Fault tolerant control of multivariable processes using auto-tuning PID controller.
Fault tolerant control of dynamic processes is investigated in this paper using an auto-tuning PID controller. A fault tolerant control scheme is proposed composing an auto-tuning PID controller based on an adaptive neural network model. The model is trained online using the extended Kalman filter (EKF) algorithm to learn system post-fault dynamics. Based on this model, the PID controller adjusts its parameters to compensate the effects of the faults, so that the control performance is recovered from degradation. The auto-tuning algorithm for the PID controller is derived with the Lyapunov method and therefore, the model predicted tracking error is guaranteed to converge asymptotically. The method is applied to a simulated two-input two-output continuous stirred tank reactor (CSTR) with various faults, which demonstrate the applicability of the developed scheme to industrial processes.
Yu
Ding-Li
DL
Control Systems Research Group, School of Engineering, Liverpool John Moores University, Liverpool, L3 3AF, UK.
Chang
T K
TK
Yu
Ding-Wen
DW
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D004868:Equipment Failure; D005246:Feedback; D008962:Models, Theoretical; D015999:Multivariate Analysis; D013598:Systems Theory; D014783:Viscosity