Sliding Mode Control Method Based On Particle Swarm Optimization

For a class of nonlinear system, the control strategies of combining the sliding mode control with fuzzy control, neural network control and adaptive control respectively are developed and deduced. The particle swarm optimization (PSO) algorithm is adopted to optimize system parameters. Finally the effectiveness of control methods above is validated by the inverted pendulum model.Firstly, a new fuzzy sliding mode controller method is proposed for a class of nonlinear system based on PSO. The tracking error is driven into the given boundary layer by the sliding mode control, and then the switch control is replaced by fuzzy control. In order to ensure the global stability of the system, supervise controller is used to smooth the control input. Finally, PSO is adopted to optimize the controller parameters.Secondly, in view of the disturbance upper bound is difficult to be determined. The fuzzy neural network control is introduced to the sliding mode controller design. Here the output of fuzzy neural network is utilized to approach the unknown part and the system uncertainties to realize the adaptive sliding mode control. The parameters of the controller are optimized by PSO to ensure a good dynamic performance.Finally, In view of a class of uncertain nonlinear system, a new design method of adaptive sliding mode controller based on PSO algorithm is proposed. In this paper, the outputs of fuzzy logic controller approaches the unknown function and the system's total uncertainty, and then construct parameter adaptive laws of the fuzzy model under the principle of sliding mode control; At last, using PSO optimizes parameters of the controller and adaptive parameters. This approach keeps the strong robustness of the sliding mode controller and meantime enhances system's response speed.