Critical Aspects of Electric Motor Drive Controllers and Mitigation of Torque Ripple - Review

Electric vehicles (EVs) are playing a vital role in sustainable transportation. It is estimated that by 2030, Battery EVs will become mainstream for passenger car transportation. Even though EVs are gaining interest in sustainable transportation, the future of EV power transmission is facing vital concerns and open research challenges. Considering the case of torque ripple mitigation and improved reliability control techniques in motors, many motor drive control algorithms fail to provide efficient control. To efficiently address this issue, control techniques such as Field Orientation Control (FOC), Direct Torque Control (DTC), Model Predictive Control (MPC), Sliding Mode Control (SMC), and Intelligent Control (IC) techniques are used in the motor drive control algorithms. This literature survey exclusively compares the various advanced control techniques for conventionally used EV motors such as Permanent Magnet Synchronous Motor (PMSM), Brushless Direct Current Motor (BLDC), Switched Reluctance Motor (SRM), and Induction Motors (IM). Furthermore, this paper discusses the EV-motors history, types of EVmotors, EV-motor drives powertrain mathematical modelling, and design procedure of EV-motors. The hardware results have also been compared with different control techniques for BLDC and SRM hub motors. Future direction towards the design of EV by critical selection of motors and their control techniques to minimize the torque ripple and other research opportunities to enhance the performance of EVs are also presented.publishedVersio

Learning position controls for hybrid step motors: from current-fed to full-order models

The experimental comparison of two different global learning position controls (namely, ‘adaptive learning’ and ‘repetitive learning’ controls) for hybrid step motors performing repetitive tasks has been recently presented in the literature. Related benefits and drawbacks have been successfully analyzed on the same robotic application. However, the design of the two aforementioned learning controls - though relying on a rigorous stability analysis - are based on a simplified current-fed model of the motor. They cannot achieve precise current tracking due to the mere presence of PI control actions in the outer current control loops. The aim of this paper is to enrich and update the results of the above comparison in the light of the latest contributions that generalize the theoretical design to the fullorder voltage-fed motor models of hybrid step motors. Learning actions are now included in the outer current control loops: they generalize the corresponding PI actions to the periodic scenario and allow to solve a control problem whose solution was seeming very difficult to be obtained

Parameter Estimation for PMSM based on a Back Propagation Neural Network Optimized by Chaotic Artificial Fish Swarm Algorithm

Permanent Magnet Synchronous Motor(PMSM) control system with strong nonlinearity makes it difficult to accurately identify motor parameters such as stator winding, dq axis inductance, and rotor flux linkage. Aiming at the premature convergence of traditional Back Propagation Neural Network(BPNN) in PMSM motor parameter identification, a new method of PMSM motor parameter identification is proposed. It uses Chaotic Artificial Fish Swarm Algorithm(CAFSA) to optimize the initial weights and thresholds of BPNN, and then strengthens training by BPNN algorithm. Thus, the global optimal network parameters are obtained by using the global optimization of CAFSA and the local search ability of BPNN. The simulation results and experimental data show that the initial value sensitivity of the network model optimized by CAFS-BPNN Algorithm is weak, the parameter setting is robust, and the system stability is good under complex conditions. Compared with other intelligent algorithms, such as RSL and PSO, CAFS-BPNNA has high identification accuracy and fast convergence speed for PMSM motor parameters

Multiple Objective Co-Optimization of Switched Reluctance Machine Design and Control

This dissertation includes a review of various motor types, a motivation for selecting the switched reluctance motor (SRM) as a focus of this work, a review of SRM design and control optimization methods in literature, a proposed co-optimization approach, and empirical evaluations to validate the models and proposed co-optimization methods. The switched reluctance motor (SRM) was chosen as a focus of research based on its low cost, easy manufacturability, moderate performance and efficiency, and its potential for improvement through advanced design and control optimization. After a review of SRM design and control optimization methods in the literature, it was found that co-optimization of both SRM design and controls is not common, and key areas for improvement in methods for optimizing SRM design and control were identified. Among many things, this includes the need for computationally efficient transient models with the accuracy of FEA simulations and the need for co-optimization of both machine geometry and control methods throughout the entire operation range with multiple objectives such as torque ripple, efficiency, etc. A modeling and optimization framework with multiple stages is proposed that includes robust transient simulators that use mappings from FEA in order to optimize SRM geometry, windings, and control conditions throughout the entire operation region with multiple objectives. These unique methods include the use of particle swarm optimization to determine current profiles for low to moderate speeds and other optimization methods to determine optimal control conditions throughout the entire operation range with consideration of various characteristics and boundary conditions such as voltage and current constraints. This multi-stage optimization process includes down-selections in two previous stages based on performance and operational characteristics at zero and maximum speed. Co-optimization of SRM design and control conditions is demonstrated as a final design is selected based on a fitness function evaluating various operational characteristics including torque ripple and efficiency throughout the torque-speed operation range. The final design was scaled, fabricated, and tested to demonstrate the viability of the proposed framework and co-optimization method. Accuracy of the models was confirmed by comparing simulated and empirical results. Test results from operation at various torques and speeds demonstrates the effectiveness of the optimization approach throughout the entire operating range. Furthermore, test results confirm the feasibility of the proposed torque ripple minimization and efficiency maximization control schemes. A key benefit of the overall proposed approach is that a wide range of machine design parameters and control conditions can be swept, and based on the needs of an application, the designer can select the appropriate geometry, winding, and control approach based on various performance functions that consider torque ripple, efficiency, and other metrics

Advancements in Flux Switching Machine Optimization : Applications and Future Prospects

This work was supported by the Commonwealth Scholarship Commission, U. K., under Grant Number: NGCN-180-2021Peer reviewe

Position control of linear ultrasonic motor

Master'sMASTER OF ENGINEERIN

Global identification of electrical and mechanical parameters in PMSM drive based on dynamic self-learning PSO

A global parameter estimation method for a PMSM drive system is proposed, where the electrical parameters, mechanical parameters and voltage-source-inverter (VSI) nonlinearity are regarded as a whole and parameter estimation is formulated as a single parameter optimization model. A dynamic learning estimator is proposed for tracking the electrical parameters, mechanical parameters and VSI of PMSM drive by using dynamic self learning particle swarm optimization (DSLPSO). In DSLPSO, a novel movement modification equation with dynamic exemplar learning strategy is designed to ensure its diversity and achieve a reasonable tradeoff between the exploitation and exploration during the search process. Moreover, a nonlinear multi-scale based interactive learning operator is introduced for accelerating the convergence speed of the Pbest particles; meanwhile a dynamic opposition-based learning (OBL) strategy is designed to facilitate the gBest particle to explore a potentially better region. The proposed algorithm is applied to parameter estimation for a PMSM drive system. The results show that the proposed method has better performance in tracking the variation of electrical parameters, and estimating the immeasurable mechanical parameters and the VSI disturbance voltage simultaneously

Operation Efficiency Optimization for Permanent Magnet Synchronous Motor Based on Improved Particle Swarm Optimization

In this paper, an improved online particle swarm optimization (PSO) is proposed to optimize the traditional search controller for improving the operating efficiency of the permanent magnet synchronous motor (PMSM). This algorithm combines the advantages of the attraction and repulsion PSO and the distributed PSO that can help the search controller to find the optimal d - axis air gap current quickly and accurately under non-stationary operating conditions, thereby minimizing the air gap flux and then improving the motor efficiency. To verify the effectiveness and stability of this proposed algorithm, the operating efficiency of PMSM as using this proposed algorithm is compared with that of traditional search controller under non-stationary operating conditions. The results show that the proposed algorithm can improve the operating efficiency of PMSM by 6.03% on average under non-stationary operation conditions. This indicates that the search controller based on the improved PSO has a better adaptation to the variation of external operating conditions, and can improve the operation efficiency of PMSM under non-stationary condition

A genetic-based prognostic method for aerospace electromechanical actuators

Prior awareness of impending failures of primary flight command electromechanical actuators (EMAs) utilizing prognostic algorithms can be extremely useful. Indeed, early detection of the degradation pattern might signal the need to replace the servomechanism before the failure manifests itself. Furthermore, such algorithms frequently use a model-based approach based on a direct comparison of the real (High Fidelity) and monitor (Low Fidelity) systems to discover fault characteristics via optimization methods. The monitor model enables the gathering of accurate and exact data while requiring a minimal amount of processing. This work describes a novel simplified monitor model that accurately reproduces the dynamic response of a typical aerospace EMA. The task of fault detection and identification is carried out by comparing the output signal of the reference system (the high fidelity model) with that acquired from the monitor model. The Genetic Algorithm is then used to optimize the matching between the two signals by iteratively modifying the fault parameters, getting the global minimum of a quadratic error function. Once this is found, the optimization parameters are connected with the assumed progressive failures to assess the system's health. The high-fidelity reference model examined in this study is previously conceptualized, developed, implemented in MATLAB-Simulink and finally experimentally confirmed

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