| dc.description.abstract | In this dissertation, some combinations of Genetic Algorithms, Grey Theorem, Fuzzy Control, and Neural Networks are studied. Their applications to an active suspension system for a full-car and an inverted pendulum are illustrated by examples.
First, we demonstrate some basic propositions of the GM (1,1) model. The behaviors of the development coefficient and the grey input are proposed to simplify the calculation procedure. Furthermore, the implementation of the Genetic Algorithms in optimizing the generating coefficients of the Grey Model, GM(1,1), improves the prediction values of the modeling with respect to residual errors. Secondly, we present a grey-fuzzy control and a fuzzy neural networks control, respectively, to design an active suspension system for a full-car. Our primary control goal is to emphasize the amelioration of body oscillation. The results clearly indicate that the proposed fuzzy neural networks controller outperforms the passive, the fuzzy logic and grey-fuzzy controllers in providing the desired ride quality. The grey relational method is also applied to evaluate the grade of vehicle oscillation. The simulation data shows that the grey relational analysis method is a good option in the analysis of vehicle oscillation. Finally, back propagation (BP) neural networks in conjunction with a grey relational coefficient is used to discern the optimal partitions of the consequent part in fuzzy neural networks. The learning capability of the proposed method has been demonstrated using an inverted pendulum. The square errors by BP with GRC is much smaller than that of the classical BP in the simulation. | en_US |