This note investigates process fault accommodation in a class of nonlinear continuous-time systems. A new fault estimation module, based on an adaptive estimator, is first proposed. The fault tolerant controller is constructed to compensate for the effect of the faults by stabilizing the closed-loop system. A flexible joint robotic example is given to illustrate the efficiency of the proposed approach. Index Terms-Active fault-tolerant control (FTC), adaptive estimator, fault estimation, nonlinear continuous-time systems.

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In this paper, a dynamic surface control (DSC) scheme is proposed for a class of uncertain strict-feedback nonlinear systems in the presence of input saturation and unknown external disturbance. The radial basis function neural network (RBFNN) is employed to approximate the unknown system function. To efficiently tackle the unknown external disturbance, a nonlinear disturbance observer (NDO) is developed. The developed NDO can relax the known boundary requirement of the unknown disturbance and can guarantee the disturbance estimation error converge to a bounded compact set. Using NDO and RBFNN, the DSC scheme is developed for uncertain nonlinear systems based on a backstepping method. Using a DSC technique, the problem of explosion of complexity inherent in the conventional backstepping method is avoided, which is specially important for designs using neural network approximations. Under the proposed DSC scheme, the ultimately bounded convergence of all closed-loop signals is guaranteed via Lyapunov analysis. Simulation results are given to show the effectiveness of the proposed DSC design using NDO and RBFNN.

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