TY - GEN
T1 - Incremental model-based heuristic dynamic programming with output feedback applied to aerospace system identification and control
AU - Sun, Bo
AU - Van Kampen, Erik Jan
PY - 2020
Y1 - 2020
N2 - Sufficient information about system dynamics and inner states is often unavailable to aerospace system controllers, which requires model-free and output feedback control techniques, respectively. This paper presents a novel self-learning control algorithm to deal with these two problems by combining the advantages of heuristic dynamic programming and incremental modeling. The system dynamics is completely unknown and only input/output data can be acquired. The controller identifies the local system models and learns control polices online both by tuning the weights of neural networks. The novel method has been applied to a multi-input multi-output nonlinear satellite attitude tracking control problem. The simulation results demonstrate that, compared with the conventional actor-critic-identifier-based heuristic dynamic programming algorithm with three networks, the proposed adaptive control algorithm improves online identification of the nonlinear system with respect to precision and speed of convergence, while maintaining similar performance compared to the full state feedback situation.
AB - Sufficient information about system dynamics and inner states is often unavailable to aerospace system controllers, which requires model-free and output feedback control techniques, respectively. This paper presents a novel self-learning control algorithm to deal with these two problems by combining the advantages of heuristic dynamic programming and incremental modeling. The system dynamics is completely unknown and only input/output data can be acquired. The controller identifies the local system models and learns control polices online both by tuning the weights of neural networks. The novel method has been applied to a multi-input multi-output nonlinear satellite attitude tracking control problem. The simulation results demonstrate that, compared with the conventional actor-critic-identifier-based heuristic dynamic programming algorithm with three networks, the proposed adaptive control algorithm improves online identification of the nonlinear system with respect to precision and speed of convergence, while maintaining similar performance compared to the full state feedback situation.
UR - http://www.scopus.com/inward/record.url?scp=85094119956&partnerID=8YFLogxK
U2 - 10.1109/CCTA41146.2020.9206261
DO - 10.1109/CCTA41146.2020.9206261
M3 - Conference contribution
AN - SCOPUS:85094119956
T3 - CCTA 2020 - 4th IEEE Conference on Control Technology and Applications
SP - 366
EP - 371
BT - CCTA 2020 - 4th IEEE Conference on Control Technology and Applications
PB - Institute of Electrical and Electronics Engineers (IEEE)
T2 - 4th IEEE Conference on Control Technology and Applications, CCTA 2020
Y2 - 24 August 2020 through 26 August 2020
ER -