Data-Driven Based Optimal Output-Feedback Control of Continuous-Time Systems

In this paper, we propose a novel method to solve the optimal output-feedback control problem of continuous-time (CT) linear systems based on a data-driven based reinforcement learning (RL). An output-feedback Riccati equation is first derived by further tailoring its counterpart of state-feedback optimal control. Then, based on this modified Riccati equation, we further derive an output Lyapunov function, where only the system output rather than the unknown state is involved. This allows to obtain the optimal output-feedback gain based on the measured output only. Then, an online data-driven based policy iteration is suggested to obtain the feedback gain K and matrix P. Finally, a simulation example is given to prove the effectiveness of the proposed algorithm.