Research On Intelligence Control Algorithm Based On Inverted Pendulum System

Inverted pendulum is a typical fast, multivariable, non-linearity, strong- coupling and naturally unstable system. During its control process ,it can reflect many crucial questions in the control theory , such as calm question, non-linear problem, robust question as well as tracking question and so on.The research on inverted pendulum system has the profound significance in theory and project application.The correlative scientific research achievement has already applied to astronautics science technology and subject of robot and so many domains.This thesis encircled the inverted pendulum system, discusse the soft computing which including fuzzy control, nerve network(NN), genetic algorithms(GA) as well as their mutual combination systematically, study the the intelligent control algorithm of the inverted pendulum system. Towards to the single inverted pendulum , using the learning capability of nerve network to train membership function of the fuzzy controller, establishing a fuzzy controller to control the inverted pendulum through the Adaptive Neuro-Fuzzy Inference System(ANFIS).Towards to double inverted pendulums , it reduces the input variable dimension of the fuzzy controller by designing a fusion function using optimization control theory, solve the question of "rule explosion" successfully,and design the membership function and the fuzzy rule of Mamdani fuzzy controller using the expert knowledge,and optimizes the parameter of fuzzy controller using the genetic algorithms , promoted the performance of fuzzy controller .Finally realized the inverted pendulum system's practicality control by each kind of intelligent control algorithm through programming , and obtained the satisfying control effect.The control result indicated that, the combination of two or more different intelligent control algorithm, can absorbs their merits, and counterbalances their defects mutually.Due to Adaptive Nerve-Fuzzy Controller has strong learning ability, it is suitable for time-variable object. As a kind of heuristic search algorithm, though its learning time is long,but its overall search characteristic enables it to apply in the design and optimization of fuzzy system.