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

Identification and Adaptive Control for High-performance AC Drive Systems.

High-performance AC machinery and drive systems can be found in a variety of applications ranging from motion control to vehicle propulsion. However, machine parameters can vary significantly with electrical frequency, flux levels, and temperature, degrading the performance of the drive system. While adaptive control techniques can be used to estimate machine parameters online, it is sometimes desirable to estimate certain parameters offline. Additionally, parameter identification and control are typically conflicting objectives with identification requiring plant inputs which are rich in harmonics, and control objectives often consisting of regulation to a constant set-point. In this dissertation, we present research which seeks to address these issues for high-performance AC machinery and drive systems. The first part of this dissertation concerns the offline identification of induction machine parameters. Specifically, we have developed a new technique for induction machine parameter identification which can easily be implemented using a voltage-source inverter. The proposed technique is based on fitting steady-state experimental data to the circular stator current locus in the stator flux linkage reference-frame for varying steady-state slip frequencies, and provides accurate estimates of the magnetic parameters, as well as the rotor resistance and core loss conductance. Experimental results for a 43 kW induction machine are provided which demonstrate the utility of the proposed technique by characterizing the machine over a wide range of flux levels, including magnetic saturation. The remainder of this dissertation concerns the development of generalizable design methodologies for Simultaneous Identification and Control (SIC) of overactuated systems via case studies with Permanent Magnet Synchronous Machines (PMSMs). Specifically, we present different approaches to the design of adaptive controllers for PMSMs which exploit overactuation to achieve identification and control objectives simultaneously. The first approach utilizes a disturbance decoupling control law to prevent the excitation input from perturbing the regulated output. The second approach uses a Lyapunov-based adaptive controller to constrain the states to the output error-zeroing manifold on which they are varied to provide excitation for parameter identification. Finally, a receding-horizon control allocation approach is presented which includes a metric for generating persistently exciting reference trajectories.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120862/1/davereed_1.pd

Robust Position Control of PMSM Using Fractional-Order Sliding Mode Controller

A new robust fractional-order sliding mode controller (FOSMC) is proposed for the position control of a permanent magnet synchronous motor (PMSM). The sliding mode controller (SMC), which is insensitive to uncertainties and load disturbances, is studied widely in the application of PMSM drive. In the existing SMC method, the sliding surface is usually designed based on the integer-order integration or differentiation of the state variables, while in this proposed robust FOSMC algorithm, the sliding surface is designed based on the fractional-order calculus of the state variables. In fact, the conventional SMC method can be seen as a special case of the proposed FOSMC method. The performance and robustness of the proposed method are analyzed and tested for nonlinear load torque disturbances, and simulation results show that the proposed algorithm is more robust and effective than the conventional SMC method

Sensorless control for limp-home mode of EV applications

PhD ThesisOver the past decade research into electric vehicles’ (EVs) safety, reliability and availability has become a hot topic and has attracted a lot of attention in the literature. Inevitably these key areas require further study and improvement. One of the challenges EVs face is speed/position sensor failure due to vibration and harsh environments. Wires connecting the sensor to the motor controller have a high likelihood of breakage. Loss of signals from the speed/position sensor will bring the EV to halt mode. Speed sensor failure at a busy roundabout or on a high speed motorway can have serious consequences and put the lives of drivers and passengers in great danger. This thesis aims to tackle the aforementioned issues by proposing several novel sensorless schemes based on Model Reference Adaptive Systems (MRAS) suitable for limp-home mode of EV applications. The estimated speed from these schemes is used for the rotor flux position estimation. The estimated rotor flux position is employed for sensorless torque-controlled drive (TCD) based on indirect rotor field oriented control (IRFOC). The capabilities of the proposed schemes have been evaluated and compared to the conventional back-Electromotive Force MRAS (back-EMF MRAS) scheme using simulation environment and a test bench setup. The new schemes have also been tested on electric golf buggies. The results presented for the proposed schemes show that utilising these schemes provide a reliable and smooth sensorless operation during vehicle test-drive starting from standstill and over a wide range of speeds, including the field weakening region. Employing these new schemes for sensorless TCD in limp-home mode of EV applications increases safety, reliability and availability of EVs

Real-time Energy Management System of Battery-Supercapacitor in Electric vehicles

This thesis presents the design, simulation and experimental validation of an Energy Management System (EMS) for a Hybrid Energy Storage System (HESS) composed of lithium ion batteries and Supercapacitors (SCs) in electric vehicles. The aim of the EMS is to split the power demand considering the weaknesses and strengths or the power sources. The HESS requires an EMS to determine power missions for the battery and SC in real time, where the SC is commanded to assist the battery during high power demand and recover the energy generated during braking. Frequency sharing techniques have been proposed by researchers to achieve this objective, including the Discrete Wavelet Transform (DWT) and conventional filtration methods (low and high pass filters). However, filtration approaches can introduce delay (milliseconds to tens of seconds) in the frequency components which undermines the hybridisation advantages. Hence, the selection of the filtration technique and filter design are crucial to the system's performance. Researchers have proposed power demand prediction methodologies to deal with time delay, however, the advantages and drawbacks of using such methods have not been investigated thoroughly, particularly whether time delay compensation and its inherent prediction error improves the system performance, efficiency, and timely SC contribution during the motoring and braking stages. This work presents a fresh perspective to this research field by introducing a novel approach that deals with delay without complicated prediction algorithms and improves the SC contribution during the motoring and braking stages while reducing energy losses in the system. The proposed EMS allows the SC to provide timely assistance during motoring and to recover the braking energy generated. A charging strategy controls energy circulation between the battery and SC to keep the SC charge availability during the whole battery discharge cycle. The performance and efficiency of the HESS is improved when compared to the traditional use of conventional filtration techniques and the DWT. Results show that the proposed EMS method improves the energy efficiency of the HESS. For the US06 driving cycle, the energy efficiency is 91.6%. This is superior to the efficiency obtained with an EMS based on a high pass filter (41.3%), an EMS based on DWT high frequency component (30.3%) and an EMS based on the predicted DWT high frequency component (41%)