Data-Driven and Internal Model Control for Optimal Output Regulation of Linear Systems with an Assured Convergence Rate

This paper addresses an optimal output regulation problem with a requirement on convergence rate for linear time-invariant systems with unknown system dynamics. First, an internal model is introduced to transform the problem into a dynamic optimization problem. Then, a new virtual system is designed to replace the original system and internal model. By collecting the data from the virtual system, a novel data-driven learning algorithm is proposed, which not only learns the optimal control policy but also guarantees the convergence rate of the tracking error to be no slower than a predefined convergence rate. Finally, an illustrative example is presented to validate the proposed approach.