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.

Spatial High Precision Scanning Control Method Based on Feedforward Closed-Loop Model Reference Adaptive

The time-modulated Fourier transform spectrometer realizes spectrum detection by scanning the optical path of the corner mirror. During the scanning process, the servo system is required to have high-precision and low-speed characteristics. Aiming at the fluctuation of scanning speed caused by spatial micro-vibration during scanning, a closed-loop model reference adaptive control algorithm based on feedforward is studied. The permanent magnet synchronous linear motor is used to drive the angle mirror to move back and forth along the guide rail to achieve large optical path and high-precision scanning with the maximum optical path difference of ± 34cm, the speed stability ≥ 99%.

Model Reference Control Applied on the Velocity Tracking of a Welding Robot

Robotics has been expanding over last decades, employed mainly to the activities that are most harmful to human beings. Considering that welding is one of the most risky activities in industries, studies and researches in the process automation are quite important. In this context, this work contributes to the control of the velocity tracking of the displacement of a linear welding robot. The mathematical modelling of the robot is presented, and the chosen control technique is Model Reference Control, which allows project controller based on the desired behaviour for the robot. To corroborate controller effectiveness, simulation and experimental results are presented and discussed, proving that proposed technique is adequate to control the robot velocity.

Model Reference Adaptive Control of Solar Photovoltaic Systems: Application to a Water Desalination System

The major problem of the industrial sectors is to efficiently supply their energy requirement. Renewable energy sources, in particular solar energy, are intermittently accessible widely around the world. Photovoltaics (PV) technology converts sunlight to electricity. In this work, we present a contribution dealing with a new mathematic development of tracking control technique based on Variable Structure Model Reference Adaptive Following (VSMRAF) control applied to systems coupled with solar sources. This control technique requires the system to follow a reference model (the solar radiation model) by adjusting its dynamic and ensuring the minimal value of error between the plant dynamics and that of the reference solar radiation model. This chapter provides a new theoretical analysis validated by simulation and experimental results to assure optimum operating conditions for solar photovoltaic systems.

Model Reference Adaptive Control and Fuzzy Neural Network Synchronous Motion Compensator for Gantry Robots

A model reference adaptive control and fuzzy neural network (FNN) synchronous motion compensator for a gantry robot is presented in this paper. This paper proposes the development and application of gantry robots with MRAC and FNN online compensators. First, we propose a model reference adaptive controller (MRAC) under the cascade control method to make the reference model close to the real model and reduce tracking errors for the single axis. Then, a fuzzy neural network compensator for the gantry robot is proposed to compensate for the synchronous errors between the dual servo motors to improve precise movement. In addition, an online parameter training method is proposed to adjust the parameters of the FNN. Finally, the experimental results show that the proposed method improves the synchronous errors of the gantry robot and demonstrates the methodology in this paper. This study also successfully integrates the hardware and successfully verifies the proposed methods.