This paper proposes an adaptive switch controller (ASC) design for the nonlinear multi-input multi-output system (MIMO). In\nfact, the proposed method is an online switch between the neural network adaptive PID (APID) controller and the neural network\nindirect adaptive controller (IAC). According to the design of the neural network IAC scheme, the adaptation law has been\ndeveloped by the gradient descent (GD) method. However, the adaptive PID controller is built based on the neural network\ncombining the PID control and explicit neural structure. The strategy of training consists of online tuning of the neural controller\nweights using the backpropagation algorithm to select the suitable combination of PID gains such that the error between the\nreference signal and the actual system output converges to zero. The stability and tracking performance of the neural network\nASC, the neural network APID, and the neural network IAC are analyzed and evaluated by the Lyapunov function. Then, the\ncontroller results are compared between APID, IAC, and ASC, in this paper, applying to a nonlinear system. From simulations, the\nproposed adaptive switch controller has better effects both on response time and on tracking performance with smallest MSE.
Loading....