Liu Lu. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaves 65-70). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.ii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- The Output Regulation Problem --- p.1 / Chapter 1.2 --- Control Design with Unknown High-frequency Gain Sign --- p.3 / Chapter 1.3 --- Contribution of the Thesis --- p.4 / Chapter 1.4 --- Thesis Outline --- p.5 / Chapter 2 --- Global Robust Stabilization of a Class of Nonlinear Systems --- p.6 / Chapter 2.1 --- Introduction --- p.7 / Chapter 2.2 --- Problem Formulation and Preliminaries --- p.8 / Chapter 2.3 --- Main Result --- p.11 / Chapter 2.4 --- An Example --- p.20 / Chapter 2.5 --- Application of Theorem 2.1 --- p.26 / Chapter 2.5.1 --- Chua's Circuit and Control Problem --- p.26 / Chapter 2.5.2 --- Solvability of the Control Problem --- p.28 / Chapter 2.5.3 --- Simulation Results --- p.32 / Chapter 2.5.4 --- Conclusion --- p.33 / Chapter 2.6 --- Conclusion --- p.36 / Chapter 3 --- Global Robust Output Regulation of Nonlinear Systems in Output Feedback Form --- p.39 / Chapter 3.1 --- Introduction --- p.40 / Chapter 3.2 --- Output Regulation Converted to Stabilization --- p.42 / Chapter 3.3 --- Main Result --- p.49 / Chapter 3.4 --- An Example --- p.55 / Chapter 3.5 --- Conclusion --- p.58 / Chapter 4 --- Conclusions --- p.62 / List of Figures --- p.64 / Bibliography --- p.65 / Biography

Chen Zhiyong. / "April 2005." / Thesis (Ph.D.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (p. 205-215) / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.

Ng Chi-fai. / Thesis submitted in: December 2000. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 69-73). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgments --- p.iii / List of Figures --- p.viii / List of Tables --- p.ix / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation and Objectives --- p.1 / Chapter 1.2 --- Principles of Feedforward Neural Network Approximation --- p.1 / Chapter 1.3 --- Contribution of The Thesis --- p.5 / Chapter 1.4 --- Outline of The Thesis --- p.5 / Chapter 2 --- Feedforward Neural Networks: An Approximator for Nonlinear Control Law --- p.8 / Chapter 2.1 --- Optimization Methods Applied in Feedforward Neural Network Approximation --- p.8 / Chapter 2.2 --- Example in Supervised Learning --- p.10 / Chapter 2.2.1 --- Problem Description --- p.10 / Chapter 2.2.2 --- Neural Network Configuration and Training --- p.12 / Chapter 2.2.3 --- Simulation Result --- p.13 / Chapter 3 --- Neural Based Approximation of Center Manifold Equations --- p.19 / Chapter 3.1 --- Solving Center Manifold Equations by Feedforward Neural Network Approx- imation --- p.19 / Chapter 3.2 --- Example --- p.21 / Chapter 3.2.1 --- Problem Description --- p.21 / Chapter 3.2.2 --- Simulation Result --- p.24 / Chapter 3.2.3 --- Discussion --- p.24 / Chapter 4 --- Connection of Center Manifold Equations to Output Regulation Problem --- p.29 / Chapter 4.1 --- Output Regulation Theory --- p.29 / Chapter 4.2 --- Reduction of Regulator Equation into Center Manifold Equations --- p.31 / Chapter 5 --- Application to the Control Design of Ball and Beam System --- p.34 / Chapter 5.1 --- Problem Description --- p.34 / Chapter 5.2 --- Neural Approximation Solution of Center Manifold Equations --- p.37 / Chapter 5.3 --- Simulation Results --- p.38 / Chapter 5.4 --- Discussion --- p.45 / Chapter 6 --- Neural Based Disturbance Rejection of Nonlinear Benchmark Problem (TORA System) --- p.48 / Chapter 6.1 --- Problem Description --- p.48 / Chapter 6.2 --- Neural based Approximation of the Center Manifold Equations of TORA System --- p.51 / Chapter 6.3 --- Simulation Results --- p.53 / Chapter 6.4 --- Discussion --- p.59 / Chapter 7 --- Conclusion --- p.62 / Chapter 7.1 --- Future Works --- p.63 / Chapter A --- Center Manifold Theory --- p.64 / Chapter B --- Relation between Center Manifold Equation and Output Regulation Prob- lem --- p.66 / Biography --- p.68 / References --- p.69

Zhong, Renxin. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (leaves 107-114). / Abstracts in English and Chinese. / Abstract --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Non-Minimum Phase Nonlinear Systems --- p.1 / Chapter 1.2 --- Robust Output Regulation Problem --- p.4 / Chapter 1.3 --- Global Robust Output Regulation for Non-Minimum Phase Nonlinear Systems in Lower Triangular Form --- p.6 / Chapter 1.4 --- Rotational/Translational Actuator System --- p.8 / Chapter 1.5 --- Organization and Contributions --- p.8 / Chapter 2 --- Global Robust Output Regulation for Non-Minimum Phase Non-linear Systems in Lower Triangular Form --- p.10 / Chapter 2.1 --- Introduction --- p.10 / Chapter 2.2 --- Assumptions and Preliminaries --- p.12 / Chapter 2.3 --- Solvability Conditions --- p.17 / Chapter 2.4 --- Numerical Examples --- p.19 / Chapter 2.5 --- Concluding Remarks --- p.46 / Chapter 3 --- Global Robust Output Regulation for A Class of Non-Minimum Phase Nonlinear Systems by Output Feedback Control --- p.47 / Chapter 3.1 --- Introduction --- p.48 / Chapter 3.2 --- Assumptions and Preliminaries --- p.49 / Chapter 3.3 --- Reduced order observer design --- p.56 / Chapter 3.4 --- Stabilization of x system --- p.59 / Chapter 3.5 --- "Interconnection of the n,z,ζ,x subsystems and small gain condition" --- p.63 / Chapter 3.6 --- Numerical example --- p.67 / Chapter 3.7 --- Conclusion --- p.76 / Chapter 4 --- Robust output regulation for the nonlinear benchmark problem via output feedback --- p.77 / Chapter 4.1 --- Introduction --- p.78 / Chapter 4.2 --- Disturbance rejection problem of the RTAC system by output feedback control --- p.79 / Chapter 4.3 --- Robust Disturbance rejection problem of the RTAC system by output feedback --- p.88 / Chapter 4.4 --- Conclusion --- p.98 / Chapter 5 --- Conclusion --- p.103 / List of Figures --- p.105 / Bibliography --- p.107 / Biography --- p.115

This work describes analysis, design, and implementation of a novel switching robust adaptive control (SRAC) method for nonlinear systems. The proposed method takes advantage of both adaptive control (AC) and robust control (RC) methods. SRAC employs one of the methods when this method is advantageous and switches to the other method when the other one becomes the preferred choice. To this end, RC is used to deal with transient effects caused by uncertainties and disturbances. The system switches over to AC for good steady state performance when certain switching criteria are satisfied. If external disturbances become dominant or new uncertainties are introduced while AC is active, the system will switch back to RC. In this manner, the switching process between AC and RC will continue to take place guaranteeing improved performance, robustness, and accuracy for the entire operation of the system. The novel idea behind the proposed method is a smart novel mechanism of bi-directional switching between RC and AC. In this mechanism, the involvement of estimators and switching rules play a decisive part in guaranteeing the smooth switching and the stability of the system. The implementation and design issues of the novel method were first evaluated by simulation on a mass spring system and then on a robot manipulator system. To control these systems with satisfactory performance, nonlinearities and uncertainties have been properly analysed and embedded into models and control algorithms. Simulation results showed the superior performance of the proposed method compared with other control methods. The experimental validation of the proposed method was conducted on a Puma 560 robot manipulator system which was established by joints 2 and 3 of the robot. Extensive comparative experimental results have validated the efficacy and superior performance of the proposed SRAC method over other control methods in the face of uncertainties and disturbances. As part of this work, a comprehensive dynamic model of robotic manipulator in the presence of joint motors, gravitational forces, friction forces and payload has been developed using MAPLE. A systematic design framework for the SRAC method has also been developed.

Thesis (Ph. D.)--University of Missouri--Rolla, 2007. / Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed December 6, 2007) Includes bibliographical references.

The main focus of this work is on the problem of existence of nonlinear optimal controllers realizable by artificial neural networks. Theoretical justification, currently available for control applications of neural networks, is rather limited. For example, it is unclear which neural architectures are capable of performing which control tasks. This work addresses applicability of neural networks to the synthesis of approximately optimal state feedback. Discrete-time setting is considered, which brings extra regularity into the problem and simplifies mathematical analysis. Two classes of optimal control problems are studied: time-optimal control and optimal control with summable quality index. After appropriate relaxation of the optimization problem, the existence of a suboptimal feedback mapping is demonstrated in both cases. It is shown that such a feedback may be realized by a multilayered network with discontinuous neuron activation functions. For continuous networks, similar results are obtained, with the existence of suboptimal feedback demonstrated, except for a set of initial states of an arbitrarily small measure. The theory developed here provides basis for an attractive approach of the synthesis of near-optimal feedback using neural networks trained on optimal trajectories generated in open loop. Potential advantages of control based on neural networks are illustrated on application to stabilization of interconnected power systems. A nearly time-optimal controller is designed for a single-machine system using neural networks. The obtained controller is then utilized as an element of a hierarchical control architecture used for stabilization of a multimachine power transmission system. This example demonstrates applicability of neural control to complicated, nonlinear dynamic systems. / Graduation date: 1995

When large disturbances occur in interconnected power systems, there exists the danger that the power system states may leave an associated region of stability, if timely corrective action is not taken. Open-loop remedial control actions such as field-forcing, line-tripping, switching of series-capacitors, energizing braking resistors, etc., are helpful in reducing the effects of the disturbance, but do not guarantee that the post-fault power system will be stabilized. Linear controllers are widely used in the power industry, and provide excellent damping when the power system state is close to the equilibrium. In general, they provide conservative regions of stability. This study focuses on the development of nonlinear controllers to enhance the stability of interconnected power systems following large disturbances, and allow stable operation at high power levels. There is currently interest in the power industry in using thyristor-controlled series-capacitors for the dual purpose of exercising tighter control on steady-state power flows and enhancing system stability. This device is used to implement the nonlinear controller in this dissertation. A mathematical model of the power system controlled by the thyristor-controlled series-capacitor is developed for the purpose of controller design. Discrete-time, nonlinear predictive controllers are derived by minimizing criterion functions that are quadratic in the output variables over a finite-horizon of interest, with respect to the control variables. The control policies developed in this manner are centralized in nature. The stabilizing effect of such controllers is discussed. A potential drawback is the need to have large prediction horizons to assure stability. In this context, a coordinated-control policy is proposed, in which the nonlinear predictive controller is designed with a small prediction horizon. For a class of disturbances, such nonlinear predictive controllers return the power system state to a small neighborhood of the post-fault equilibrium, where linear controllers provide asymptotic stabilization and rapid damping. Methods of coordinating the controllers are discussed. Simulation results are provided on a sample four-machine power system model. There exists considerable uncertainty in power system models due to constantly shifting loads and generations, line-switching following disturbances, etc. The performance of fixed-parameter controllers may not be good when the plant description changes considerably from the reference. In this context, a bilinear model-based self-tuning controller is proposed for the stabilization of power systems for a class of faults. A class of generic predictive controllers are presented for use with the self-tuning controller. Simulation results on single-machine and multimachine power systems are provided. / Graduation date: 1995

A new decentralized nonlinear controller for Robot Manipulators is presented in this thesis. Based on concepts of Lyapunov stability theory and some control ideas proposed in [3]-[7], we obtain continuous nonlinear decentralized control laws which guarantee position and velocity tracking to within an arbitrarily small error. Assumptions based on physical constraints of manipulators are made to guarantee the existence of the controller and asymptotic stability of the closed loop system. Simulations show how well this rather simple control scheme works on two of the links of the Puma 560 Manipulator. The main contribution of this thesis is that it extends the results of a class of complex centralized control algorithms to the decentralized robust control of interconnected nonlinear subsystems like robot manipulators. / Graduation date: 1992

An adaptive decentralized nonlinear controller for a robot manipulator is presented in this thesis. Based on the adaptive control schemes designed by Seraji [18], Dai [30], and Jimenez [31], we redesigned and further simplified the control algorithm and, as a consequence, we achieved better path tracking performance. The proposed adaptive controller is made of a PD feedback controller which has time varying gains, a feedforward compensator based on the idea of inverse dynamics, and an auxiliary signal. Due to its adaptive structure, the controller shows robustness against disturbances and unmodeled dynamics. In order to ensure asymptotic tracking we select a Lyapunov function such that the controller forces the negative definiteness of the time derivative of such a Lyapunov function. To do this, the tracking position and velocity error are penalized and used as a part of the adaptive control gain. The main advantages of this scheme are the comparably faster convergence of tracking error, relatively simpler structure, and smoother control activity. This controller only requires the position and angular speed measurement, it does not require any knowledge about the mathematical model of the robot manipulator. Simulation shows the capacity of this controller and its robustness against disturbances. / Graduation date: 1993