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

High Precision Positioning and Very Low Velocity Control of a Permanent Magnet Synchronous Motor

The purpose of this report is to evaluate a direct driven permanent magnet motor in high accuracy position and low speed operation. Actuation in this case is usually accomplished by stepping motors combined with belts and pulleys. High accuracy positioning is considered to be within 0.1 degrees and low speed 0.05 degrees per second, while at the same time have a 180 degree step response within 0.5 second. A model is derived of the motor along with methods for model parameter identification. This model is the basis for simulation of the motor in closed loop control. A prototype is developed in order to prove the validity of the results made by simulations. Experiments on the prototype resulted in two control methods, namely field oriented control and synchronous control. Conclusions drawn from the projects are as follows. The simulations do mirror the inherent problems with the permanent magnet motor. The prototype developed for the project is functioning and highly capable. Field oriented control was unable to meet the specified requirements. However, combined with iterative learning control the performance was improved significantly. Synchronous control satisfied most of the requirements, although its responsiveness and low efficiency are possible areas of improvement in future research

High Precision Positioning and Very Low Velocity Control of a Permanent Magnet Synchronous Motor

The purpose of this report is to evaluate a direct driven permanent magnet motor in high accuracy position and low speed operation. Actuation in this case is usually accomplished by stepping motors combined with belts and pulleys. High accuracy positioning is considered to be within 0.1 degrees and low speed 0.05 degrees per second, while at the same time have a 180 degree step response within 0.5 second. A model is derived of the motor along with methods for model parameter identification. This model is the basis for simulation of the motor in closed loop control. A prototype is developed in order to prove the validity of the results made by simulations. Experiments on the prototype resulted in two control methods, namely field oriented control and synchronous control. Conclusions drawn from the projects are as follows. The simulations do mirror the inherent problems with the permanent magnet motor. The prototype developed for the project is functioning and highly capable. Field oriented control was unable to meet the specified requirements. However, combined with iterative learning control the performance was improved significantly. Synchronous control satisfied most of the requirements, although its responsiveness and low efficiency are possible areas of improvement in future research

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

Cogging torque reduction in brushless motors by a nonlinear control technique

This work addresses the problem of mitigating the effects of the cogging torque in permanent magnet synchronous motors, particularly brushless motors, which is a main issue in precision electric drive applications. In this work, a method for mitigating the effects of the cogging torque is proposed, based on the use of a nonlinear automatic control technique known as feedback linearization that is ideal for underactuated dynamic systems. The aim of this work is to present an alternative to classic solutions based on the physical modification of the electrical machine to try to suppress the natural interaction between the permanent magnets and the teeth of the stator slots. Such modifications of electric machines are often expensive because they require customized procedures, while the proposed method does not require any modification of the electric drive. With respect to other algorithmic-based solutions for cogging torque reduction, the proposed control technique is scalable to different motor parameters, deterministic, and robust, and hence easy to use and verify for safety-critical applications. As an application case example, the work reports the reduction of the oscillations for the angular position control of a permanent magnet synchronous motor vs. classic PI (proportional-integrative) cascaded control. Moreover, the proposed algorithm is suitable to be implemented in low-cost embedded control units

Torque pulsations minimization in PM synchronous motor drive

Master'sMASTER OF ENGINEERIN

Multi-Objective Drive-Cycle Based Design Optimization of Permanent Magnet Synchronous Machines

Research conducted previously has shown that a battery electric vehicle (BEV) motor design incorporating drive-cycle optimization can lead to achievement of a higher torque density motor that consumes less energy over the drive-cycle in comparison to a conventionally designed motor. Such a motor indirectly extends the driving range of the BEV. Firstly, in this thesis, a vehicle dynamics model for a direct-drive machine and its associated vehicle parameters is implemented for the urban dynamometer driving schedule (UDDS) to derive loading data in terms of torque, speed, power, and energy. K-means clustering and Gaussian mixture modeling (GMM) are two clustering techniques used to reduce the number of machine operating points of the drive-cycle while preserving the characteristics of the entire cycle. These methods offer high computational efficiency and low computational time cost while optimizing an electric machine. Differential evolution (DE) is employed to optimize the baseline fractional slot concentrated winding (FSCW) surface permanent magnet synchronous machine (SPMSM). A computationally efficient finite element analysis (CEFEA) technique is developed to evaluate the machine at the representative drive-cycle points elicited from the clustering approaches. In addition, a steady-state thermal model is established to assess the electric motor temperature variation between optimization design candidates. In an alternative application, the drive-cycle cluster points are utilized for a computationally efficient drive-cycle system simulation that examines the effects of inverter time harmonics on motor performance. The motor is parameterized and modeled in a PSIM motor-inverter simulation that determines the current excitation harmonics that are injected into the machine during drive-cycle operation. These current excitations are inserted into the finite element analysis motor simulation for accurate analysis of the harmonic effects. The analysis summarizes the benefits of high-frequency devices such as gallium nitride (GaN) in comparison to insulated gate bipolar transistors (IGBT) in terms of torque ripple and motor efficiency on a drive-cycle

Recent Advances in Robust Control

Robust control has been a topic of active research in the last three decades culminating in H_2/H_\infty and \mu design methods followed by research on parametric robustness, initially motivated by Kharitonov's theorem, the extension to non-linear time delay systems, and other more recent methods. The two volumes of Recent Advances in Robust Control give a selective overview of recent theoretical developments and present selected application examples. The volumes comprise 39 contributions covering various theoretical aspects as well as different application areas. The first volume covers selected problems in the theory of robust control and its application to robotic and electromechanical systems. The second volume is dedicated to special topics in robust control and problem specific solutions. Recent Advances in Robust Control will be a valuable reference for those interested in the recent theoretical advances and for researchers working in the broad field of robotics and mechatronics

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

Automated Design Optimization of Synchronous Machines: Development and Application of a Generic Fitness Evaluation Framework

A rotating synchronous electric machine design can be described to its entirety by a combination of 17 to 24 discrete and continuous parameters pertaining the geometry, material selection, and electrical loading. Determining the performance attributes of a design often involves numerical solutions to thermal and magnetic equations. Stochastic optimization methods have proven effective for solving specific design problems in literature. A major challenge to design automation, however, is whether the design tool is versatile enough to solve design problems with different types of objectives and requirements. This work proposes a black-box approach in an attempt to encompass a wide variety of synchronous machine design problems. This approach attempts to enlist all possible attributes of interest (AoIs) to the end-user so that the design optimization problem can be framed by combination of such attributes only. The number of ways the end-user can input requirements is now defined and limited. Design problems are classified based on which of the AoI’s are constraints, objectives or design parameters. It is observed that regardless of the optimization problem definition, the evaluation of any design is based on a common set of physical and analytical models and empirical data. Problem definitions are derived based on black-box approach and efficient fitness evaluation algorithms are tailored to meet requirements of each problem definition. The proposed framework is implemented in Matlab/C++ environment encompassing different aspects of motor design. The framework is employed for designing synchronous machines for three applications where designs based on conventional motor construction did not meet all design requirements. The first design problem is to develop a novel bar-conductor tooth-wound stator technology for 1.2 kW in-wheel direct drive motor for an electric/hybrid-electric two wheeler (including practical implementation). The second design problem deals with a novel outer-rotor buried ferrite magnet geometry for a 1.2 kW in-wheel geared motor drive used in an electric/hybrid-electric two wheeler (including practical implementation). The third application involves design of an ultra-cost-effective and ultra-light-weight 1 kW aluminum conductor motor. Thus, the efficacy of automated design is demonstrated by harnessing the framework and algorithms for exploring new technologies applicable for three distinct design problems originated from practical applications