Immune Sliding Mode Variable Structure Theory And Its Application In Vector Control

Sliding mode variable structure control is essentially a special kind of nonlinear control, When the system state in the sliding mode surface, the system are highly robust to internal parameters change and external disturbances. But in fact, after the state tracking reach the sliding mode surface, it is difficult to strictly follow slide along sliding mode surface to the equilibrium point but slide back and forth across the surface on both sides, resulting in chattering phenomena.First of all, this paper designs a method of adaptive fuzzy sliding mode control, in order to solve the problems of conventional sliding mode contorl, such as low performance of robustness and chattering phenomena. This method combines the advantages of fuzzy control, sliding mode variable structure control and adaptive control. In this way, the paper designs two different adaptive fuzzy controller: one is used to approximate the internal uncertain parameter of nonlinear system in equivalent sliding mode control, in order to desensiblize the internal parameter change of the system; the other to continuously approximate high frequency switching function, in order to eliminate the impact of external disturbances on the system and chattering phenomena. The simulation results prove that adaptive fuzzy sliding mode control system is more robust and adaptive than conventional sliding mode control system, and the system chattering phenomena has been restrained.And then by using the strong global convergence capability and robustness of immune algorithm, this paper designs the hybrid training algorithm for RBF neural network which uses immune genetic algorithm to train the"center"and"width"of network hidden units, uses the least square method to value optimization, and improves the learning efficiency and identification precision of network. The simulation results prove this method is more significantly higher of approaching precision to the nonlinear system than that of commonly used gradient descent training algorithm.Finally, this paper use the theory of sliding mode variable structure control to design speed controller in the vector control system, in order to improve the system performance of overshoot and anti-jamming; replace sliding mode switch control with mentioned above RBF network which is based on immune genetic algorithm, in order to inhibit chattering phenomena caused by the sliding mode control in system. Compared the method with PI controller vector control, the simulation results show that using RBFNN-based sliding mode control of the asynchronous motor vector control system is with better performance.