TY - GEN
T1 - Neural network and extended state observer based sliding mode control of electro-hydrostatic actuators
AU - Alemu, Ataklti E.
AU - Fu, Jian
AU - Fu, Yongling
PY - 2016
Y1 - 2016
N2 - Electro-hydrostatic actuators (EHA) are integrated, electrically powered, hydraulic actuators that are used to drive aircraft control surfaces or other moving parts that need hydraulic power. Some of the advantages of EHA are improved reliability, efficiency and reduction in the overall weight of the actuation system. However, design of a high performance EHA controller is challenging because of the variations of its parameters, nonlinear actuator friction, leakages and model uncertainty. To achieve the desired performance of an EHA, this paper proposes a hybrid control algorithm that combines the merits of radial basis function neural network (RBFNN) and sliding mode control (SMC). An RBFNN is used to approximate the uncertainties of EHA and the weights of its output layers are updated based on Lyapunov stability analysis. Besides, implementation of this control method demands full state availability of EHA and an extended state observer is designed. Furthermore, the mathematical model of an EHA involves derivative of a friction force and it is obtained by using a continuous approximation of a LuGre friction model. The performance of the proposed controller is compared with a PID controller. Simulation results illustrated the chattering elimination, superior tracking performance and robustness of the RBFNN based SMC.
AB - Electro-hydrostatic actuators (EHA) are integrated, electrically powered, hydraulic actuators that are used to drive aircraft control surfaces or other moving parts that need hydraulic power. Some of the advantages of EHA are improved reliability, efficiency and reduction in the overall weight of the actuation system. However, design of a high performance EHA controller is challenging because of the variations of its parameters, nonlinear actuator friction, leakages and model uncertainty. To achieve the desired performance of an EHA, this paper proposes a hybrid control algorithm that combines the merits of radial basis function neural network (RBFNN) and sliding mode control (SMC). An RBFNN is used to approximate the uncertainties of EHA and the weights of its output layers are updated based on Lyapunov stability analysis. Besides, implementation of this control method demands full state availability of EHA and an extended state observer is designed. Furthermore, the mathematical model of an EHA involves derivative of a friction force and it is obtained by using a continuous approximation of a LuGre friction model. The performance of the proposed controller is compared with a PID controller. Simulation results illustrated the chattering elimination, superior tracking performance and robustness of the RBFNN based SMC.
KW - Electro-Hydrostatic Actuator(EHA)
KW - Extended State Observer(ESO)
KW - LuGre Friction Model
KW - Radial Basis Function Neural Network (RBFNN)
KW - Sliding Mode Control(SMC)
UR - https://www.scopus.com/pages/publications/85014870081
U2 - 10.2316/P.2016.830-050
DO - 10.2316/P.2016.830-050
M3 - 会议稿件
AN - SCOPUS:85014870081
T3 - Proceedings of the IASTED International Conference on Modelling, Identification and Control
SP - 145
EP - 152
BT - Proceedings of the 35th IASTED International Conference on Modelling, Identification and Control, MIC 2016
PB - Acta Press
T2 - 35th IASTED International Conference on Modelling, Identification and Control, MIC 2016
Y2 - 15 February 2016 through 16 February 2016
ER -