Applications of Particle Swarm Optimization Algorithms in Control of Induction Motor Drives

Sang Ho Dang

Doctoral thesis

Abstract:

Induction motors, as well as electrical drives, are widely used in industry applications and consume a large number of electrical energy in the world. Energy saving, torque fast response and speed accuracy are main area in controlling induction motors. During last years, control methods have been developed to get these goals. Among these control methods, vector control (VC) is more and more popular because of high performance, energy saving, controlled acceleration, etc. However, in controlling AC machine drive by using VC, the motor speed is required. Together with the development of semiconductor technologies and digital signal processing (DSP), software instruments have been used to estimate speed, reducing hardware complexity and cost of a mechanical speed sensor. However, due to the nonlinearity, high order and multivariable properties of induction motor dynamics, the development of advanced induction motor control is still a challenging task. In this research proposal, basic description of the torque and flux control, as well as the theory and application of Particle Swarm Optimization (PSO) algorithms are reviewed in details. From that, a PSO algorithm for speed control is proposed to implement the pulse width modulation with a constant switching frequency. In addition, the sliding mode observer for speed estimation is investigated. The parameter sensitivity of the observer and controller are analyzed. Furthermore, the robustness of control and observer algorithms are also proved by Lyapunov’s criterion. Simulation models and control structures in MATLAB – Simulink environment are developed to verify the performance of the proposed algorithms. Finally, the experimental work in an induction motor drive controlled by eZdspTMF28335 is presented to compare with theoretical assumptions and simulation results.

Abstract:

Induction motors, as well as electrical drives, are widely used in industry applications and consume a large number of electrical energy in the world. Energy saving, torque fast response and speed accuracy are main area in controlling induction motors. During last years, control methods have been developed to get these goals. Among these control methods, vector control (VC) is more and more popular because of high performance, energy saving, controlled acceleration, etc. However, in controlling AC machine drive by using VC, the motor speed is required. Together with the development of semiconductor technologies and digital signal processing (DSP), software instruments have been used to estimate speed, reducing hardware complexity and cost of a mechanical speed sensor. However, due to the nonlinearity, high order and multivariable properties of induction motor dynamics, the development of advanced induction motor control is still a challenging task. In this research proposal, basic description of the torque and flux control, as well as the theory and application of Particle Swarm Optimization (PSO) algorithms are reviewed in details. From that, a PSO algorithm for speed control is proposed to implement the pulse width modulation with a constant switching frequency. In addition, the sliding mode observer for speed estimation is investigated. The parameter sensitivity of the observer and controller are analyzed. Furthermore, the robustness of control and observer algorithms are also proved by Lyapunov’s criterion. Simulation models and control structures in MATLAB – Simulink environment are developed to verify the performance of the proposed algorithms. Finally, the experimental work in an induction motor drive controlled by eZdspTMF28335 is presented to compare with theoretical assumptions and simulation results.

Keywords

Induction motor, particle swarm optimization, vector control, stator resistance estimation, rotor resistance estimation, sensorless control.

Keywords

Induction motor, particle swarm optimization, vector control, stator resistance estimation, rotor resistance estimation, sensorless control.

Language used: English

Date on which the thesis was submitted / produced: 2. 7. 2020

Thesis defence

• Date of defence: 8. 12. 2020
• Supervisor: Pavel Brandštetter
• Reader: Petr Palacký, Ondřej Frančík, Pavol Fedor

VSB – Technical University of Ostrava

Fakulta elektrotechniky a informatiky

Doctoral programme / field:
Elektrotechnika / Elektrické stroje, přístroje a pohony