Model-Free Global Stabilization of Continuous-Time Linear Systems with Saturating Actuators Using Adaptive Dynamic Programming

This paper addresses the problem of global stabilization of a class of continuous-time linear systems subject to actuator saturation using a model-free approach. We propose a gain-scheduled low gain feedback scheme that prevents saturation from occurring and achieves global stabilization. The parameterized algebraic Riccati equation (ARE) framework is employed to design the low gain feedback control laws. An adaptive dynamic programming (ADP) method is presented to find the solution of the parameterized ARE without requiring the knowledge of the system dynamics. In particular, we present an iterative ADP algorithm that searches for an appropriate value of the low gain parameter and iteratively solves the parameterized ADP Bellman equation. The closed-loop stability and the convergence of the algorithm to the nominal solution of the parameterized ARE are shown. Simulation results illustrate the effectiveness of the proposed scheme.