Sensorless Current Source-Fed PM Drive System For Low Speed Operations

The objective of this paper is to control the speed of Permanent Magnet Synchronous Motor (PMSM) over wide range of speed by consuming minimum time and low cost. Therefore, comparative performance analysis of PMSM on basis of speed regulation has been done in this study. Comparison of two control strategies i.e. Field oriented control (FOC) without sensor less Model Reference Adaptive System (MRAS) and FOC with sensor less MRAS has been carried out. Sensor less speed control of PMSM is achieved by using estimated speed deviation as feedback signal for the PI controller. Performance of the both control strategies has been evaluated in in MATLAB Simulink software. Simulation studies show the response of PMSM speed during various conditions of load and speed variations. Obtained results reveal that the proposed MRAS technique can effectively estimate the speed of rotor with high exactness and torque response is significantly quick as compared to the system without MRAS control system.

Model reference adaptive system (MRAS) is one of the popular methods to observe the speed and the angle information of the Permanent magnet synchronous motor (PMSM). This paper proposes a new adaptation scheme for MRAS to replace the conventional PI controller based on Popov Hyperstability theorem. In this project, the speed of PMSM is controlled by using field oriented control (FOC) method and the MRAS is used to observe the speed and the rotor position of the motor. The reference model for the MRAS is the PMSM itself while the adjustable model is the current model in the rotor reference frame. The Sugeno Fuzzy Logic Inference System (FIS) is implemented in the adaptation scheme in order to tune the error between the reference and the adjustable model. The new proposed method shows it is capable to track the motor speed efficiently at wide range of operating speeds.

This article presents the sensorless control of a PMSM based on the FOC (Field Oriented Control) strategy and on PFC (Power Factor Correction), where the rotor speed is provided by speed observers. It presents the block structure of the PFC together with the numerical simulations achieved in Matlab/Simulink, on the improvement of the PF (Power Factor) of the supply circuit. It also presents the block structures of the FOC strategy and of the back-EMF (back electromotive force) sliding mode, MRAS (Model Reference Adaptive System), and PLL (Phase Locked Loop) type observers. The numerical simulations show the superiority of rotor speed estimation when using the back-EMF sliding mode observer compared to the rotor speed estimate provided by MRAS and PLL observers.

In order to optimize the speed-control performance of the permanent-magnet synchronous motor (PMSM) system with different disturbances and uncertainties, a nonlinear speed-control algorithm for the PMSM servo systems using sliding-mode control (SMC) and load torque compensation techniques is developed in this paper. A SMC strategy is designed based on field oriented control (FOC) theory, which is adopted in the speed loop of PMSM system. Meanwhile, considering the SMC need providing large gain to restrain disturbances resulting in large chattering, an adaptive extended sliding mode torque observer (ESMO) is proposed to estimate and compensate load torque. Through torque estimation for feed-forward compensation, one new composite SMC algorithm has been proposed to take a smaller value of switching gain without large chattering. The control scheme is verified by comprehensive simulation and experimental results.

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 id and iq currents 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.

This work proposes a novel state-feedback controller for Permanent Magnet Synchronous Motors (PMSMs) based on an extended integral model with dynamic input. Traditional Field-Oriented Control (FOC) schemes use cascade PI loops to regulate torque and speed; however, their dynamic response is limited by saturation effects. To overcome this, we design an optimal controller using the Linear Quadratic Regulator (LQR) theory, which employs an augmented state-space model that integrates tracking errors and replaces the inner current loop with a dynamic input. The Riccati equation is solved for customized matrices to minimize control energy while ensuring a fast transient response. Simulations under abrupt speed changes (from negative to positive) and constant load torque demonstrate the controller’s superiority over conventional PI-FOC.

This study compares Field-Oriented Control (FOC) and trapezoidal control techniques for Permanent Magnet Synchronous Motors (PMSMs) in modern industrial variable-speed applications. Traditionally, variable-speed applications have relied on DC machines, which have inherent drawbacks such as increased maintenance, spark losses, higher inertia, and overall higher costs. This FOC has emerged as a sophisticated method that enables independent control of torque and magnetization flux in AC motors, allowing for precise regulation of torque and speed tailored to the motor's electromagnetic state. FOC leverages Sinusoidal Pulse-Width Modulation (SPWM) and Space Vector Pulse Width Modulation (SVPWM) techniques to enhance DC voltage utilization and minimize switching and harmonic losses. In contrast, the paper presents a detailed model of a PMSM drive system based on electronic components rather than standard mathematical blocks, leading to more realistic simulation results. All simulation modules, including the inverter and PWM generator, were custom-built, allowing for performance comparison under varying speed and torque conditions. Brushless DC (BLDC) motors, which are typically categorized as PMSMs with trapezoidal back-EMF waveforms, have gained traction due to their simplicity and cost-effectiveness. The trapezoidal control technique, also known as six-step commutation, simplifies the control process by aligning motor phase switching with the rotor position, resulting in a quasi-square-wave current. However, this method introduces torque ripple and presents control challenges, particularly in low-speed applications. Given the rising economic constraints and stringent governmental standards, there is an increasing demand for efficient, low-interference, and adaptable electrical systems. This study demonstrates that FOC provides a more robust and efficient solution for high-performance applications than the trapezoidal control technique in PMSMs.

FOC (field-oriented control) or vector control technique is used in permanent magnet synchronous motor to control the torque and field components of stator currents. To control the speed of the PMSM drive, the reference speed is taken as a input signal which is compared to actual speed of the motor and then speed controller generates a quadrature axis current signal which is used in vector control to control the torque and the flux. The simulation modelling of PMSM drive is done in MATLAB/Simulink. The measurements are taken of both simulated and hardware model of PMSM. The experiment results of the PMSM drive using FOC scheme are shown in this paper. PMSM operates in both constant torque as well as constant power region. Here, motor operation is done only in constant torque region. The experiment results indicate that the vector controlled-PMSM has improved speed stability still in a very low speed range.

This paper deals with the performance analysis of the field oriented control (FOC) of permanent magnet synchronous motor (PMSM) drive with a PID (proportional plus integral plus derivative) in DC link voltage control and fuzzy PID for speed control in closed loop operation. A mathematical model of the PMSM drive has been developed by considering each accessories of the model to analyze both the steady state and transient performances of the PMSM drive system. The current controlled inverter feeding power to the PMSM motor is powered from a PWM current controlled converter. The advantages of the integrated PMSM drive are demonstrated. The PID voltage control takes care of the constant DC link voltage to feed the inverter. The fuzzy controller provides a better response to the drive system specially in the steady state condition. The state space model developed in terms of derivatives of winding currents, DC link voltage, supply currents, rotor speed, and rotor position are simulated to obtain the required information. The study also examines the effectiveness of the PMSM drive under steady state and transient conditions for different parameter variation.

In this paper, a power factor corrected Permanent Magnet Synchronous motor (PMSM) drive is discussed in detail. The wide variety of applications of PMSM drives makes it necessary to achieve fast and reliable drive control system design. This paper presents an analytical modeling of Surface Permanent magnet Synchronous motor (SPMSM) and Interior Permanent magnet Synchronous motor (IPMSM). A PI-controller based closed-loop Volts/hertz (V/F) controller is designed. It is based on Field Oriented Control (FOC) algorithm and operates in constant torque region. The controller is designed to ensure that the three phase IPMSM is operated close to unity power factor for wide speed range. IPMSM is designed and modeled using MATLAB/Simulink environment. The performance of the motor is evaluated in terms of power factor, total harmonic distortion and efficiency. Furthermore, a comparative analysis is done to validate the efficacy of IPMSM.

This paper describes two alternative variable speed motor drive systems for washing machine applications. Three phase induction motors with tachometer feedback and direct drive permanent magnet synchronous motors with hall sensor feedback are two drive systems commonly used in North American washers today. Appliance manufacturers are now evaluating sensorless drive systems because of the low reliability and high cost of the speed and position feedback sensors. A Field Oriented Control Algorithm with an embedded rotor flux and position estimation algorithm enables sensorless control of both permanent magnet synchronous motors and induction motors. The estimator derives rotor shaft position and speed from rotor flux estimates obtained from measured stator currents and the applied voltages. Sampling of currents in the dc link shunt simplifies stator current measurement and minimizes cost. Field oriented control algorithm allows good dynamic control of torque and enables an extended speed range through field weakening. The digital control algorithm runs on a unique hardware engine that allows algorithms to be designed using graphical tools. A common hardware platform can run either the PMSM or IM using sensorless field oriented control in a front loading washer application. Test results are presented for both drives in standard wash cycles.

Field-Oriented Control (FOC) is widely recognized as a standard framework for Permanent Magnet Synchronous Motor (PMSM) drives. Linear control techniques are commonly employed in designing controllers for this strategy. However, traditional control methods often exhibit performance limitations and reduced robustness, particularly under harsh operating conditions, which makes the FOC structure less appealing and less effective. To address and overcome these challenges, this study proposes a Second-order Non-singular Terminal Sliding (SNTS) mode approach to achieve fast, accurate, and robust tracking for the FOC control structure applied to PMSM drives. The SNTS method combines the benefits of non-singular terminal sliding mode and second-order control laws. This approach ensures rapid and precise tracking while minimizing steady-state errors by using a nonlinear terminal sliding mode surface instead of a linear one. Furthermore, the system state transitions smoothly along the sliding mode surface with continuous functions, which reduces chattering around the sliding surface. The second-order control law incorporated into this method helps mitigate chattering and achieve fast convergence. The Lyapunov stability theory is employed to verify the stability of the SNTS technique designed for the PMSM system. Simulation and experimental validation on a hardware platform confirm the effectiveness and superiority of the proposed SNTS method, demonstrating its capability to enhance the performance of speed controllers for PMSM drives.

In modern high performance ac drives, field oriented control (FOC) or direct torque control (DTC) schemes are applied to achieve high dynamic operation. Recently, efforts have been made to eliminate the machine shaft sensor in order to reduce costs and increase reliability. Although DTC is often designated as a sensorless scheme, it has similar problems to FOC when operated around zero flux frequency. In this paper, the performance of both schemes is investigated with special emphasis on operation around zero speed. In that operating range it is assumed nowadays that, without a shaft sensor, the flux position can only be determined by exploiting parasitic effects of the machine such as saturation or slotting. Sensorless schemes to estimate the spatial flux position control suffer from reduced accuracy in specific load ranges which degrades the performance of the drive when compared to operation with a shaft sensor. On a test stand FOC and DTC have been realized and the flux position calculated with the use of a speed sensor has been modified with different error functions to test the reaction of both schemes on erroneous flux input values.

This article presents a Permanent Magnet Synchronous Motor (PMSM) sensorless control system using backstepping control. The Extended State Observer (ESO) is also used to perform the sensorless control, and to estimate the load torque which is considered an external disturbance of the control system. Starting from the classic Field Oriented Control (FOC) type PMSM control structure, the classic PI speed and current controllers i <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sub> , i <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> are replaced by a backstepping control structure. The main blocks used for the implementation and numerical simulation in the Matlab/Simulink graphical programming environment are presented. The very good static and dynamic performance obtained through numerical simulations even at high speeds, step variations of the value of the load torque, uniformly distributed noise, and variations of the PSMS nominal parameters can be explained by the fact that the backstepping control is applied to the non-linear system of the PMSM and by the step-by-step method of achieving each iqref, ud and uq control by using a Lyapunov-type function. Future articles propose the approach of real-time implementation of the PMSM sensorless control by using the backstepping control.

This paper examines the enhancement of Electric Vehicle (EV) Permanent Magnet Synchronous Motor (PMSM) drives through the integration of Field-Oriented Control (FOC) with Model Predictive Control (MPC). The study aims to achieve high precision and dynamic response for PMSM drives under diverse operating conditions. FOC provides good control capability over full torque and speed ranges. MPC is an optimal control technique in which the calculated control actions minimize a cost function for a constrained dynamic system. The theoretical framework combines FOC and MPC principles, utilizing MPC's predictive capabilities to optimize d-q current references in real-time. The methodology encompasses the design and implementation of an MPC technique integrated with FOC, with key objectives including minimising torque ripples, maintaining vehicle stability through robust control loops, and optimising PMSM drive performance across a wide speed range of EV. Different types of torque control methodologies, such as PI Controller and Voltage Vector Control for PMSM Control, can be applied to an electric vehicle. The results indicate significant improvements in torque ripple reduction, dynamic response, and disturbance rejection, demonstrating the robustness and adaptability of the proposed control system compared with the PI Controller. This approach effectively addresses key challenges and signifies advancements over traditional control methods, contributing to the field of electric vehicle control systems.