TY - GEN
T1 - Fault tolerant control for a class of nonlinear system based on active disturbance rejection control and rbf neural networks
AU - Zhou, Lushan
AU - Ma, Liling
AU - Wang, Junzheng
N1 - Publisher Copyright:
© 2017 Technical Committee on Control Theory, CAA.
PY - 2017/9/7
Y1 - 2017/9/7
N2 - In this paper, a fault tolerant control method based on active disturbance rejection control (ADRC) and radial basis function neural network (RBFNN) is proposed for a class of multi-input-multi-output nonlinear system with actuator faults, components faults and sensor faults. The proposed method does not rely on the plant model. By regarding the faults and plant uncertainties as the disturbance, through the observation of extended state observer and the compensation of feedback control signal, this method achieves the fault tolerance control of the plant with component fault and actuator fault. For sensor faults, in this work, radial basis function neural network is applied to estimate the real output of the system. Then this output estimation is utilized by active disturbance rejection control to achieve the fault tolerance of sensor. Finally, the effectiveness of the proposed method is validated by the simulation results of the three-tank system.
AB - In this paper, a fault tolerant control method based on active disturbance rejection control (ADRC) and radial basis function neural network (RBFNN) is proposed for a class of multi-input-multi-output nonlinear system with actuator faults, components faults and sensor faults. The proposed method does not rely on the plant model. By regarding the faults and plant uncertainties as the disturbance, through the observation of extended state observer and the compensation of feedback control signal, this method achieves the fault tolerance control of the plant with component fault and actuator fault. For sensor faults, in this work, radial basis function neural network is applied to estimate the real output of the system. Then this output estimation is utilized by active disturbance rejection control to achieve the fault tolerance of sensor. Finally, the effectiveness of the proposed method is validated by the simulation results of the three-tank system.
KW - Fault tolerant control
KW - active disturbance rejection control
KW - radial basis function neural network
UR - http://www.scopus.com/inward/record.url?scp=85032221138&partnerID=8YFLogxK
U2 - 10.23919/ChiCC.2017.8028513
DO - 10.23919/ChiCC.2017.8028513
M3 - Conference contribution
AN - SCOPUS:85032221138
T3 - Chinese Control Conference, CCC
SP - 7321
EP - 7326
BT - Proceedings of the 36th Chinese Control Conference, CCC 2017
A2 - Liu, Tao
A2 - Zhao, Qianchuan
PB - IEEE Computer Society
T2 - 36th Chinese Control Conference, CCC 2017
Y2 - 26 July 2017 through 28 July 2017
ER -