with equal or proportional actuation for each actuator, that is, all
control inputs are designed to be equal or proportional to each other. This
actuation scheme is employed throughout the book, except for Chapter 5, where
a multivariable design is used for the case when the actuators are
divided into several groups and each group has actuators of the same physical
characteristics (for example, an aircraft has a group of four engines and
a group of three rudder segments), and within each group, an equal or proportional
actuation is used.
With 12 chapters, the book systematically develops
adaptive state tracking and output tracking control schemes for systems with
parameter and actuator failure uncertainties. Designs and analysis for both
linear systems and nonlinear systems with unknown actuator failures are
covered. Key issues for adaptive actuator failure compensation, namely,
design condition, controller structure, error equations, adaptive laws for
updating the controller parameters, analysis of stability and tracking
properties, are given in detail. Extensive simulation
results are presented to verify the desired closed-loop system performance.
This work is aimed at developing a theoretical framework for adaptive control
of systems with actuator failures, to provide guidelines for designing
control systems with guaranteed stability and tracking performance in the
presence of system parameter uncertainties and failure uncertainties.
Chapter 1 presents some background material. Basic concepts and fundamental
principles of adaptive control systems are introduced. The actuator failure
compensation problems for linear systems and nonlinear systems are
formulated.
An overview of several existing actuator failure compensation design methods,
including multiple models, switching and tuning designs, fault diagnosis
designs, adaptive designs, and robust designs, is also given.
Chapters 2--8 address the adaptive actuator failure compensation problems for
linear time-invariant systems with unknown actuator failures. Chapter 2
presents several model reference state feedback state tracking designs. For a
linear time-invariant system with m actuators, the adaptive actuator failure
compensation problem for up to m - 1 unknown actuator failures is
investigated.
Designs for three types of actuator failures: ``lock-in-place,''
parametrizable
time-varying, and unparametrizable time-varying, are developed. Conditions
and controller structures for achieving plant-model state matching, adaptive
laws for updating the controller parameters, and analysis of closed-loop
stability and asymptotic state tracking properties are addressed in a
unified and comprehensive framework. State feedback actuator failure
compensation designs for a class of multi-input systems are also derived.
A more general case of up to m - q (q > = 1) unknown actuator failures is
then addressed. Necessary and sufficient conditions for actuator failure
compensation are derived. It is shown that the number of fully functional
actuators is crucial in determining the actuation range that specifies the
compensation design conditions in terms of system actuation structures.
Such conditions are required for both a nominal design using system and
failure knowledge and an adaptive design without such knowledge. An adaptive
actuator failure compensation control scheme based on such system actuation
conditions is developed for systems with unknown dynamics parameters and
unknown ``lock-in-place'' actuator failures. Simulation results are presented
to verify the desired system performance with failure compensation.
Chapter 3 investigates the state feedback output tracking problem for
single-output linear time-invariant systems with any up to m - 1 uncertain
failures of the total m actuators. In particular, adaptive rejection of
the effect of certain unmatched input disturbances on the output of a linear
time-invariant system is addressed in detail. A lemma that presents a novel
basic property of linear time-invariant systems is derived to characterize
system conditions for plant-model output matching. An adaptive disturbance
rejection control scheme is developed for such systems with uncertain
dynamics parameters and disturbances. This adaptive control technique is
applicable to control of systems with actuator failures whose failure values,
failure time instants, and failure patterns are unknown. A solution
capable of accommodating the ``lock-in-place'' and time-varying actuator
failures in the presence of any up to m - 1 uncertain failures of the total
m actuators is presented to this adaptive actuator failure compensation
problem. The developed
adaptive actuator failure compensation schemes ensure closed-loop
stability and asymptotic output tracking despite the uncertainties in
actuator failures and system parameters.
Simulation results verify the desired
system performance in the presence of unknown actuator failures.
Chapter 4 develops a model reference adaptive control scheme using output
feedback for output tracking for linear time-invariant systems with unknown
actuator failures. An effective output feedback controller structure is
proposed for actuator failure compensation. When implemented with true
matching parameters, the controller achieves desired plant-model output
matching, and when implemented with adaptive parameter estimates, the
controller achieves closed-loop stability and asymptotic output tracking,
which is also verified by simulation results.
Compensation of varying failures is achieved based on an output
matching condition for a system with multiple inputs whose actuation vectors
may be linearly independent.
Chapter 5 deals with the output tracking problem for multi-output linear
time-invariant systems using output feedback. Two adaptive control schemes
based on model reference adaptive control are developed for a class of
multi-input multi-output systems with unknown actuator failures. An effective
controller structure is proposed to achieve the desired plant-model output
matching when implemented with matching parameters. Based on design
conditions on the controlled plant, which are also needed for nominal
plant-model output matching for a chosen controller structure, two adaptive
controllers are proposed and stable adaptive laws are derived for updating
the controller parameters when system and failure parameters are unknown.
All closed-loop signals are bounded and the system outputs track some given
reference outputs asymptotically, despite the uncertainties in failures and
system parameters. Simulation results are presented to demonstrate the
performance of the adaptive control system in the presence of unknown rudder
and aileron failures in an aircraft lateral dynamic model.
Chapter 6 studies adaptive pole placement control for linear time-invariant
systems with unknown actuator failures, applicable to both minimum and
nonminimum phase systems. A detailed analysis shows the existence of a
nominal controller (when both system and actuator failure parameters are
known) that achieves the desired pole placement, output tracking, and
closed-loop signal boundedness. For that case when both system and failure
parameters are unknown, an adaptive control scheme is developed. A
simulation study with a linearized lateral dynamic model of the DC-8 aircraft
is presented to verify the desired actuator failure compensation performance.
Chapter 7 applies several adaptive control schemes developed in the previous
chapters to a linearized longitudinal dynamic model of a transport aircraft
model. The tested adaptive schemes include state feedback design for state
tracking, state feedback design for output tracking, and output feedback
design for output tracking. Various actuator failures are considered.
Extensive simulation results for different cases are presented to demonstrate
the effectiveness of the adaptive actuator failure compensation designs.
Chapter 8 presents a robust adaptive control approach using output feedback
for output tracking for discrete-time linear time-invariant
systems with uncertain failures of redundant actuators in the presence of the
unmodeled dynamics and bounded output disturbance.
Technical issues such as plant-model output matching, adaptive
controller structure, adaptive parameter update laws, stability and tracking
analysis, and robustness of system performance are solved for the
discrete-time adaptive actuator failure compensation problem. A case study
is conducted for adaptive compensation of rudder servomechanism failures of
a discrete-time Boeing 747 dynamic model, verifying the desired
adaptive system performance.
Chapters 9--11 deal with actuator failure compensation problems for nonlinear
systems. Chapter 9 formulates such problems and
develops adaptive control schemes for feedback
linearizable systems. Different structure conditions that characterize
different classes of systems amenable to actuator failure compensation are
specified, with which adaptive state feedback control
schemes are developed for systems with uncertain actuator failures.
Chapter 10 addresses actuator failure compensation problems for nonlinear
systems that can be transformed into parametric-strict-feedback form with
zero dynamics. Two main cases are studied for adaptive actuator failure
compensation: systems with stable zero dynamics, and systems with extra
controls for stabilization. Design conditions on systems admissible for
actuator failure compensation are clarified. Adaptive state feedback
control schemes are developed, which ensure asymptotic output tracking and
closed-loop signal boundedness despite the uncertainties in actuator failures
as well as in system parameters. An adaptive control scheme is applied to a
twin otter aircraft longitudinal nonlinear dynamics model in the presence of
unknown failures in a two-segment elevator servomechanism. Simulation results
verify the desired adaptive actuator failure compensation performance.
Chapter 11 presents an adaptive control scheme that achieves stability and
output tracking for output-feedback nonlinear systems with unknown actuator
failures. A state observer is designed for estimating the unavailable system
states, based on a chosen control strategy, in the presence of actuator
failures with unknown failure values, time instants, and pattern. An adaptive
controller is developed by employing a backstepping technique, for which
parameter update laws are derived to ensure asymptotic output tracking and
closed-loop signal boundedness, as shown by detailed stability analysis.
An extension of the developed adaptive actuator failure
compensation scheme to nonlinear systems whose
dynamics are state-dependent is also given to accommodate a larger
class of nonlinear systems. An application to controlling the angle of
attack of a nonlinear aircraft model in the presence of elevator segment
failures is studied,
with simulation results presented to illustrate the effectiveness
of the failure compensation design.
Chapter 12 presents concluding remarks and suggests a list of theoretical
and practical topics for further research in this area of adaptive control.
To help the readers understand the basic designs of adaptive control in
the absence of actuator failures, the book includes an appendix that
presents the schemes of model reference adaptive control using state feedback
for state tracking, state feedback for output tracking, output feedback
for output tracking, and multivariable design, as well as adaptive
pole placement control. Key issues such as a priori system knowledge,
controller structure, plant-model matching, adaptive laws, and stability
are addressed.
This book describes adaptive actuator failure compensation approaches for
effectively controlling uncertain dynamic systems with uncertain actuator
failures. It addresses the theoretical issues of actuator
failure models, controller structures,
design conditions, adaptive laws, and stability analysis, with extensive
simulation results on various aircraft system models. Design
guidelines provided here
may be used to develop advanced adaptive control techniques for
control systems with controller adaptation and failure
compensation capacities to improve reliability, maintainability, and
survivability. The research leading to this book was supported by
the National Aeronautics and Space Administration (NASA). However, the views
and contents of this book are solely those of the authors and not of NASA.
Gang Tao and Jing Sun (editors)
Control systems theory, as an interdisciplinary science that deals
with basic principles underlying the analysis and synthesis of
interconnected systems, has had an enormous impact on the
development of basic physical science, social economy, and
advanced technology. Over the last 50 years, the advancement in
control theory and its applications have played a crucial and
prominent role to enable engineering activities in improving
social infrastructure, life quality, and environment. Advanced
theory for feedback control and other control mechanisms provides
foundation and new insights to other branches of physical sciences
such as communication, biomedical, and micro-nano systems. New control
design tools have helped to streamline the system design and
integration tasks for many industries, such as the process and
automotive industry, thereby leading to more effective and robust
products and processes. Widespread applications of
micro-processors, distributed actuators and sensors, and real-time
computing have further extended the domains of control application
and made feedback even more ubiquitous, covering macro systems
such as aircrafts, automobiles as well as micro entities like
biology cells and nano-devices.
While it is evident that control theory has enabled many
technological breakthroughs in aerospace, automotive, biomedical
and other fields, it is equally convincing that new developments
emerged in other fields have offered new challenges and
opportunities for control engineers and researchers. It is this
healthy cross-fertilization between the control theory and its
application domains that has propelled the immense progresses of
the control systems theory and led to the vast amount of
scientific and technical publications in the literature. The field
is developing and expanding rapidly with the stimulation of
emerging challenges and the encouragement of the promising
solutions.
This book presents a collection of diverse topics on some recent
advances in control systems theory and applications, contributed
by the authors who have enthusiastically and persistently worked
in this exciting field. Moreover, most of the authors are alumni
of the University of Science and Technology of China (USTC), who
studied in their Alma Mater during different time periods of her
glorious 50 years. The publication of this book is also intended
to be a celebratory event for the 50th anniversary of the founding
of USTC, a commemoratory testimony to those authors' Alma Mater for her
dedication and contributions to education and research.
The book consists of 15 chapters whose topics range from different
areas of control systems theory to various control applications:
from adaptive control, control of bifurcations, digital control, fault
tolerance control, H_infty control, learning control, neural
and fuzzy control, nonlinear control, optimization, parameter
estimation, predictive control, robust control, stochastic
control, system identification, variable structure control, to
aircraft flight control, building vibration control, computer
control systems, medical robots, portfolio management, robot
formation and control, and smart structures.
The 15 chapters, with their titles and
authors (and their USTC class numbers), are summarized as follows.
Chapter 1:
A Sensitivity-Based View to the Stochastic Learning and
Optimization,
by Xi-Ren Cao (6204), Fang Cao (9862)
Chapter 2:
Brief Review of Research on Robust Pole Clustering and Robust Structural Control,
by Sheng-Guo Wang (6206)
Chapter 3:
Two Challenging Problems in Control Theory,
by Minyue Fu (7765)
Chapter 4:
Developments in Receding Horizon Optimization-based Controls: Towards
Real-time Implementation for Nonlinear Systems with Fast Dynamics,
by Jing Sun (7765), Reza Ghaemi, Ilya Kolmanovsky
Chapter 5:
Multivariable Model Reference Adaptive Control,
by Gang Tao (7765)
Chapter 6:
On Computer-Controlled Variable Structure Control Systems,
by Bing Wang, Xinghuo Yu (7765), Xiangjun Li, Changhong Wang
Chapter 7:
Multi-Robot Formation Control Based on Feedback from Onboard Sensors,
by Tove Gustavi, Maja Karasalo, Xiaoming Hu (7865)
Chapter 8:
Semiactive Control Strategies for Vibration Reduction in Smart Structures,
by Ningsu Luo (7865)
Chapter 9:
Identification and Control of Nonlinear Dynamic Systems via a
Constrained Input-Output Neurofuzzy Network,
by Marcos Gonzalez-Olvera, Yu Tang (7868)
Chapter 10:
Decomposition-Based Robot Control,
by Guangjun Liu (7965)
Chapter 11:
From Adaptive Observers to Decoupled State and Parameter Estimations,
by Qinghua Zhang (8110)
Chapter 12:
Reduced-Order Controllers for the H_infty Control Problem with
Unstable Invariant Zeros or Infinite Zeros,
by Xin Xin (8210)
Chapter 13:
Recent Advances in Bifurcation Control,
by Hua O. Wang (8364)
Chapter 14:
Intelligent Medical Robot Application: Tele-Neurosurgical Robot Case Study,
by Weimin Shen, Jianjun (Jason) Gu (8700), Yanjun Shen
Chapter 15:
Applications of Stochastic Control Theory in Portfolio Management,
by Tao Pang (9001).
On the behalf of the USTC alumni authors of this book, we would
like to express our heartfelt gratitude to the teachers of our
Alma Mater, who, with their enthusiasm and dedication, led us to
this fascinating field and taught us the knowledge and skills that
allowed us to explore the subject in various directions presented
in this book. Our experience at our Alma Mater had been life
enriching, and it shaped our personal and professional life in
numerous ways. This book is specially edited and dedicated to our
Alma Mater at her 50th anniversary in the special year of 2008. We
would also like to express our appreciation to the contributions
of other authors to this book, for joining this effort and making
this special edition possible.
In addition, all the authors of this book would like to thank our
colleagues for their intellectual stimulation and collaboration in
our research, our students for their diligent and conscientious
effort and for being our continuous inspiration, and our
universities and our research sponsors for their support to our
professional duties and research activities.
Gang Tao and Jing Sun (USTC Class 7765)