Integral reinforcement learning-based optimal output feedback control for linear continuous-time systems with input delay.

In this paper, an integral reinforcement learning (IRL)-based model-free optimal output-feedback (OPFB) control scheme is developed for linear continuous-time systems with input delay, where the input and past output data are employed rather than the system dynamic model. First, the equivalence between the delayed optimal control and delay-free case is analyzed. Subsequently, the system state is constructed with output signal and the Bellman equation is written in the form of past outputs. Therefore, the IRL algorithm is developed to learn the OPFB control policy, where the iterative policy is evaluated and improved simultaneously. It is proved that the obtained optimal OPFB controller gives the same solution as the optimal state-feedback. Finally, the presented simulation results illustrate the effectiveness of the developed control method. [ABSTRACT FROM AUTHOR]