ANFIS based Direct Torque Control of PMSM Motor for Speed and Torque Regulation

Nowadays, the Permanent Magnet Synchronous Motors (PMSM) are gaining popularity among electric motors due to their high efficiency, high-speed operation, ruggedness, and small size. PMSM motors comprise a trapezoidal electromotive force which is also called synchronous motors. Direct Torque Control (DTC) has been extensively applied in speed regulation systems due to its better dynamic behavior. The controller manages the amplitude of torque and stator flux directly using the direct axis current. To manage the motor speed, the torque error, flux error, and projected location of flux linkage are employed to adjust the inverter switching sequence via Space Vector Pulse Width Modulation (SVPWM). One of the most common problems encountered in a PMSM motor is Torque ripple, which is recreated by power electronic commutation and a better controller reduces the ripples to increase the drive's performance. Conventional controllers such as PI, PID and SVPWM-DTC were compared with the proposed Adaptive Neuro-Fuzzy Inference System (ANFIS) in terms of performance measures such as speed and torque ripple. In this work, the Two-Gaussian membership function of the ANFIS controller is used in conjunction with a PMSM motor to reduce torque ripple up to 0.53 Nm and maintain the speed with a distortion error of 2.33 %

High performance position control for permanent magnet synchronous drives

In the design and test of electric drive control systems, computer simulations provide a useful way to verify the correctness and efficiency of various schemes and control algorithms before the final system is actually constructed, therefore, development time and associated costs are reduced. Nevertheless, the transition from the simulation stage to the actual implementation has to be as straightforward as possible. This document presents the design and implementation of a position control system for permanent magnet synchronous drives, including a review and comparison of various related works about non-linear control systems applied to this type of machine. The overall electric drive control system is simulated and tested in Proteus VSM software which is able to simulate the interaction between the firmware running on a microcontroller and analogue circuits connected to it. The dsPIC33FJ32MC204 is used as the target processor to implement the control algorithms. The electric drive model is developed using elements existing in the Proteus VSM library. As in any high performance electric drive system, field oriented control is applied to achieve accurate torque control. The complete control system is distributed in three control loops, namely torque, speed and position. A standard PID control system, and a hybrid control system based on fuzzy logic are implemented and tested. The natural variation of motor parameters, such as winding resistance and magnetic flux are also simulated. Comparisons between the two control schemes are carried out for speed and position using different error measurements, such as, integral square error, integral absolute error and root mean squared error. Comparison results show a superior performance of the hybrid fuzzy-logic-based controller when coping with parameter variations, and by reducing torque ripple, but the results are reversed when periodical torque disturbances are present. Finally, the speed controllers are implemented and evaluated physically in a testbed based on a brushless DC motor, with the control algorithms implemented on a dsPIC30F2010. The comparisons carried out for the speed controllers are consistent for both simulation and physical implementation

Permanent-Magnet Synchronous Machine Drives

The permanent-magnet synchronous machine (PMSM) drive is one of best choices for a full range of motion control applications. For example, the PMSM is widely used in robotics, machine tools, actuators, and it is being considered in high-power applications such as industrial drives and vehicular propulsion. It is also used for residential/commercial applications. The PMSM is known for having low torque ripple, superior dynamic performance, high efficiency and high power density. Section 1 deals with the introduction of PMSM and how it is evolved from synchronous motors. Section 2 briefly discusses about the types of PMSM. Section 3 tells about the assumptions in PMSM for modeling of PMSM and it derives the equivalent circuit of PMSM. In Section 4, permanent magnet synchronous motor drive system is briefly discussed with explanation of each blocks in the systems. Section 5 reveals about the control techniques of PMSM like scalar control, vector control and simulation of PMSM driven by field-oriented control using fuzzy logic control with space vector modulation for minimizing torque ripples. PMSM control with and without rotor position sensors along with different control techniques for controlling various parameters of PMSM for different applications is presented in Section 6

Neural-Network Vector Controller for Permanent-Magnet Synchronous Motor Drives: Simulated and Hardware-Validated Results

This paper focuses on current control in a permanentmagnet synchronous motor (PMSM). The paper has two main objectives: The first objective is to develop a neural-network (NN) vector controller to overcome the decoupling inaccuracy problem associated with conventional PI-based vector-control methods. The NN is developed using the full dynamic equation of a PMSM, and trained to implement optimal control based on approximate dynamic programming. The second objective is to evaluate the robust and adaptive performance of the NN controller against that of the conventional standard vector controller under motor parameter variation and dynamic control conditions by (a) simulating the behavior of a PMSM typically used in realistic electric vehicle applications and (b) building an experimental system for hardware validation as well as combined hardware and simulation evaluation. The results demonstrate that the NN controller outperforms conventional vector controllers in both simulation and hardware implementation

Mitigating the Torque Ripple in Electric Traction using Proportional Integral Resonant Controller

Permanent magnet (PM) machines offer high efficiencies which are attractive to be used in vehicle propulsion systems, however, their design creates an inherent torque ripple. This is particularly problematic for electric vehicles (EV) due to low damping of torsional vibration which can result in reduced vehicle comfort. This can prohibit the take up of PM machines, missing opportunities for improving vehicle energy efficiency. This paper presents the application of resonant control (RC) to suppress the impact of the PM torque ripple this enabling take up of this technology and for the first time aims to demonstrate a reduction in vibration at a vehicle level. A prototype PM machine and driveline have been fitted to a light-duty off-road vehicle. Firstly an analysis of the vehicle vibration when it is driven in a speed-control mode with a conventional proportional-integral (PI) control. The main source of the vibration is identified as the 24th harmonic torque ripple of the PM machine, which originates from the cogging torque and air-gap flux harmonics. The vibration is more severe when the torque ripple frequency is close to the natural frequency of the drivetrain. The application of Resonance Control has demonstrated over 80% reduction in speed ripple even when the torque ripple frequency is close to the natural frequency of the vehicle

Implementation and Analysis of Direct Torque Control for Permanent Magnet Synchronous Motor Using Gallium Nitride based Inverter

Permanent magnet synchronous machines (PMSMs) attract considerable attention in various industrial applications, such as electric and hybrid electric vehicles, due to their high efficiency and high-power density. In this thesis, the mathematical model of PMSM and two popular control strategies, field-oriented control (FOC) and direct torque control (DTC), are analyzed and compared. The results demonstrated that the DTC has better dynamic response in comparison to FOC. Moreover, DTC can eliminate the use of position sensor, which will save the cost of the PMSM drive system. Therefore, this thesis focuses on the design and implementation of high-performance DTC for PMSMs with a Gallium Nitride (GaN) based high switching frequency motor drive. First, the characteristics and operation principles of a PMSM are introduced. Then, the mathematical models of a PMSM under different coordinate systems are investigated. Consequently, a PMSM model is developed based on the dq rotating reference frame and implemented in the MATLAB/Simulink for validation. Two advanced PMSM control strategies, FOC and DTC, are investigated and compared in terms of control performance through comprehensive simulation studies and the results demonstrate that DTC has better dynamic performance. Conventional DTC contributes to higher torque ripple in the PMSM due to the limited switching frequency in a conventional semiconductor-based motor drive, which inevitably deteriorates the drive performance. Therefore, this thesis aims to reduce the torque ripple in the DTC based PMSM drive by using the new generation wide bandgap switching devices. More specifically, DTC is improved by using the optimized space vector pulse width modulation strategy and a higher switching frequency contributed by the GaN based motor drive. Finally, the proposed DTC-SVM based PMSM control strategy is implemented on the digital signal processor (DSP) and evaluated on the laboratory GaN based PMSM drive. Both the simulation and experimental results show that the proposed improvement in the DTC can further improve the PMSM drive performance

Torque Control

This book is the result of inspirations and contributions from many researchers, a collection of 9 works, which are, in majority, focalised around the Direct Torque Control and may be comprised of three sections: different techniques for the control of asynchronous motors and double feed or double star induction machines, oriented approach of recent developments relating to the control of the Permanent Magnet Synchronous Motors, and special controller design and torque control of switched reluctance machine

Artificial Neural Network-Based Gain-Scheduled State Feedback Speed Controller for Synchronous Reluctance Motor

This paper focuses on designing a gain-scheduled (G-S) state feedback controller (SFC) for synchronous reluctance motor (SynRM) speed control with non-linear inductance characteristics. The augmented model of the drive with additional state variables is introduced to assure precise control of selected state variables (i.e. angular speed and d-axis current). Optimal, non-constant coefficients of the controller are calculated using a linear-quadratic optimisation method. Non-constant coefficients are approximated using an artificial neural network (ANN) to assure superior accuracy and relatively low usage of resources during implementation. To the best of our knowledge, this is the first time when ANN-based gain-scheduled state feedback controller (G-S SFC) is applied for speed control of SynRM. Based on numerous simulation tests, including a comparison with a signum-based SFC, it is shown that the proposed solution assures good dynamical behaviour of SynRM drive and robustness against q-axis inductance, the moment of inertia and viscous friction fluctuations

Application of Optimal Switching Using Adaptive Dynamic Programming in Power Electronics

In this dissertation, optimal switching in switched systems using adaptive dynamic programming (ADP) is presented. Two applications in power electronics, namely single-phase inverter control and permanent magnet synchronous motor (PMSM) control are studied using ADP. In both applications, the objective of the control problem is to design an optimal switching controller, which is also relatively robust to parameter uncertainties and disturbances in the system. An inverter is used to convert the direct current (DC) voltage to an alternating current (AC) voltage. The control scheme of the single-phase inverter uses a single function approximator, called critic, to evaluate the optimal cost and determine the optimal switching. After offline training of the critic, which is a function of system states and elapsed time, the resulting optimal weights are used in online control, to get a smooth output AC voltage in a feedback form. Simulations show the desirable performance of this controller with linear and nonlinear load and its relative robustness to parameter uncertainty and disturbances. Furthermore, the proposed controller is upgraded so that the inverter is suitable for single-phase variable frequency drives. Finally, as one of the few studies in the field of adaptive dynamic programming (ADP), the proposed controllers are implemented on a physical prototype to show the performance in practice. The torque control of PMSMs has become an interesting topic recently. A new approach based on ADP is proposed to control the torque, and consequently the speed of a PMSM when an unknown load torque is applied on it. The proposed controller achieves a fast transient response, low ripples and small steady-state error. The control algorithm uses two neural networks, called critic and actor. The former is utilized to evaluate the cost and the latter is used to generate control signals. The training is done once offline and the calculated optimal weights of actor network are used in online control to achieve fast and accurate torque control of PMSMs. This algorithm is compared with field-oriented control (FOC) and direct torque control based on space vector modulation (DTC-SVM). Simulations and experimental results show that the proposed algorithm provides desirable results under both accurate and uncertain modeled dynamics

Design and Control of Electrical Motor Drives

Dear Colleagues, I am very happy to have this Special Issue of the journal Energies on the topic of Design and Control of Electrical Motor Drives published. Electrical motor drives are widely used in the industry, automation, transportation, and home appliances. Indeed, rolling mills, machine tools, high-speed trains, subway systems, elevators, electric vehicles, air conditioners, all depend on electrical motor drives.However, the production of effective and practical motors and drives requires flexibility in the regulation of current, torque, flux, acceleration, position, and speed. Without proper modeling, drive, and control, these motor drive systems cannot function effectively.To address these issues, we need to focus on the design, modeling, drive, and control of different types of motors, such as induction motors, permanent magnet synchronous motors, brushless DC motors, DC motors, synchronous reluctance motors, switched reluctance motors, flux-switching motors, linear motors, and step motors.Therefore, relevant research topics in this field of study include modeling electrical motor drives, both in transient and in steady-state, and designing control methods based on novel control strategies (e.g., PI controllers, fuzzy logic controllers, neural network controllers, predictive controllers, adaptive controllers, nonlinear controllers, etc.), with particular attention to transient responses, load disturbances, fault tolerance, and multi-motor drive techniques. This Special Issue include original contributions regarding recent developments and ideas in motor design, motor drive, and motor control. The topics include motor design, field-oriented control, torque control, reliability improvement, advanced controllers for motor drive systems, DSP-based sensorless motor drive systems, high-performance motor drive systems, high-efficiency motor drive systems, and practical applications of motor drive systems. I want to sincerely thank authors, reviewers, and staff members for their time and efforts. Prof. Dr. Tian-Hua Liu Guest Edito