Model predictive current controller is a popular and effective technique to provide fast dynamic response in the field of motor control. However, conventional predictive controllers are susceptible to deteriorating control performance when model mismatch exists, such as changes in motor parameters due to the temperature variations. Therefore, this article proposes a precise model-aid extended state observer (MAESO) compensation-based real-time model predictive current controller with enhanced parameter robustness performance and high bandwidth. The predictive controller is converted into the form of multiparameter quadratic programming for online solution using numerical computational method and the constraints are linearized. In addition, the disturbances estimated by MAESO are fed back to the controller in the form of parameters for cycle-by-cycle compensation without extra controller design. Comparative simulations and experiments under different operating conditions are carried out to verify the effectiveness and superiority of the proposed method.
Model predictive current controller is a popular and effective technique to provide fast dynamic response in the field of motor control. However, conventional predictive controllers are susceptible to deteriorating control performance when model mismatch exists, such as changes in motor parameters due to the temperature variations. Therefore, this article proposes a precise model-aid extended state observer (MAESO) compensation-based real-time model predictive current controller with enhanced parameter robustness performance and high bandwidth. The predictive controller is converted into the form of multiparameter quadratic programming for online solution using numerical computational method and the constraints are linearized. In addition, the disturbances estimated by MAESO are fed back to the controller in the form of parameters for cycle-by-cycle compensation without extra controller design. Comparative simulations and experiments under different operating conditions are carried out to verify the effectiveness and superiority of the proposed method.
Model Predictive Current Control With Model-Aid Extended State Observer Compensation for PMSM Drive
Liu H.;Buccella C.;Cecati C.
2023-01-01
Abstract
Model predictive current controller is a popular and effective technique to provide fast dynamic response in the field of motor control. However, conventional predictive controllers are susceptible to deteriorating control performance when model mismatch exists, such as changes in motor parameters due to the temperature variations. Therefore, this article proposes a precise model-aid extended state observer (MAESO) compensation-based real-time model predictive current controller with enhanced parameter robustness performance and high bandwidth. The predictive controller is converted into the form of multiparameter quadratic programming for online solution using numerical computational method and the constraints are linearized. In addition, the disturbances estimated by MAESO are fed back to the controller in the form of parameters for cycle-by-cycle compensation without extra controller design. Comparative simulations and experiments under different operating conditions are carried out to verify the effectiveness and superiority of the proposed method.Pubblicazioni consigliate
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