Adaptive Optimal Control for Large-Scale Systems based on Robust Policy Iteration

In this paper, a robust policy iteration (PI) based adaptive optimal control approach is proposed for large-scale systems. The parametric and dynamic uncertainties are coped with by using the robust adaptive dynamic programming and small-gain theory. Based on the robust policy iteration, the learned control policy is composed of the measurement state and input. The closed-loop stability and the convergence of the proposed algorithms are analyzed by using Lyapunov stability theory and small-gain techniques. A practical example of multimachine power systems with governor controllers is given to demonstrate the effectiveness of the proposed method.