Nonsingular Terminal Sliding Mode Control Based on Binary Particle Swarm Optimization for DC–AC Converters

Abstract

This paper proposes an improved feedback algorithm by binary particle swarm optimization (BPSO)-based nonsingular terminal sliding mode control (NTSMC) for DC–AC converters. The NTSMC can create limited system state convergence time and allow singularity avoidance. The BPSO is capable of finding the global best solution in real-world application, thus optimizing NTSMC parameters during digital implementation. The association of NTSMC and BPSO extends the design of classical terminal sliding mode to converge to non-singular points more quickly and introduce optimal methodology to avoid falling into local extremum and low convergence precision. Simulation results show that the improved technique can achieve low total harmonic distortion (THD) and fast transients with both plant parameter variations and sudden step load changes. Experimental results of a DC–AC converter prototype controlled by an algorithm based on digital signal processing have been shown to confirm mathematical analysis and enhanced performance under transient and steady-state load conditions. Since the improved DC–AC converter system has significant advantages in tracking accuracy and solution quality over classical terminal sliding mode DC–AC converter systems, this paper will be applicable to designers of relevant robust control and optimal control technique.

Predictions of the Key Operating Parameters in Waste Incineration Using Big Data and a Multiverse Optimizer Deep Learning Model

“…The training data and the iteration period are also guaranteed to be constant. The Particle Swarm Optimization algorithm (PSO) [40], Beetle Antennae Search algorithm (BAS) [41], Sparrow Search algorithm (SSA) [42], and MVO-optimized SVM are used with the same image feature parameters and time delay compensation, respectively, and the prediction effect is finally observed using the same set of test data. Since optimization algorithms are generally strongly stochastic, ten sets of tests are performed for each algorithm, and the Wilcoxon sign rank test [43] is performed between the RMSEs of the prediction results of the three comparison algorithms and the RMSEs of the MVO, respectively, to prove the optimization effectiveness of the MVO algorithm.…”