Continuous-time Value-Iteration-Based Learning for Constrained-Input Nonlinear Nonzero-Sum Game

Abstract

A continuous-time value iteration based learning method is proposed for constrained-input nonlinear nonzero-sum game in this paper. Most existing studies were based on policy iteration, and thus they require an initial admissible control policy as the initial condition or some proper control policy to make the states satisfy the persistent excitation (PE) condition. However, no mater the initial admissible control policy nor a PE satisfied control policy, they can not be derived by a general feasible way. Such difficulty of choosing control policy may limit the actual application. The proposed method is developed based on value iteration and the requirement of choosing proper control policy can be avoided. Moreover, since the control signal should always be designed within limits in practice, the constrained-input property is taken into consideration. Simulation results are displayed to show the effectiveness.