As dynamic systems encounter a greater variety of unexpected failures, solving the control problems of such systems is a grand challenge for control engineers. In this research work, we investigate the fault tolerant control problem and propose an intelligent on-line sliding mode control strategy using artificial neural networks to handle the desired trajectories tracking problem for systems suffering from catastrophic faults or incipient failures. The approach is to continuously monitor the system performance and identify the system's current state 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 by adding a corrective sliding mode control signal to confine the system performance within a boundary layer. Meanwhile, an artificial neural network is initialized and compensates for the unknown fault dynamics on-line. When the on-line learning process converges, the control input is tuned again by using the output of the identification model and a new least upper bound for the remaining uncertainty is estimated to further reduce the tracking error. The simulation results show a significant improvement in trajectory following performance based upon the proposed intelligent on-line control strategy.
The growing demand in system reliability and survivability under failures has urged ever-increasing research effort on the development of fault diagnosis and accommodation. In this paper, the on-line fault tolerant control problem for dynamic systems under unanticipated failures is investigated from a realistic point of view without any specific assumption on the type of system dynamical structure or failure scenarios. The sufficient conditions for system on-line stability under catastrophic failures have been derived using the discrete-time Lyapunov stability theory. Based upon the existing control theory and the modern computational intelligence techniques, an on-line fault accommodation control strategy is proposed to deal with the desired trajectory-tracking problems for systems suffering from various unknown and unanticipated catastrophic component failures. Theoretical analysis indicates that the control problem of interest can be solved on-line without a complete realization of the unknown failure dynamics provided an on-line estimator satisfies certain conditions. Through the on-line estimator, effective control signals to accommodate the dynamic failures can be computed using only the partially available information of the faults. Several on-line simulation studies have been presented to demonstrate the effectiveness of the proposed strategy. To investigate the feasibility of using the developed technique for unanticipated fault accommodation in hardware under the real-time environment, an on-line fault tolerant control test bed has been constructed to validate the proposed technology. Both on-line simulations and the real-time experiment show encouraging results and promising futures of on-line real-time fault tolerant control based solely upon insufficient information of the system dynamics and the failure dynamics.
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