Hybrid Intelligent Control Using Hippocampus-Based Fuzzy Neural Networks for Active Power Filter

To tackle harmonic issue in the power grid, an intelligent control scheme consists of fast integral terminal sliding-mode control and hippocampus-based fuzzy neural network (HBFNN) is proposed and applied to active power filter (APF) to eliminate harmonics in this article. At first, the mathematical model of APF under the influence of external interference and parameter perturbation is derived in accordance with its topological structure. Afterwards, a fast integral terminal sliding-mode controller is developed for APF. The system stability is subsequently proved according to Lyapunov stability criterion, and the finite-time convergence of tracking error is certified. In addition, based on the biological structure and characteristics of the hippocampus, an innovative HBFNN is constructed to approximate the controller. Furthermore, the adaptive laws derived by the Lyapunov's theorem can realize the automatic adjustment of parameters and ensure closed-loop stability. Ultimately, relevant simulation and experimental results corroborate the availability and superiority of the designed intelligent control strategy in harmonic elimination.