TY - GEN
T1 - Online adaptation of controller parameters based on approximate dynamic programming
AU - Guo, Wentao
AU - Liu, Feng
AU - Si, Jennie
AU - Mei, Shengwei
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/9/3
Y1 - 2014/9/3
N2 - Controller parameter tuning is an integral part of control engineering practice. Existing tuning methods usually start with an accurate mathematical model of the controlled system, which may pose some challenges for practicing engineers dealing with real systems. As such, parameter optimization and adaptation are treated as two independent steps during tuning. To address these issues, we propose a new, online parameterized controller tuning method for a general nonlinear dynamic system. This tuning method is based on direct heuristic dynamic programming (direct HDP), a model-free algorithm in the approximated dynamic programming (ADP) family. By using a Lyapunov stability approach, we provide uniformly ultimately bounded (UUB) results under some mild conditions for controller parameters, the critic neural network weights, and the action neural network weights. Simulation studies based on the benchmark cart-pole system demonstrate adaptability and optimization capabilities of the proposed controller parameter tuning method.
AB - Controller parameter tuning is an integral part of control engineering practice. Existing tuning methods usually start with an accurate mathematical model of the controlled system, which may pose some challenges for practicing engineers dealing with real systems. As such, parameter optimization and adaptation are treated as two independent steps during tuning. To address these issues, we propose a new, online parameterized controller tuning method for a general nonlinear dynamic system. This tuning method is based on direct heuristic dynamic programming (direct HDP), a model-free algorithm in the approximated dynamic programming (ADP) family. By using a Lyapunov stability approach, we provide uniformly ultimately bounded (UUB) results under some mild conditions for controller parameters, the critic neural network weights, and the action neural network weights. Simulation studies based on the benchmark cart-pole system demonstrate adaptability and optimization capabilities of the proposed controller parameter tuning method.
UR - http://www.scopus.com/inward/record.url?scp=84908494506&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84908494506&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2014.6889869
DO - 10.1109/IJCNN.2014.6889869
M3 - Conference contribution
AN - SCOPUS:84908494506
T3 - Proceedings of the International Joint Conference on Neural Networks
SP - 256
EP - 262
BT - Proceedings of the International Joint Conference on Neural Networks
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 International Joint Conference on Neural Networks, IJCNN 2014
Y2 - 6 July 2014 through 11 July 2014
ER -