Numerical Simulation For Inverted Pendulum Control System With Neural Network

Inverted pendulum control system which is a kind of perfect equipment in autocontrol, mechanism and electron is used to test the result of control. Inverted pendulum system is a nonlinear, coupling, variable and erratic system. In order to ensure the swing angle and the position of dolly to chang in a small range, a proper force must be applied to the inverted pendulum system. Neural network can be used to solve the problem of inverted pendulum control because of its ability of sel-organization, sel-adaption and generalization. BP neural network can work out how much force applied to the dolly through gradient degressive algorithm to adjust the joint weights.Artificial neural networks offer speediness, simple and convenient learing ability and application performance for nonlinear system model. Multilayer feedforward neural networks are used widely because of its intuitionistic structure, simple and convenient algorithm and the ability to approach random nonlinear continuum mapping. However, BP neural network lies the problem of slow convergence speed and the bad numerical value stability and so on. A lot of algorithms in nonlinear optimization theory are used into the learning of multilayer feedforward neural networks, and get some improvement. This article uses the BFGS optimization method which is considered to be the best optimization algorithm in solving nonrestrain question, to train the weight of BP neural network.The control of nonlinear system is applied to some simulation examples of a discrete-time system and the inverted pendulum model system to demonstrate the performance and control efficiency of the proposed method. The simulation results show that the suggested methods give better global convergent characteristics and faster convergence.