The field oriented control (FOC) strategy of the permanent magnet synchronous motor (PMSM) includes all the advantages deriving from the simplicity of using PI-type controllers, but inherently the control performances are limited due to the nonlinear model of the PMSM, the need for wide-range and high-dynamics speed and load torque control, but also due to the parametric uncertainties which occur especially as a result of the variation of the combined rotor-load moment of inertia, and of the load resistance. Based on the fractional calculus for the integration and differentiation operators, this article presents a number of fractional order (FO) controllers for the PMSM rotor speed control loops, and id and iq current control loops in the FOC-type control strategy. The main contribution consists of proposing a PMSM control structure, where the controller of the outer rotor speed control loop is of FO-sliding mode control (FO-SMC) type, and the controllers for the inner control loops of id and iq currents are of FO-synergetic type. Superior performances are obtained by using the control system proposed, even in the case of parametric variations. The performances of the proposed control system are validated both by numerical simulations and experimentally, through the real-time implementation in embedded systems.

Sensorless Fractional Order Control of PMSM Based on Synergetic and Sliding Mode Controllers

In conventional model predictive control, the dimensions of the control variables are different from each other, which makes adjusting the weighted factors in the cost function complicated. This issue can be solved by adopting the model predictive flux control. However, the performance of the electromagnetic torque is affected by the change of the cost function. A novel model predictive torque control of the interior permanent magnet synchronous motor is presented in this paper, and the cost function involving the excitation torque and reluctance torque is established. Combined with the model predictive flux control and discrete space vector modulation, the current ripple and torque ripple are reduced. The performance of torque under an overload condition is superior to model predictive flux control. The effectiveness of the proposed algorithm is verified by the simulation and experimental results.

https://www.mdpi.com/2079-9292/8/7/769/pdf

An Improved Model Predictive Torque Control for a Two-Level Inverter Fed Interior Permanent Magnet Synchronous Motor

Artificial intelligence based power consumption estimation of two-phase brushless DC motor according to FEA parametric simulation

The field-oriented control (FOC) strategy of a permanent magnet synchronous motor (PMSM) in a simplified form is based on PI-type controllers. In addition to their low complexity (an advantage for real-time implementation), these controllers also provide limited performance due to the nonlinear character of the description equations of the PMSM model under the usual conditions of a relatively wide variation in the load torque and the high dynamics of the PMSM speed reference. Moreover, a number of significant improvements in the performance of PMSM control systems, also based on the FOC control strategy, are obtained if the controller of the speed control loop uses sliding mode control (SMC), and if the controllers for the inner control loops of idand iqcurrents are of the synergetic type. Furthermore, using such a control structure, very good performance of the PMSM control system is also obtained under conditions of parametric uncertainties and significant variations in the combined rotor-load moment of inertia and the load resistance. To improve the performance of the PMSM control system without using controllers having a more complicated mathematical description, the advantages provided by reinforcement learning (RL) for process control can also be used. This technique does not require the exact knowledge of the mathematical model of the controlled system or the type of uncertainties. The improvement in the performance of the PMSM control system based on the FOC-type strategy, both when using simple PI-type controllers or in the case of complex SMC or synergetic-type controllers, is achieved using the RL based on the Deep Deterministic Policy Gradient (DDPG). This improvement is obtained by using the correction signals provided by a trained reinforcement learning agent, which is added to the control signals ud, uq, and iqref. A speed observer is also implemented for estimating the PMSM rotor speed. The PMSM control structures are presented using the FOC-type strategy, both in the case of simple PI-type controllers and complex SMC or synergetic-type controllers, and numerical simulations performed in the MATLAB/Simulink environment show the improvements in the performance of the PMSM control system, even under conditions of parametric uncertainties, by using the RL-DDPG.

/pdf/improvement-of-pmsm-sensorless-control-based-on-synergetic-2bgm98hj.pdf

Improvement of PMSM Sensorless Control Based on Synergetic and Sliding Mode Controllers Using a Reinforcement Learning Deep Deterministic Policy Gradient Agent

Power electronics technology is still an emerging technology, and it has found its way into many applications, from renewable energy generation (i [...]

/pdf/applications-of-power-electronics-2cchyxpl9i.pdf

Applications of Power Electronics

This paper presents sliding mode control of sensor-less parallel-connected two five-phase permanent magnet synchronous machines (PMSMs) fed by a single five-leg inverter. For both machines, the rotor speeds and rotor positions as well as load torques are estimated by using Extended Kalman Filter (EKF) scheme. Fully decoupled control of both machines is possible via an appropriate phase transposition while connecting the stator windings parallel and employing proposed speed sensor-less method. In the resulting parallel-connected two-machine drive, the independent control of each machine in the group is achieved by controlling the stator currents and speed of each machine under vector control consideration. The effectiveness of the proposed Extended Kalman Filter in conjunction with the sliding mode control is confirmed through application of different load torques for wide speed range operation. Comparison between sliding mode control and PI control of the proposed two-motor drive is provided. The speed response shows a short rise time, an overshoot during reverse operation and settling times is 0.075 s when PI control is used. The speed response obtained by SMC is without overshoot and follows its reference and settling time is 0.028 s. Simulation results confirm that, in transient periods, sliding mode controller remarkably outperforms its counterpart PI controller.

/pdf/extended-kalman-filter-based-sliding-mode-control-of-41imb49jrv.pdf

Extended Kalman filter based sliding mode control of parallel-connected two five-phase PMSM drive system

This paper is devoted to the vector control of five-phase permanent magnet synchronous motor (PMSM) fed by a five-phase voltage source inverter (VSI) controlled by space vector modulation (SVM). Based on the mathematical model of five-phase machine, vector control strategy is developed and the corresponding controllers are properly designed in order to provide independent torque control. Five-phase SVM algorithm is adopted to improve the performance of vector control drive. The efficacy of the proposed vector control based multiphase drive is verified by simulation at various dynamic operating conditions.

Vector control of five-phase permanent magnet synchronous motor drive

Sliding mode control of grid-connected wind energy system driven by 2 five-phase permanent magnet synchronous generators controlled by a new fifteen-switch converter

Direct torque control (DTC) based on artificial neural network (ANN) of a five-phase permanent magnet synchronous motor drive (PMSM) is presented in this paper. Using the mathematical model of the five-phase motor, DTC control strategy is developed, and the corresponding controllers are properly designed in order to provide independent torque and flux control. In order to improve the performance of the DTC, a neural network based DTC scheme is adopted instead of the DTC based on the look-up table. The employed neural network uses the Levenberg-Marquardt back propagation algorithm for the adjustment of weights to increase the learning process accuracy. The efficacy of the proposed method is verified by simulation for various dynamic operating conditions, and the system’s performance is compared with conventional DTC.

Direct Torque Control Based on Artificial Neural Network of a Five-Phase PMSM Drive

1185 Copyright c The Korean Institute of Electrical Engineers This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/ licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Sliding Mode Control Based DTC of Sensorless Parallel-Connected Two Five-Phase PMSM Drive System

Tounsi Kamel

Papers

Extended Kalman filter based sliding mode control of parallel-connected two five-phase PMSM drive system

Vector control of five-phase permanent magnet synchronous motor drive

Sliding mode control of grid-connected wind energy system driven by 2 five-phase permanent magnet synchronous generators controlled by a new fifteen-switch converter

Direct Torque Control Based on Artificial Neural Network of a Five-Phase PMSM Drive

Sliding mode control based dtc of sensorless parallel-connected two five-phase pmsm drive system