Title :
Finite-horizon optimal control design for uncertain linear discrete-time systems
Author :
Qiming Zhao ; Hao Xu ; Jagannathan, Sarangapani
Author_Institution :
Dept. of Electr. & Comput. Eng., Missouri Univ. of S&T, Rolla, MO, USA
Abstract :
In this paper, the finite-horizon optimal adaptive control design for linear discrete-time systems with unknown system dynamics by using adaptive dynamic programming (ADP) is presented. In the presence of full state feedback, the terminal state constraint is incorporated in solving the optimal feedback control via the Bellman equation. The optimal regulation of the uncertain linear system is solved in a forward-in-time and online manner without using value and/or policy iterations. Due to the nature of finite horizon, the stability of the closed-loop system is involved but verified by using Lyapunov theory. The effectiveness of the proposed method is verified by simulation results.
Keywords :
Lyapunov methods; adaptive control; closed loop systems; control system synthesis; discrete time systems; dynamic programming; linear systems; optimal control; state feedback; uncertain systems; ADP; Bellman equation; Lyapunov theory; adaptive dynamic programming; closed-loop system; finite-horizon optimal adaptive control design; optimal feedback control; terminal state constraint; uncertain linear discrete-time systems; unknown system dynamics; Dynamic programming; Equations; Learning (artificial intelligence); Linear systems; Mathematical model; Optimal control; Vectors; Adaptive Estimator; Finite-horizon Optimal Control; Linear System; Optimal Control; Q-learning;
Conference_Titel :
Adaptive Dynamic Programming And Reinforcement Learning (ADPRL), 2013 IEEE Symposium on
Conference_Location :
Singapore
DOI :
10.1109/ADPRL.2013.6614982
