Q-learning based tracking control with novel finite-horizon performance index.

A data-driven method is designed to realize the model-free finite-horizon optimal tracking control (FHOTC) of unknown linear discrete-time systems based on Q-learning in this paper. First, a novel finite-horizon performance index (FHPI) that only depends on the next-step tracking error is introduced. Then, an augmented system is formulated, which incorporates with the system model and the trajectory model. Based on the novel FHPI, a derivation of the augmented time-varying Riccati equation (ATVRE) is provided. We present a data-driven FHOTC method that uses Q-learning to optimize the defined time-varying Q-function. This allows us to estimate the solutions of the ATVRE without the system dynamics. Finally, the validity and features of the proposed Q-learning-based FHOTC method are demonstrated by means of conducting comparative simulation studies. [ABSTRACT FROM AUTHOR]