TY - GEN
T1 - Data-driven adaptive optimal output-feedback control of a 2-DOF helicopter
AU - Gao, Weinan
AU - Jiang, Zhong Ping
N1 - Funding Information:
This work has been partly supported by the U.S. National Science Foundation grants ECCS-1230040 and ECCS-1501044.
Publisher Copyright:
© 2016 American Automatic Control Council (AACC).
PY - 2016/7/28
Y1 - 2016/7/28
N2 - This paper studies a data-driven adaptive optimal control problem of a Quanser's 2-degree-of-freedom (DOF) helicopter via output-feedback. A novel sampled-data-based approximate/adaptive dynamic programming (ADP) approach is developed. We start from a stabilizing controller computed using the bound of model uncertainties. Then the optimal control gain is iteratively learned by input/output information. The convergence of the proposed approach is theoretically ensured and the tradeoff between optimality and sampling period is rigorously studied as well. Finally, we show the performance of the proposed algorithm under bounded model uncertainties.
AB - This paper studies a data-driven adaptive optimal control problem of a Quanser's 2-degree-of-freedom (DOF) helicopter via output-feedback. A novel sampled-data-based approximate/adaptive dynamic programming (ADP) approach is developed. We start from a stabilizing controller computed using the bound of model uncertainties. Then the optimal control gain is iteratively learned by input/output information. The convergence of the proposed approach is theoretically ensured and the tradeoff between optimality and sampling period is rigorously studied as well. Finally, we show the performance of the proposed algorithm under bounded model uncertainties.
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U2 - 10.1109/ACC.2016.7525294
DO - 10.1109/ACC.2016.7525294
M3 - Conference contribution
AN - SCOPUS:84992107598
T3 - Proceedings of the American Control Conference
SP - 2512
EP - 2517
BT - 2016 American Control Conference, ACC 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 American Control Conference, ACC 2016
Y2 - 6 July 2016 through 8 July 2016
ER -