Global Identification of Electrical and Mechanical Parameters in PMSM Drive Based on Dynamic Self-Learning PSO

Abstract

A global parameter estimation method for a PMSM drive system is proposed, where the electrical parameters, mechanical parameters and voltage-source-inverter (VSI) nonlinearity are regarded as a whole and parameter estimation is formulated as a single parameter optimization model. A dynamic learning estimator is proposed for tracking the electrical parameters, mechanical parameters and VSI of PMSM drive by using dynamic self learning particle swarm optimization (DSLPSO). In DSLPSO, a novel movement modification equation with dynamic exemplar learning strategy is designed to ensure its diversity and achieve a reasonable tradeoff between the exploitation and exploration during the search process.Moreover, a nonlinear multi-scale based interactive learning operator is introduced for accelerating the convergence speed of the Pbest particles; meanwhile a dynamic opposition-based learning (OBL) strategy is designed to facilitate the gBest particle to explore a potentially better region. The proposed algorithm is applied to parameter estimation for a PMSM drive system. The results show that the proposed method has better performance in tracking the variation of electrical parameters, and estimating the immeasurable mechanical parameters and the VSI disturbance voltage simultaneously.

Fast Terminal Sliding Mode Control of Permanent Magnet In-Wheel Motor Based on a Fuzzy Controller

“…On the premise of fewer influences on control accuracy, a simplified mathematical models can be established under the following assumptions [34]: (1) the magnetic circuit is unsaturated; (2) porcelain and eddy current loss are ignored; (3) there is no damping winding on the rotor; (4) the distribution of the magnetic field air gap is a sine wave; (5) the permanent magnets of PMIWMs are surface-mounted. On the above assumptions, the mathematical model of PMIWM can be obtained with Equation (1) [35]:…”

“…The control block diagram of the FTSMC system using the proposed fuzzy rules is presented in Figure 4 [35]. Using a fuzzy controller to adjust the upper bound of ( ) K t in the control system in real time is another contribution of this paper.…”

“…The control block diagram of the FTSMC system using the proposed fuzzy rules is presented in Figure 4 [35].…”

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

“…where t denotes an iterative algebra; c 1 and c 2 represent a learning factor, generally c 1 = c 2 = 2, and rand( ) refers to a random number between 0 and 1 independent of each other. There is a maximum speed of particle flight [24][25][26]. The maximum speed is used instead when the calculated speed surmounts this maximum.…”

“…Calculate the initial temperature T emp0 if t iter = 0, so that the initial acceptance rate of various particles in the initial iteration is large (∑ [exp(−Δ / 0 ) > rand( )] > (0.95 ). At least 95% of the particles pass (24) and (25) can be accepted as a new location after iteration).…”