Robust fault-tolerant control for uncertain robot manipulators based on adaptive quasi-continuous high-order sliding mode and neural network

Abstract

This paper investigates a robust fault-tolerant control scheme for uncertain robot manipulators. The proposed scheme is designed via active fault-tolerant control method by combining a fault estimation scheme with a novel robust adaptive quasi-continuous second-order sliding mode (AQC2S) controller, so as to accommodate not only system failures but also uncertainties. First, a neural network based fault estimation is designed to online approximate the unknown uncertainties and faults. The estimated uncertainty and fault information are then used to compensate in advance for the effects of uncertainties in fault-free operation and both uncertainties and faults in fault operation. To eliminate the neural network compensation error, QC2S with adaptation gain, named as adaptive QC2S (AQC2S), is proposed. By integrating the advantages of the neural network observer and the AQC2S controller, the integrated scheme has a good capability to accommodate both the uncertainties and faults with chattering-free, higher position tracking accuracy, and no requirement of prior knowledge of the fault information. The stability and convergence of the proposed fault-tolerant control system is proved theoretically. Simulation results for a PUMA560 robot demonstrate the effectiveness of the proposed algorithm.

Keywords

Robot manipulators fault diagnosis fault-tolerant control neural network sliding mode control

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