TY - GEN
T1 - Data-driven adaptive optimal tracking control for completely unknown systems
AU - Hou, D.
AU - Na, Jing
AU - Gao, G.
AU - Li, G.
PY - 2018
Y1 - 2018
N2 - In this paper, an online data-driven based solution is developed for linear quadratic tracking (LQT) problem of linear systems with completely unknown dynamics. By applying the vectorization operator and Kronecker product, an adaptive identifier is first built to identify the unknown system dynamics, where a new adaptive law with guaranteed convergence is proposed. By using system augmentation method and introducing a discounted factor in the cost function, a compact form of LQT formulation is proposed, where the feedforward and feedback control actions can be obtained simultaneously. Finally, a new policy iteration is introduced to solve the derived augmented algebraic Riccati equation (ARE). Simulation results are presented to demonstrate the effectiveness of the proposed algorithm
AB - In this paper, an online data-driven based solution is developed for linear quadratic tracking (LQT) problem of linear systems with completely unknown dynamics. By applying the vectorization operator and Kronecker product, an adaptive identifier is first built to identify the unknown system dynamics, where a new adaptive law with guaranteed convergence is proposed. By using system augmentation method and introducing a discounted factor in the cost function, a compact form of LQT formulation is proposed, where the feedforward and feedback control actions can be obtained simultaneously. Finally, a new policy iteration is introduced to solve the derived augmented algebraic Riccati equation (ARE). Simulation results are presented to demonstrate the effectiveness of the proposed algorithm
UR - http://www.scopus.com/inward/record.url?eid=2-s2.0-85057038235&partnerID=MN8TOARS
U2 - 10.1109/DDCLS.2018.8515964
DO - 10.1109/DDCLS.2018.8515964
M3 - Conference contribution
BT - Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018
ER -