Frontiers in Adaptive Control Frontiers in Adaptive Control Edited by Shuang Cong In-Tech intechweb.org Published by In-Tech In-Tech Kirchengasse 43/3, A-1070 Vienna, Austria Hosti 80b, 51000 Rijeka, Croatia Abstracting and non-profit use of the material is permitted with credit to the source. Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published articles. Publisher assumes no responsibility liability for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained inside. After this work has been published by the In-Teh, authors have the right to republish it, in whole or part, in any publication of which they are an author or editor, and the make other personal use of the work. © 2009 In-tech www.intechweb.org Additional copies can be obtained from: publication@intechweb.org First published January 2009 Printed in Croatia Frontiers in Adaptive Control, Edited by Shuang Cong p. cm. ISBN 978-953-7619-43-5 1. Adaptive Control I. Shuang Cong V Preface Starting in the early 1950s, the design of autopilots for high performance aircraft moti- vated an intense research activity in adaptive control. Today, adaptive control theory has grown to be a rigorous and mature discipline. Because it is good at dealing with uncertain quantities in dynamic systems in which exist unknown parameters and disturbances, adap- tive control has become more popular in many fields of engineering and science in terms of algorithms, design techniques, analytical tools, and modifications. Nowadays, the presence of robotics in human-oriented applications demands control paradigms to face partly known, unstructured, and time-varying environments. Variety disturbances including ap- plied external forces, higher order dynamics, nonlinearities and noise are always present in complex control systems such as robot manipulators and the human-machine ensemble. Adaptive control is required to be applied into new fields and more complex situations. The objective of this book is to provide an up-to-date and state-of-the-art coverage of di- verse aspects related to adaptive control theory, methodologies and applications. These in- clude various robust techniques, performance enhancement techniques, techniques with less a-priori knowledge, nonlinear adaptive control techniques and intelligent adaptive tech- niques. There are several themes in this book which instance both the maturity and the novelty of the general adaptive control. The book consists of 17 Chapters. Each chapter is introduced by a brief preamble providing the background and objectives of subject matter. The experiment results are presented in considerable detail in order to facilitate the compre- hension of the theoretical development, as well as to increase sensitivity of applications in practical problems. The outline of each chapter is as follows: In Chapter 1, an adaptive control for a free-floating space robot is proposed by using the inverted chain approach, which is a unique formulation for a space robot compared with that for a ground-based manipulator system. Chapter 2 deals with introducing how to ob- tain models linear in parameters for real systems and then using observations from the sys- tem to estimate the parameters or to fit the models to the systems with a practical view. A new procedure for model validation in the frequency domain is presented in Chapter 3. This procedure permits to validate or invalidate models over certain frequency ranges. The pro- cedure is the translation of a time domain residual whiteness test to a frequency dependent residual whiteness test. The counterpart on the frequency domain of a time domain white- ness test is established. In the methodologies, substantial progress of the Kalman filtering design for nonlinear stochastic systems made in the past decade offers promise for solving some long-standing control problems, which is considered in Chapter 4. In Chapter 5, a backstepping-like pro- cedure incorporating the model reference adaptive control (MRAC) is employed to circum- vent the difficulty introduced by its cascade structure and various uncertainties. A Lyapunov-like analysis is used to justify the closed-loop stability and boundedness of inter- nal signals. In Chapter 6, a novel Takagi-Sugeno(TS) Feedforward fuzzy approxima- tor(FFA)-based adaptive control scheme is proposed and applied to motion/force tracking VI control of holonomic systems. By integrating the feed-forward fuzzy compensation and er- ror-feedback concepts, the proposed FFA-based control concept avoids heavy computation load and achieves global control results. In Chapter 7, two sliding mode adaptive control strategies have been proposed for single-input single-output(SISO) and single-input multi- ple-output(SIMO) systems with unknown bound time-varying uncertainty respectively. Chapter 8 introduces the Active Observer (AOB) algorithm for robotic manipulation. The AOB reformulates the classical Kalman filter (CKF) to accomplish MRAC. The AOB pro- vides a methodology to achieve model-reference adaptive control through extra states and stochastic design in the framework of Kalman filters. In Chapter 9, the human-machine en- semble is regarded as an adaptive controller where both the environment and human cogni- tion vary, the latter due to environmental and situational demands. Chapter 10 presents a parameter estimation routine that allows exact reconstruction of the unknown parameters in finite-time provided a given excitation condition is satisfied. The robustness of the routine to an unknown bounded disturbance or modeling error is also shown. In Chapter 11, a general scheme to construct adaptive policies in control models is to combine statistical estimation methods of the unknown distribution with control procedures. Such policies have opti- mality properties provided that the estimators are consistent in an appropriate sense. Chap- ter 12 develops a new adaptive control framework which applies to any nonlinearly param- eterized system satisfying a general Lipschitzian property. This allows one to extend the scope of adaptive control to handle very general control problems of nonlinear parameteri- zation since Lipschitzian parameterizations include as special cases convex/concave and smooth parameterizations. Chapter 13 presents a simple and straightforward adaptive con- troller strategy from the class of direct methods, based on reference models. The algorithm offers an alternative solution to the burden of process identification, and will present possi- bilities to tune both integer-and fractional-order controllers. In the applications, Chapter 14 considers yaw dynamics of a vehicle operating under un- certain road conditions with unknown velocity and mass. Authors develop an adaptive con- trol design technique motivated by the demand for a system capable of adjusting to devi- ations in vehicle parameters with almost negligible performance compromises. Chapter 15 proposes an indirect multiple -input multiple-output (MIMO) MRACS with structural esti- mation of the interactor. By using indirect method, unreasonable assumptions such as as- suming the diagonal degrees of interactor can be avoided. Since the controller parameters are calculated based on the observability canonical realization of the estimated values, the proposed method is suitable for on-line calculations. Chapter 16 discourses on adaptive con- trol for wireless local area networks introducing the Priority Oriented Adaptive Control with QoS Guarantee (POAC-QG) protocol for WLANs. It can be adapted into the Hybrid Control Function (HCF) protocol of the IEEE 802.11e standard in place of Hybrid Control Channel Access (HCCA). A Time Division Multiple Access (TDMA) scheme is adopted for the access mechanism. POAC-QG is designed to efficiently support all types of real-time traffic. Chapter 17 surveyed various topics in Very Large Scale Integrated (VLSI) technology in adaptive control perspective: The design margins in process and circuit level are con- sidered to be headroom for power savings, and adaptive control schemes are used to figure out the margins automatically and to make adjustment without harming the system oper- ation. An adaptive control is also used to optimize the circuit operation for time-varying cir- cumstances. This type of scheme enables the chip to operate always in optimal condition for wide range of operation conditions. I believe the new algorithms and adaptive control strategies presented in this book are very effective approaches to solve the problems in unknown parameter estimation, model- VII ing, analysis, adaptive controller design and some important research challenge. The book is also intended to be served as a reference for the researcher as well as the practitioner who wants to solve the problems caused by the uncertainty in the controlled systems. I hope that the reader will share my excitement to present this book on frontiers in adaptive control and will find it useful. Finally, I would like to thanks all the authors of each Chapter for their contribution to make this book possible. My special thanks go to the publisher, In-Tech, for publishing this book. Shuang Cong University of Science and Technology of China P. R. China scong@ustc.edu.cn IX Contents Preface V 1. An Adaptive Control for a Free-Floating Space Robot by Using Inverted Chain Approach 001 Satoko Abiko and Gerd Hirzinger 2. On-line Parameters Estimation with Application to Electrical Drives 017 Navid R. Abjadi, Javad Askari, Marzieh Kamali and Jafar Soltani 3. A New Frequency Dependent Approach to Model Validation 031 Pedro Balaguer and Ramon Vilanova 4. Fast Particle Filters and Their Applications to Adaptive Control in Change-Point ARX Models and Robotics 051 Yuguo Chen, Tze Leung Lai and Bin Wu 5. An Adaptive Controller Design for Flexible-joint Electrically-driven Robots With Consideration of Time-Varying Uncertainties 071 Ming-Chih Chien and An-Chyau Huang 6. Global Feed-forward Adaptive Fuzzy Control of Uncertain MIMO Nonlinear Systems 097 Chian-Song Chiu and Kuang-Yow Lian 7. Function Approximation-based Sliding Mode Adaptive Control for Time-varying Uncertain Nonlinear Systems 121 Shuang Cong, Yanyang Liang and Weiwei Shang 8. Model Reference Adaptive Control for Robotic Manipulation with Kalman Active Observers 145 Rui Cortesão 9. Triggering Adaptive Automation in Naval Command and Control 165 Tjerk de Greef and Henryk Arciszewski 10. Advances in Parameter Estimation and Performance Improvement in Adaptive Control 189 Veronica Adetola and Martin Guay X 11. Estimation and Control of Stochastic Systems under Discounted Criterion 209 Hilgert Nadine and Minjárez-Sosa J. Adolfo 12. Lipschitzian Parameterization-Based Approach for Adaptive Controls of Nonlinear Dynamic Systems with Nonlinearly Parameterized Uncer- tainties: A Theoretical Framework and Its Applications 223 N.V.Q. Hung, H.D. Tuan and T. Narikiyo 13. Model-free Adaptive Control in Frequency Domain: Application to Mechanical Ventilation 253 Clara Ionescu and Robin De Keyser 14. Adaptive Control Design for Uncertain and Constrained Vehicle Yaw Dynamics 271 Nazli E. Kahveci 15. A Design of Discrete-Time Indirect Multivariable MRACS with Structural Estimation of Interactor 281 Wataru Kase and Yasuhiko Mutoh 16. Adaptive Control in Wireless Networks 297 Thomas D. Lagkas, Pantelis Angelidis and Loukas Georgiadis 17. Adaptive Control Methodology for High-performance Low-power VLSI Design 321 Se-Joong Lee [...]... joints in the same structure as in the conventional expression This scheme is termed the inverted chain approach The following subsections explain the dynamic equations of the system in the inverted chain approach, for a serial rigid-link manipulator attached to a floating base, as shown in Fig 2 The main notations used in this section are listed in Table 1 2 .1 Equations of motion - Inverted chain... method The following reference error energy is considered as a Lyapunov function: An Adaptive Control for a Free-Floating Space Robot by Using Inverted Chain Approach 7 (10 ) The time-derivative of is given as: (11 ) where Property 2 in Section 2 is used Since the control command is expressed in eq (9), noted in Remark 1 in Section 2), the time-derivative of results in: ( (12 ) Figure 3 Control diagram... results in: (24) The inequality (24) indicates the reference error converges asymptotically to zero if and only if and Accordingly, the control law for the trajectory tracking in operational space (14 ) and the adaptation law (23) yield a stable adaptive controller Fig 4 shows the control diagram for the proposed adaptive control 10 Frontiers in Adaptive Control Figure 4 Control diagram for an adaptive. .. weighting matrix Eq (25) can be rewritten as: An Adaptive Control for a Free-Floating Space Robot by Using Inverted Chain Approach 11 (26) which indicates a time-varying low-pass filter and that parameter and tracking error convergence in composite adaptive control can be smoother and faster than in the nominal adaptive control only To analyze the stability of the system applied the above composite adaptive. .. Robot Manipulators, The Int Journal of Robotics Research, vol 6, no 3, pp 49 - 59, 19 87 Slotine, J J E & Li, W (19 88) Adaptive Manipulator Control: A Case Study, IEEE Transactions on Automatic Control, vol 33, no 11 , pp 995 - 10 03, 19 88 Slotine, J J E & Li, W (19 91) Applied Nonlinear Control: Prentice Hall, ISBN 978 013 0408907 Tsumaki, Y; Fiorini, P.; Chalfant, G & Seraji, H (20 01) A Numerical SC Approach... Impedance Control for a Free-Floating Robot in the Grasping of a Tumbling Target with Parameter Uncertainty, Proc of the 2006 IEEE/RSJ Int Conf on Intelligent Robots and Systems, pp 10 20 - 10 25, Beijing, China, Oct 2006 Gu, Y L & Xu, Y (19 93) A Normal Form Augmentation Approach to Adaptive Control of Space Robot Systems, Proc of the 19 93 IEEE Int Conf on Robotics and Automation, vol 2, pp 7 31 - 737,... (2000) Singularity-Consistent Parameterization of Robot Motion and Control, Int Journal of Robotics Research, vol 19 , no 2, pp 15 9 - 18 2, 2000 Senft, V & Hirzinger, G (19 95) Redundant Motions of Non Redundant Robots - A New Approach to Singularity Treatment, Proc of the 19 95 IEEE Int Conf on Robotics and Automation, pp 15 53 - 15 58, Nagoya, Japan, May 19 95 Slotine, J J E & Li, W (19 87) On the Adaptive Control. .. described in Section 3 In Table 4, "w/o AC", "with AC" and "with CAC" stand for the case without adaptive control, with adaptive control and the case with composite adaptive control, respectively The simulations verify that the proposed adaptive controls are effective to achieve the trajectory tracking against the parameter uncertainties An Adaptive Control for a Free-Floating Space Robot by Using Inverted... termed a free-floating robot In this chapter, we consider the free-floating robot The dynamic equation (1) possesses following important properties Property 1: The inerta matrices and are symmetric and uniformly positive-definite for all n : ∈ R 3 1 : ∈ R 3 1 : ∈ R 6 1 : n 1 ∈R : ∈ R 6×6 : ∈ R n×n : ∈ R 6×n : ∈ R 6×6 : ∈ R n×n : ∈ R 6×n : ∈ R 6 1 : ∈ R 6 1 : ∈ R 6 1 : ∈ R n 1 : ∈ R 6 1 : ∈ R 6×6 : ∈ R... model inaccuracies lead to the deviation of operational space trajectory provided by the kinematic mapping One method to deal with this issue can be found in an adaptive control Xu and Gu proposed an adaptive control scheme for space robots in both joint space and operational space [Xu et al., 19 92, Gu & Xu, 19 93] However, the adaptive control proposed in [Xu et al., 19 92] requires perfect attitude control . stable adaptive controller. Fig. 4 shows the control diagram for the proposed adaptive control. Frontiers in Adaptive Control 10 Figure 4. Control diagram for an adaptive trajectory tracking. (11 ) where Property 2 in Section 2 is used. Since the control command is expressed in eq. (9), ( noted in Remark 1 in Section 2), the time-derivative of results in: (12 ) Figure 3. Control. sliding mode adaptive control strategies have been proposed for single-input single-output(SISO) and single-input multi- ple-output(SIMO) systems with unknown bound time-varying uncertainty