Adaptive Optimal Control With Guaranteed Convergence Rate for Continuous-Time Linear Systems With Completely Unknown Dynamics

The traditional linear quadratic optimal control can be summarized as finding the state feedback controller so that the closed-loop system is stable and the performance index is minimum. And, it is well-known that the solution of the linear quadratic optimal control problem can be solved by algebraic Riccati equation. However, the feature of the traditional linear quadratic optimal control theory is that the convergence rate is not specified. This may result in the phenomena of slow convergence. In this paper, we mainly consider the linear quadratic optimal control with guaranteed convergence rate (LQOCGCR) and propose a policy iteration-based adaptive dynamic programming algorithm that includes offline and online versions for finding the solution of the LQOCGCR. Finally, a numerical example is worked out to show the effectiveness of the proposed approach.