Intelligent on-line fault tolerant control for unanticipated catastrophic failures.
As dynamic systems become increasingly complex, experience rapidly changing environments, and encounter a greater variety of unexpected component failures, solving the control problems of such systems is a grand challenge for control engineers. Traditional control design techniques are not adequate to cope with these systems, which may suffer from unanticipated dynamic failures. In this research work, we investigate the on-line fault tolerant control problem and propose an intelligent on-line control strategy to handle the desired trajectories tracking problem for systems suffering from various unanticipated catastrophic faults. Through theoretical analysis, the sufficient condition of system stability has been derived and two different on-line control laws have been developed. The approach of the proposed intelligent control strategy is to continuously monitor the system performance and identify what the system's current state is by using a fault detection method based upon our best knowledge of the nominal system and nominal controller. Once a fault is detected, the proposed intelligent controller will adjust its control signal to compensate for the unknown system failure dynamics by using an artificial neural network as an on-line estimator to approximate the unexpected and unknown failure dynamics. The first control law is derived directly from the Lyapunov stability theory, while the second control law is derived based upon the discrete-time sliding mode control technique. Both control laws have been implemented in a variety of failure scenarios to validate the proposed intelligent control scheme. The simulation results, including a three-tank benchmark problem, comply with theoretical analysis and demonstrate a significant improvement in trajectory following performance based upon the proposed intelligent control strategy.
Yen
Gary G
GG
School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK 74078, USA.
Ho
Liang-Wei
LW
D001185:Artificial Intelligence; D003198:Computer Simulation; D004867:Equipment Design / Q000379:methods*; D004868:Equipment Failure; D019544:Equipment Failure Analysis; D004869:Equipment Safety / Q000379:methods*; D005246:Feedback; D008962:Models, Theoretical; D016571:Neural Networks, Computer