A Hybrid Wavelet Fuzzy Neural Network and Switching Particle Swarm Optimization Algorithm for AC Servo System

Abstract

A hybrid computational intelligent approach which combines wavelet fuzzy neural network (WFNN) with switching particle swarm optimization (SPSO) algorithm is proposed to control the nonlinearity, wide variation in loads, time variation, and uncertain disturbance of the high-power AC servo system. The WFNN method integrated wavelet transforms with fuzzy rules and is proposed to achieve precise positioning control of the AC servo system. As the WFNN controller, the back-propagation method is used for the online learning algorithm. Moreover, the SPSO is proposed to adapt the learning rates of the WFNN online, where the velocity updating equation is according to a Markov chain, which makes it easy to jump the local minimum, and acceleration coefficients are dependent on mode switching. Furthermore, the stability of the closed loop system is guaranteed by using the Lyapunov method. The results of the simulation and the prototype test prove that the proposed approach can improve the steady-state performance and possess strong robustness to both parameter perturbation and load disturbance.

Enhancing Particle Swarm Optimization Performance Through CUDA and Tree Reduction Algorithm

“…Optimization techniques are crucial in various domains for finding optimal solutions to complex problems. However, Particle Swarm Optimization, a widely used metaheuristic algorithm, has demonstrated limitations in terms of convergence speed and local optimization performance [1] [2]. As a result, researchers have turned to parallel computing techniques like Compute Unified Device Architecture (CUDA) a parallel computing platform and application programming interface (API) developed by NVIDIA, to enhance the performance of PSO by implementing it on a parallel architecture.…”

Passivity‐Based Control for Rocket Launcher Position Servo System Based on ADRC Optimized by IPSO‐BP Algorithm

“…However, on the technology shipborne rocket launchers are not equivalent to ordinary rocket launchers; the stability of firing accuracy is influenced not only by the nonlinearity and strong uncertainty of rocket launcher position servo system but also by the ship swing [4,5]. In order to improve the accuracy and robustness of the rocket launcher, some well performance and high precision alternative current servo systems are studied with intelligent control algorithms in many articles [6][7][8].…”

“…However, the PSO algorithm has some defects, such as slow convergence speed and easily falling into local minima. The back propagation (BP) algorithm, on the contrary, has a strong ability to find local optimal solution; but its ability to find the global optimal solution is very weak [7,30]. Taking advantages of PSO and BP, a PSO-BP controller is designed to further enhance the robustness and adaptability of traditional ADRC.…”

Application of Simulated Annealing Particle Swarm Optimization Based on Correlation in Parameter Identification of Induction Motor

“…The correlation algorithm is used in this paper to extract the amplitude and phase of the stator current fundamental signal, and the harmonic components are filtered out to emphasize the fundamental signal given the characteristics of the stator voltage and current fundamental frequency equal to construct the same frequency with the stator voltage reference signal [10][11][12]. Then, to identify the parameters of the dynamic mathematical model of asynchronous motor, a novel optimization algorithm incorporating simulated annealing with the advantages of particle swarm optimization is used [13][14][15].…”