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Model-Free Adaptive Sliding Mode Control for Discrete-Time Nonlinear Systems With Sensor Fault and Prescribed Performance

This paper concentrates on the issue of a model-free adaptive sliding mode control approach for a family of discrete-time single-input and single-output (SISO) nonlinear systems with sensor fault and prescribed performance. First of all, we set out to build an approximator that is based on radial basis function neural network to estimate the sensor fault offline, and for the purpose of restricting the tracking error in a prescribed region when the sensor fault occurs, the prescribed performance control (PPC) method is adopted. Then, we consider sliding mode control strategy to achieve the stability and ensure the convergence of tracking error within PPC scheme. We have to point out that all of these works above are carried in model-free adaptive control (MFAC) framework. Finally, a simulation example result is given to validate the effectiveness of the algorithm proposed in this article.