Self-Evolving Recurrent Chebyshev Fuzzy Neural Sliding Mode Control for Active Power Filter

A self-evolving recurrent Chebyshev fuzzy neural network (SERCFNN) approximator based on a fractional order sliding mode controller (FOSMC) is developed for an active power filter to suppress harmonic distortions. The self-evolving algorithm, which incorporates the structure learning with parameter learning, is able to dynamically adjust the number of fuzzy rules and the shape of fuzzy partitions. The consequent part of the proposed SERCFNN combines with Chebyshev polynomials to expand the dimensionality of the input. For relaxing the requirement of the parametric and functional certainty, a SERCFNN-based uncertainty approximator is utilized to dynamically approximate the compound unknown function, yielding an approximator-based FOSMC to tolerate extensive uncertainties. The approximator-based control law and parameter updating laws are obtained from the Lyapunov stability theory, which guarantee the designed control system is asymptotically stable. The control algorithm is implemented in a dSPACE-based experimental system to validate its feasibility, and the hardware experimental results confirm its superiority in harmonic compensation regardless of load disturbances.