Abstract
The speed response of the interior permanent magnet synchronous motor (IPMSM) drive at low speeds was analyzed. To eliminate the effect of external disturbance or parameter uncertainty, a nonlinear speed control loop was designed based on the sliding-mode exponential reaching law, which reduces chatter, which is the major drawback of the constant reaching law sliding-mode control technique. The proposed nonlinear speed control eliminates speed ripples at low speed under load disturbance. The problem of speed convergence at low speed is caused by electromagnetic torque ripples, which cause shaft speed oscillations that affect drive performance. The main objective of the proposed method is to change the traditional IPMSM control design by compensating with an appropriate signal along the reference current and across the output of the speed control loop. To optimize the speed tracking performance during disturbances or parametric variations, a nonlinear speed control scheme is designed that can vigorously adapt to the change in the controlled system. The comparative analysis shows that the method provides excellent transient performance (e.g., fast convergence response, less overshoot, and fast settling time) and standstill performance (e.g., reduced steady-state error) compared with conventional control methods at low speed under varying load conditions. The method is easy to implement and does not require additional computational cost. To demonstrate the effectiveness and feasibility of the design approach, a numerical analysis was conducted, and the control scheme was verified using MATLAB/Simulink considering various operating conditions.
Highlights
IntroductionInterior permanent magnet synchronous motors (IPMSMs) are extensively used in several high-performance industrial applications (e.g., electric vehicle drives, robotics, traction drives, and home appliances) owing to their significant properties, such as fast dynamics, high precision, high torque and power density, dynamic performance, low maintenance cost, and high reliability
Interior permanent magnet synchronous motors (IPMSMs) are extensively used in several high-performance industrial applications owing to their significant properties, such as fast dynamics, high precision, high torque and power density, dynamic performance, low maintenance cost, and high reliability
At low speed, because of the presence of external disturbances, such as parasitic torque ripples or parameter uncertainties, caused by imperfect machine design, the uncertain data measured from the sensor caused by noise and the effect of mechanical load or power electronic switches [3,4] cause the speed trajectory tracking performance to deteriorate and degrade the robustness
Summary
Interior permanent magnet synchronous motors (IPMSMs) are extensively used in several high-performance industrial applications (e.g., electric vehicle drives, robotics, traction drives, and home appliances) owing to their significant properties, such as fast dynamics, high precision, high torque and power density, dynamic performance, low maintenance cost, and high reliability. Several control strategies based on conventional controllers, such as proportional–integral (PI) control [5,6] have attracted attention owing to their simplicity and ease of implementation on hardware; they are highly dependent on actual drive parameters and require exact parameter values to tune the PI gain to achieve efficient closed-loop performance. In previous research [7,8,9], deadbeat control showed excellent speed tracking performance, but it depends highly on the IPMSM parameters to achieve efficient tracking performance by setting the closed-loop poles to zero. Fuzzy logic controllers [10,11] can effectively deal with drive nonlinearities and model unknown parameter uncertainties This control scheme depends highly on gains and requires extensive knowledge to choose appropriate fuzzy interference rules to achieve excellent speed tracking. Model predictive control (MPC) [12,13,14] is easy to implement and straightforward, but highly depends on the exact model of the IPMSM to predict the future control output variables that ensure adequate closed-loop control effects
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
