| Article ID: | A31215 |
| PMID: | 31021775 |
| Source: | IEEE Trans Neural Netw Learn Syst |
| Title: | H∞ Static Output-Feedback Control Design for Discrete-Time Systems Using Reinforcement Learning. |
| Abstract: | This paper provides necessary and sufficient conditions for the existence of the static output-feedback (OPFB) solution to the H∞ control problem for linear discrete-time systems. It is shown that the solution of the static OPFB H∞ control is a Nash equilibrium point. Furthermore, a Q-learning algorithm is developed to find the H∞ OPFB solution online using data measured along the system trajectories and without knowing the system matrices. This is achieved by solving a game algebraic Riccati equation online and using the measured data. A simulation example shows the effectiveness of the proposed method. |
| DOI: | 10.1109/TNNLS.2019.2901889 |
