کنفرانس بین المللی ماشین و محرکه های الکتریکی

صفحه اصلی / The 5th International Conference on Electrical Machines and Drives

An Integrated Approach for High-Performance Speed Tracking of Permanent Magnet Synchronous Motors under Unknown Load Torque

نویسندگان :

Roya Delgosha¹ Reza Delgosha²

1- دانشگاه صنعتی خواجه نصیرالدین طوسی 2- دانشگاه صنعتی شریف

کلمات کلیدی :

Permanent Magnet Synchronous Motor (PMSM)،State-Dependent Riccati Equation (SDRE)،Extended Kalman Filter (EKF)،Nonlinear Control،Optimal Tracking،Load Torque Estimation

چکیده :

Permanent Magnet Synchronous Motors (PMSMs) are prevalent today due to their advantages, including high efficiency and power. They are the primary motor in most Electric Vehicles (EVs), providing precise, rapid movements for industrial robots and CNC machines, and they act as generators in wind turbines. Therefore, identifying them and designing efficient controllers are crucial, resulting in higher performance and benefiting their capacities. According to the nonlinearities of the PMSM mathematical model and its sensitivity to external disturbances, designing a controller for this type of motor is challenging, and it degrades the performance of conventional linear controllers such as PI controllers. To address this problem, this paper evaluates the performance of an integrated control strategy that combines an advanced suboptimal tracker with an Extended Kalman Filter (EKF). The controller is based on a novel State-Dependent Riccati Equation (SDRE) technique that overcomes the limitations of traditional SDRE regulators by transforming the tracking problem into an equivalent regulation problem. Applying this unique formulation, the tracking of non-decaying reference trajectories will be possible using a discounted cost function, and controller gains are generated from a single Riccati equation. This methodology avoids the need to solve the complex Hamilton-Jacobi-Bellman equation. Concurrently, the EKF can estimate the unknown load torque online and is designed to reject disturbances proactively. Combining EKF and the novel SDRE method results in a nonlinear control structure that enhances tracking accuracy and system robustness.