Double-Hidden-Layer Chebyshev Fuzzy Neural Fractional-Order Fast Terminal Sliding Mode Control With Disturbance Observers and Its Application

Based on the theory of fractional calculus, this paper establishes a fractional mathematical model for an APF system. Meanwhile, a fast terminal sliding mode control with fractional-order disturbance observer and attention-mechanism double hidden layer Chebyshev recurrent neural network (FODO-AM-DHLCRNN-FTSMC) is proposed. In the presence of external disturbances, the FODO is designed to estimate the disturbances and implement disturbance compensation in the controller. The designed AM-DHLCRNN is used to estimate the nonlinear terms in the APF mathematical model. Due to its double-hidden layer structure and the addition of Chebyshev polynomials and attention mechanisms, it can dynamically adjust the weights of different network nodes according to the input, thereby handling the input in different regions more flexibly and having better dynamic mapping ability and approximation performance. Finally, the adaptive law of neural network parameters is derived through the Lyapunov method, which proved the stability of the control system. The feasibility of the proposed method is proved through simulation and hardware experiments, showing the satisfactory harmonic suppression performance.