Intelligent Terminal Sliding Mode Control of Active Power Filters by Self-Evolving Emotional Neural Network

In this article, a control system based on evolutionary emotional neural network is proposed for active power filters (APFs) to improve power quality. First, the dynamic model of the APF containing external disturbances and component parameter perturbations is introduced. The global fast terminal sliding mode (GFTSM) control method is proposed for the APF and its finite-time convergence and global robustness are demonstrated. In addition, an emotional neural network based on Hermite orthogonal polynomials as the activation function is constructed and combined with an evolutionary mechanism to form a self-evolving emotional neural network (SEENN). Then, a model-free control system based on SEENN is designed to address the model dependence of the GFTSM controller design. The parameter update law is designed under the Lyapunov framework to ensure stability. Finally, the results of prototype experiments show the excellent performance of the proposed control algorithm.