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URN etd-0712105-054747 Statistics This thesis had been viewed 2477 times. Download 1349 times. Author Ti-Hung Chen Author's Email Address No Public. Department Electrical Engineering Year 2005 Semester 2 Degree Ph.D. Type of Document Doctoral Dissertation Language English Page Count 144 Title ADAPTIVE FUZZY SLIDING MODE CONTROLLER DESIGN FOR A CLASS OF NONLINEAR UNCERTAIN SYSTEMS Keyword fuzzy sliding mode control fuzzy control adaptive control adaptive control fuzzy control fuzzy sliding mode control Abstract In this dissertation, some adaptive fuzzy sliding mode controller (AFSMC) schemes for a class of nonlinear uncertain systems are presented. The design procedures of AFSMC can be expressed as follows: first, construct the fuzzy models to describe the input/output behavior of the give nonlinear uncertain system. Then, based on the fuzzy model, design a fuzzy sliding mode controller (FSMC) to achieve the control objective. After that, design the adaptive laws for tuning the adjustable parameters of fuzzy model by Lyapunov synthesis approach. Many publications have shown that AFSMC is a powerful and robust control scheme. But, it exist some worth studying topics in design AFSMC, such as how to guarantee the H∞ tracking performance throughout the entire system states, how to treat the system that not all the system states are available for measurement, etc.
Focusing on the above-mentioned topics, this dissertation proposes the following three control strategies: (1) the modified adaptive fuzzy sliding mode controller design (MAFSMC), (2) the H∞ tracking-based adaptive fuzzy sliding mode controller design (H∞AFSMC), and (3) the observer-based adaptive fuzzy sliding mode controller design with state variable filters (O-AFSMC).
Chapter 2 first presents the modified adaptive fuzzy sliding mode controller design (MAFSMC) for a class of nonlinear uncertain systems. Conventionally, the adaptive laws of AFSMC are designed as functions of the tracking error vector. In this scheme, as the tracking error vector approach zero, the adaptive laws of AFSMC would not adjust the parameters of the fuzzy models. Hence, to compensate the modeling error, it needs relatively larger control signal for achieving the control objective. It may occur that the modeling error still exist, while the tracking error vector approaches to zero. Unlike the conventional adaptive algorithm, here, we propose the modified adaptive algorithm utilizes both the tracking error and the modeling error in its adaptive laws, such that the fuzzy model parameters would continuously update until both the tracking error and the modeling error converge to zero. Thus, the fuzzy model obtained by using the proposed MAFSMC will more accurate that of the conventional AFSMC, and the proposed MAFSMC performs better than the conventional AFSMC.
Chapter 3 presents the H∞ tracking-based adaptive fuzzy sliding mode controller design (H∞AFSMC) for a class of nonlinear uncertain systems. This control strategy incorporates the H∞ tracking control scheme into AFSMC and based on the proposed Lyapunov stability criterion, guarantees the H∞ tracking performance throughout the entire system states. After that, the H∞ tracking control problem can be characterized in terms of solving an eigenvalue problem (EVP) to be efficiently solved by using convex optimization techniques.
Chapter 4 presents the observer-based adaptive fuzzy sliding mode controller design with state variable filters (O-AFSMC) for a class of nonlinear uncertain systems, in which not all the states are available for measurement. Conventionally, to treat this controlled system, first, the observer is applied to estimate the tracking error vector. Then, based on the estimated tracking error, the control law is designed. Next, applying strictly- positive-real (SPR)-Lyapunov design approach, design the adaptive laws to adjust the parameters of the fuzzy model. Unlike SPR-Lyapunov design approach, we adopt a set of stable state variable filters to design the adaptive laws. That is, passing the observation error, the difference between the actual tracking error and the estimated tracking error, to a set of state variable filters, obtains a filtered observation error vector, and then, based on the filtered observation error vector, the adaptive laws are designed to adjust the adjustable parameters of the fuzzy model. Since only requiring the selected state variable filters must be stable, the proposed O-AFSMC is more easily to be realized than SPR-Lyapunov design approach.
The simulation results illustrate the design procedure of the proposed control strategies and demonstrate their effectiveness.
Advisor Committee Chung-Chun Kung - advisor
Bor-Sen Chen - co-chair
Chau-Yun Hsu - co-chair
Chiang-Cheng Chiang - co-chair
Ching-Chang Wong - co-chair
Wen-June Wang - co-chair
Files Date of Defense 2005-07-07 Date of Submission 2005-07-12