Improved Model Predictive Current Control for SPMSM Drives With Parameter Mismatch

Model predictive current control (MPCC) can predict future motor behavior according to a motor model. In practice, however, motor parameters will vary at run time, and the parameter mismatch disturbances caused by the variation in motor parameters will deteriorate the MPCC performance. To suppress the parameter mismatch disturbances effectively, this paper proposes a modified MPCC with a current variation update mechanism. In contrast with the traditional current prediction equation that contains crude model parameters, the modified current prediction equation contains only measured information, taking advantage of the proposed current variation update mechanism, which can update the modified prediction equation within each sampling period. A simulation established by MATLAB software indicates that the proposed method can effectively suppress the parameter mismatch disturbances. Experiments are carried out to verify the correctness of the proposed method

A New Position and Speed Estimation Scheme for Position Control of PMSM Drives Using Low-Resolution Position Sensors

A new position control method for permanent magnet synchronous motor (PMSM) drive with a low-resolution encoder is proposed in this paper. Three binary Hall position sensors are utilized to realize a moderate-performance position control system for the consideration of economy and simplicity in servo application. Compared with sensorless control, the usage of binary Hall position sensors is a guarantee of both control performance and low cost. However, the low resolution of the Hall sensor will heavily deteriorate the accuracy of the position and speed calculation. Such drawback can be effectively minimized by using appropriate position and speed estimation schemes. With the help of polynomial fitting and state observer techniques, a solution is provided to realize semi-closed loop control by treating the position and speed estimators as separate systems. The performance can be improved (1) by proposing a polynomial fitting scheme with least squares method, high-resolution rotor-position predictor can be derived by fitting the predefined position data from binary Hall position sensors in a linear or quadratic manner; (2) by adopting the dual-sampling-rate observer, instantaneous speed can be estimated at each control cycle and the estimation error is corrected once a new measurement form the Hall arrives. Furthermore, a nonlinear position control algorithm is introduced to increase standstill stability. Extensive experimental results are given to demonstrate the feasibility of the proposed method and its superiority over conventional methods

An eDrive-Based Estimation Method of the Laundry Unbalance and Laundry Inertia for Washing Machine Applications

The estimation of the laundry unbalance and laundry inertia is fundamental in washing machine applications. On the one hand, the estimation and management of the laundry unbalance play a pivotal role in reducing mechanical stress and noise during the spinning phase. On the other hand, the laundry inertia’s estimation, performed at the beginning of the washing cycle, allows for the determination of the proper amounts of water and detergent, the water temperature, and the tumbling time. In this way, good washing performance is obtained, avoiding the waste of energy and resources. Moreover, at the end of the washing cycle, the laundry inertia’s accurate estimation is needed to properly manage the spinning phase. With the aim of optimizing the washing performance, this paper proposes a novel method to estimate the laundry unbalance and laundry inertia. The proposed approach does not require additional sensors, since it uses the already implemented motor control scheme, enhanced by a dedicated position-tracking observer. Experimental results have been carried out on a commercial horizontal-axis direct-drive washer, demonstrating the validity of the proposed solution