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Abstract
The dissertation addresses the challenges faced by rare-earth-free motor drives, particularly in the context of electrified transportation, industrial applications, and home appliances. These motor drives offer advantages such as a simple and rigid structure, independence from rare-earth permanent magnet materials, and an extended-speed constant-power range. However, they also suffer from high nonlinearities, which lead to issues like high current ripples and torque ripples. Furthermore, the integration of the Internet of Things (IoT) introduces additional intelligence to motor drives but also increases their vulnerability to cyber attacks.
First of all, through Ansys simulations, Chapter 2 analyzes the characteristics of SRMs and the mutually coupled SRMs (MCSRMs) and develops models for these machines using Look-Up tables to account for nonlinearities. The mathematical model of IMs is also established for control purposes in subsequent chapters. Then, in Chapter 3, a sliding mode current controller (SMC) is then developed to reduce current ripples in MCSRMs while maintaining a fixed switching frequency and high system dynamics. Additionally, comparative studies of various model-based predictive current control techniques are explored, which show promise in dealing with nonlinearities and uncertainties. Simulations are conducted to demonstrate the effectiveness of these approaches. In Chapter 4, an offline torque ripple reduction method based on linear programming is employed for MCSRMs. Additionally, an online torque ripple reduction technique is proposed for SRMs, which intelligently regulates torque ripples using the Minimum Principle. Simulation results validate the effectiveness of both methods. Chapter 5 addresses vulnerability assessment in an IM-based control system by mathematically describing potential cyber attacks and proposing five evaluation matrices to quantify the impact caused by such attacks. The results emphasize the significance and motivation of the research. Chapter 6 delves into the robust model predictive control (RMPC) technique. A generalized structure for the robust control approach is proposed, comprising a state observer, an incremental model predictive controller in the disturbance-free system, and a feedback controller to correct tracking errors. Steady-state analysis is provided and verified using MATLAB/SIMULINK, and experimental results are presented to demonstrate the effectiveness of the RMPC approach, even under specially designed cyber attacks.
First of all, through Ansys simulations, Chapter 2 analyzes the characteristics of SRMs and the mutually coupled SRMs (MCSRMs) and develops models for these machines using Look-Up tables to account for nonlinearities. The mathematical model of IMs is also established for control purposes in subsequent chapters. Then, in Chapter 3, a sliding mode current controller (SMC) is then developed to reduce current ripples in MCSRMs while maintaining a fixed switching frequency and high system dynamics. Additionally, comparative studies of various model-based predictive current control techniques are explored, which show promise in dealing with nonlinearities and uncertainties. Simulations are conducted to demonstrate the effectiveness of these approaches. In Chapter 4, an offline torque ripple reduction method based on linear programming is employed for MCSRMs. Additionally, an online torque ripple reduction technique is proposed for SRMs, which intelligently regulates torque ripples using the Minimum Principle. Simulation results validate the effectiveness of both methods. Chapter 5 addresses vulnerability assessment in an IM-based control system by mathematically describing potential cyber attacks and proposing five evaluation matrices to quantify the impact caused by such attacks. The results emphasize the significance and motivation of the research. Chapter 6 delves into the robust model predictive control (RMPC) technique. A generalized structure for the robust control approach is proposed, comprising a state observer, an incremental model predictive controller in the disturbance-free system, and a feedback controller to correct tracking errors. Steady-state analysis is provided and verified using MATLAB/SIMULINK, and experimental results are presented to demonstrate the effectiveness of the RMPC approach, even under specially designed cyber attacks.