Research On Sliding Mode-Predictive Control

This paper has proposed a new control strategy, sliding mode predictive control focuses to solve the problems of model mismatch, disturbance and delay during the process of control. There are three important parts in this paper: the first one is respectively to design and simulate system by way of sliding mode control strategy and the predictive control strategy, to analyze advantages and disadvantages of the two strategies. In the second part, the improved reaching law for sliding mode control systems is proposed. A new method combines the quasi-sliding mode control strategies with a novel reaching law. The reaching law is improved by introducing a boundary layer, outside which an exponential reaching law is applied and inside which the feedback control strategy is used. Simulation results show that the proposed approach is able to effectively eliminate chattering of the sliding mode control system. The last one is to propose three cases to settle the problems of the model mismatch and error, disturbance and delay by means of the sliding mode -predictive control strategy. During the simplifying the high order system to the low order system, between the real model and the simplified model there is an error. At the same time, the sliding mode control can not dispose the restrains of the state and control. Predictive control has the advance to make up for these problems, while it can not ensure the stability, so switching function is introduced. Through the predictive controller the increment of input acts and corrects the sliding mode controller, we can obtain the better results. According to the delay-time system, we can predict the system, and build the system's model that is corrected by the error between the real value and output value. During the sliding mode control we can predict the switching function to avoid the uncertain conditions.Sliding mode-predictive control is a new kind of strategy that can be adapted to many types of systems, such as nonlinear system, and some systems including restrictions or uncertain conditions. This strategy can make the control system realize optimization on condition that ensures the system's stability.