Neural Network-Based Adaptive Sliding Mode Control for T-S Fuzzy Fractional Order Systems

This brief investigates the adaptive sliding mode control based on the radial basis function (RBF) neural network for T-S fuzzy fractional order systems. The RBF neural network is used to approximate the nonlinearities and external disturbances. Then, the switching control term is represented as a proportional integral control format to reduce the chattering phenomenon. The conditions of the sliding mode controller are given to ensure the stability of the control system. Finally, the efficiency of the conditions is demonstrated by a permanent magnet synchronous motor (PMSM) example, i.e., it shows that the designed controller can stabilize T-S fuzzy fractional order systems.