Model-free semi-global output regulation for discrete-time linear systems subject to input amplitude saturation

In this paper, a data-driven method is developed based on off-policy reinforcement learning to solve the semi-global output regulation of discrete-time linear systems with input saturation. Algebraic Riccati equation based method is used to design a family of state feedback laws for the constrained output regulation problem. In contrast to the existing methods, complete knowledge of the system dynamics is no longer required in this paper. Instead, the data collected from on-line is efficiently utilized to obtain the adaptive optimal control policy. It is shown that the presented method can find feedback control inputs with constraint of amplitude saturation and the ability to stabilize a given linear system with all its poles inside or on the unit circle. Finally, a simulation example is carried out to demonstrate the conclusions of the whole paper.