Nonlinear Optimal Position Control with Observer for Position Tracking of Surfaced Mounded Permanent Magnet Synchronous Motors

Abstract

Previous control methods were designed based on cascade structure and consist of position and current controllers for permanent magnet-synchronous motors (PMSMs). Thus, the structures of the previous methods are necessarily complex although the stability is guaranteed. Thus, the gain tuning is difficult to obtain for the desired control performance for the PMSMs. To overcome this problem, this paper proposes a nonlinear optimal position control method with an observer to improve the position tracking performance of PMSMs. The proposed method consists of a desired state generator, controller, and nonlinear observer. The desired states and inputs are derived using the PMSM model. Then, the state feedback controller is designed based on the whole tracking error dynamics including both mechanical and electrical dynamics. The nonlinear observer is designed to estimate the velocity and load torque. The closed-loop stability is proven using the input-to-state stability. The proposed method is not designed based on the cascade structure. Furthermore, the control and observer gains are chosen using an optimal control method to obtain the desired performance for the PMSMs. This approach simplifies the design process such that the control algorithm is suitable for real-time control. The performance of the proposed method is validated via simulations and experiments.

Embedded Controller Optimization for Efficient Electric Motor Drive

“…Permanent Magnet Synchronous Motors (PMSMs) exhibit those qualities due to high power density, ease to manufacture and compact structure [1]. To enhance the efficiency of the energy conversion system and to take advantage of the intrinsic characteristics of the PMSM, researchers have extensively explored various feedback controllers, such as cascaded Proportional-Integral (PI) controllers [2], model predictive control [3], linear-quadratic regulator [4], etc. Cascaded speed and current PI controllers have been historically used since the discovery of Park and Clarke's transformations [5], and they are tuned most of the time with pole placement methods (Ackerman's formula) [2].…”

“…Model predictive control and linear quadratic regulators are optimization-based controllers that require userdefined matrices in order to introduce a trade-off between power consumption and performance in a system. In [4], these matrices are used to compute offline the gain of a state feedback controller.…”