Predictive Variable Gain Iterative Learning Control for PMSM

A predictive variable gain strategy in iterative learning control (ILC) is introduced. Predictive variable gain iterative learning control is constructed to improve the performance of trajectory tracking. A scheme based on predictive variable gain iterative learning control for eliminating undesirable vibrations of PMSM system is proposed. The basic idea is that undesirable vibrations of PMSM system are eliminated from two aspects of iterative domain and time domain. The predictive method is utilized to determine the learning gain in the ILC algorithm. Compression mapping principle is used to prove the convergence of the algorithm. Simulation results demonstrate that the predictive variable gain is superior to constant gain and other variable gains

Coevolutionary particle swarm optimization using AIS and its application in multiparameter estimation of PMSM

In this paper, a coevolutionary particle-swarm-optimization (PSO) algorithm associating with the artificial immune principle is proposed. In the proposed algorithm, the whole population is divided into two kinds of subpopulations consisting of one elite subpopulation and several normal subpopulations. The best individual of each normal subpopulation will be memorized into the elite subpopulation during the evolution process. A hybrid method, which creates new individuals by using three different operators, is presented to ensure the diversity of all the subpopulations. Furthermore, a simple adaptive wavelet learning operator is utilized for accelerating the convergence speed of the pbest particles. The improved immune-clonal-selection operator is employed for optimizing the elite subpopulation, while the migration scheme is employed for the information exchange between elite subpopulation and normal subpopulations. The performance of the proposed algorithm is verified by testing on a suite of standard benchmark functions, which shows faster convergence and global search ability. Its performance is further evaluated by its application to multiparameter estimation of permanent-magnet synchronous machines, which shows that its performance significantly outperforms existing PSOs. The proposed algorithm can estimate the machine dq-axis inductances, stator winding resistance, and rotor flux linkage simultaneously. © 2013 IEEE

Coevolutionary Particle Swarm Optimization Using AIS and its Application in Multiparameter Estimation of PMSM

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this paper, a coevolutionary particle-swarm-optimization (PSO) algorithm associating with the artificial immune principle is proposed. In the proposed algorithm, the whole population is divided into two kinds of subpopulations consisting of one elite subpopulation and several normal subpopulations. The best individual of each normal subpopulation will be memorized into the elite subpopulation during the evolution process. A hybrid method, which creates new individuals by using three different operators, is presented to ensure the diversity of all the subpopulations. Furthermore, a simple adaptive wavelet learning operator is utilized for accelerating the convergence speed of the pbest particles. The improved immune-clonal-selection operator is employed for optimizing the elite subpopulation, while the migration scheme is employed for the information exchange between elite subpopulation and normal subpopulations. The performance of the proposed algorithm is verified by testing on a suite of standard benchmark functions, which shows faster convergence and global search ability. Its performance is further evaluated by its application to multiparameter estimation of permanent-magnet synchronous machines, which shows that its performance significantly outperforms existing PSOs. The proposed algorithm can estimate the machine dq-axis inductances, stator winding resistance, and rotor flux linkage simultaneously. © 2013 IEEE