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Redundancy based Neuroadaptive Fault-tolerant Control of Nonlinear Uncertain Systems under Actuation Failures

In this paper, a redundancy based neuroadaptive fault-tolerant control law is proposed for a class of multi-input multi-output (MIMO) nonlinear uncertain systems in the presence of modeling uncertainties and actuation failures as well as unexpected external disturbances. By employing weighted distribution technique, the proposed control approach is able to deal with the situation that some of the actuators completely or partially fail to work, where a spacial matrix decomposition strategy with the aid of neural networks (NN) is used to establish the control design and stability analysis without the need for all system nonlinearities and fault detection/diagnostic. It is shown that all the signals of uncertain systems are uniformly ultimately bounded (UUB). A numerical simulation result is shown to verify the effectiveness of the proposed scheme.