Online adaptive optimal control for continuous-time Markov jump linear systems using a novel policy iteration algorithm

This study studies the online adaptive optimal control problems for a class of continuous-time Markov jump linear systems (MJLSs) based on a novel policy iteration algorithm. By utilising a new decoupling technique named subsystems transformation, the authors re-construct the MJLSs and a set of new coupled systems composed of N subsystems are obtained. The online policy iteration algorithm was used to solve the coupled algebraic matrix Riccati equations with partial knowledge regarding to the system dynamics, and the relevant optimal controllers equivalent to the investigated MJLSs are designed. Moreover, the convergence of the novel policy iteration algorithm is also established. Finally, a simulation example is given to illustrate the effectiveness and applicability of the proposed approach.