Adaptive and optimal output feedback control of linear systems: An adaptive dynamic programming approach

This paper proposes a computational adaptive optimal output feedback control method for continuous-time linear systems. By periodic sampling, we use measurable input/output data to reconstruct the unmeasurable state, and then utilize adaptive dynamic programming (ADP) technique to iteratively solve the discrete-time algebraic Riccati equation. An exploration noise is introduced for online learning purpose without compromising accuracy of the proposed iterative algorithm. The stability and the optimality of the sampled-data system in close-loop with the proposed control policy are also analyzed. The feasibility of the output feedback ADP scheme is validated by simulation on a third-order linear system.