Multiparameter Identification of Permanent Magnet Synchronous Motor Based on Model Reference Adaptive System—Simulated Annealing Particle Swarm Optimization Algorithm

The accurate identification of permanent magnet synchronous motor (PMSM) parameters is the basis for high-performance drive control. The traditional PMSM multiparameter identification method experiences problems with the uncertainty of the identification results and low identification accuracy due to the under-ranking of the mathematical model of motor control. A multiparameter identification of PMSM based on a model reference adaptive system and simulated annealing particle swarm optimization (MRAS-SAPSO) is proposed here. The algorithm first identifies the electrical parameters of the PMSM (stator winding resistance R, cross-axis inductance L, and magnetic linkage ψf) by means of the model reference adaptive system method. Second, the result is used as the initial population in particle swarm optimization identification to further optimize and identify the electrical and mechanical parameters (moment of inertia J and damping coefficient B) in the motor control system. Additionally, in order to avoid problems such as premature convergence of the particle swarm in the optimization search process, the results of the adaptive simulated annealing algorithm to optimize multiparameter identification are introduced. The simulation experiment results show that the five identification parameters obtained by the MRAS-SAPSO algorithm are highly accurate and stable, and the errors between them and the real values are below 2%. This also verifies the effectiveness and reliability of this identification method.

Comparison between PI-DTC-SPWM and fuzzy logic for a sensorless asynchronous motor drive

Currently, asynchronous cage motors are among the most commonly requested machines accentuated by their extension to the field of electric vehicles. Therefore, the development of robust and sophisticated controls for this machine is of significant interest. Artificial intelligence control techniques, such as fuzzy logic, are at the forefront of recent research. However, their design becomes much more complicated for a motor via a multilevel inverter. The main purpose of this paper is to show that it is possible to achieve fuzzy logic control of a squirrel cage asynchronous motor supplied via the usual two-level inverter. This is achieved, by adopting a DTC strategy based on a sinusoidal PWM with multilevel inverter. It employs a feedback information estimator with dual structure between the sliding mode observer at low speed and the model reference adaptive system in sliding mode at high speed. For both installations, speed is regulated using a sliding mode controller.

Sensorless Fault-Tolerant Control of Dual Three-Phase Permanent Magnet Synchronous Motor

Dual three-phase permanent magnet synchronous motors (DTPMSM) are used in the steer-by-wire system of electric vehicles that require high reliability. Multiple faults should be considered for the steering system, such as open-circuit faults and speed sensor faults. However, the current speed sensorless control methods of the dual three-phase motor are mainly derived from the promotion of the three-phase motor. They fail when an open-circuit fault occurs, leading to the failure of fault-tolerant control. Researchers have noticed this problem and proposed many methods, but they are very complicated and computationally intensive. This paper proposes one type of improved model reference adaptive system (MRAS). By adding certain fault-related restraints to the output of the adjustable model, speed sensorless control can automatically fit the open-circuit fault and estimate accurately even if an open-circuit fault occurs, which makes sure the whole system continues to operate. Simulation results are presented that contain normal operation, open-circuit fault operation, fault-tolerant control operation, and the whole process from start to fault-tolerant operation. The results show that no matter what period the motor is in, the improved speed sensor can accurately estimate the motor speed and position. The improved model reference adaptive system is significant for improving the reliability of the motor steering system and ensuring the safety of people and property.

MRAS-Based Switching Linear Feedback Strategy for Sensorless Speed Control of Induction Motor Drives

This paper presents a newly designed switching linear feedback structure of sliding mode control (SLF-SMC) plugged with an model reference adaptive system (MRAS) based sensorless field-oriented control (SFOC) for induction motor (IM). Indeed, the performance of the MRAS depends mainly on the operating point and the parametric variation of the IM. Hence, the sliding mode control (SMC) could be considered a good control alternative due to its easy implementation and robustness. Simulation and experimentation results are presented to show the superiority of the proposed SLF-SMC technique in comparison with the classical PI controller under different speed ranges and inertia conditions.

APPLICATION OF LEARNING FEED-FORWARD CONTROLLER BASED ON MODEL REFERENCE ADAPTIVE SYSTEM FOR MISSILE STABILIZATION

The article presents the results of research, analysis and how to build learning feed-forward controller based on model reference adaptive system in the remote control loop for missile stabilization. The controller structure is simple, adaptive control law applying Lyapunov stability theory fast convergence and sustainable. The simulation results have shown the advantages of using algorithm, the missile is always stable when there is a parameter change due to the effects of flight conditions.