Stochastic Optimal Design for Unknown Linear Networked Control System Zero-sum Games via Q-Learning
Hao Xu, Salem Jagannathan · 2011
In this paper, stochastic optimal strategy for unknown linear networked control system (NCS) quadratic zero-sum games related to which satisfies a corresponding Game Theoretic Riccati equation (GRE). An adaptive estimator (AE) is proposed to learn the Q-function online and value and policy iterations are not needed unlike in traditional ADP schemes. Update laws for tuning the unknown parameters of adaptive estimator (AE) are derived. Lyapunov theory is used to show that all signals are asymptotic stable (AS) and that the approximated control and disturbance signals converge to optimal control and disturbance inputs. Simulation results are included to show the effectiveness of the scheme.