URN etd-0903109-141232 Statistics This thesis had been viewed 1705 times. Download 4 times. Author Chih-Chia Chao Author's Email Address No Public. Department Electrical Engineering Year 2008 Semester 2 Degree Master Type of Document Master's Thesis Language English Page Count 104 Title STUDY OF ADAPTIVE SELF-CONSTRUCTING FUZZY NEURAL NETWORK FOR UNCERTAIN NONLINEAR SYSTEMS Keyword inverted pendulum robust control estimator fuzzy neural network fuzzy neural network estimator robust control inverted pendulum Abstract The adaptive self-constructing fuzzy neural network (ASCFNN) controller is proposed for the uncertain nonlinear systems in this thesis. The ASCFNN control system is composed of an ASCFNN identifier, a computation controller and a robust controller. The ASCFNN identifier is used to estimate parameters of the uncertain nonlinear system. The computation controller is designed to sum up the output of the ASCFNN identifier. The robust controller is designed to compensate the uncertainties of the system parameters and uncertain external disturbance, and achieve robust stability of the system. The structure and parameter learnings are adopted in the ASCFNN identifier to achieve favorable approximation performance. The Mahalanobis distance (M-distance) method in the structure learning is employed to determine if the fuzzy rules are generated/ eliminated or not. Concurrently, the adaptive laws are derived based on the sense of Lyapunov so that the stability of the system can be guaranteed. Finally, the simulation results and the experiment which integrates the linear induction motor (LIM) and an inverted pendulum (IP) are implemented to verify the effectiveness of the proposed ASCFNN controller. Advisor Committee Hung-Ching Lu - advisor
Files indicate not accessible Date of Defense 2009-07-29 Date of Submission 2009-09-03