Stabilization of a class of nonlinear control systems via a neural network scheme with convergence analysis

Abstract

In this paper, the stability of a class of nonlinear control systems is analyzed. We first construct an optimal control problem by inserting a suitable performance index; this problem is referred to as an infinite horizon problem. By a suitable change of variable, the infinite horizon problem is reduced to a finite horizon problem. We then present a feedback controller designing approach for the obtained finite horizon control problem. This approach involves a neural network scheme for solving the nonlinear Hamilton Jacobi Bellman equation. By using the neural network method, an analytic approximate solution for value function and a suboptimal feedback control law are achieved. A learning algorithm based on a dynamic optimization scheme with stability and convergence properties is also provided. Some illustrative examples are employed to demonstrate the accuracy and efficiency of the proposed plan. As a real-life application in engineering, the stabilization of a micro-electromechanical system is studied.