Motor Parameter Variations Research Articles

Current disturbance compensation of continuous model predictive control scheme for PMSMs

During the operation of a permanent magnet synchronous motor, limitations imposed by digital chips inevitably result in certain delays. Additionally, external environmental factors introduce variations in motor parameters. This study aims to comprehensively analyze these issues. Firstly, the control delay and disturbances caused by motor parameter changes are transformed into current disturbances using a uniform approach. Secondly, various sliding mode model predictive controllers are designed to effectively suppress the current disturbances arising from aforementioned reasons. Moreover, considering controller implementation aspects, this paper thoroughly discusses the influence of core controller parameters on current disturbances and their adaptability towards different parameter mismatches. Furthermore, extensive examination is conducted on global stability conditions for controller realization. Finally, experimental results validate that the proposed methods successfully optimize current disturbances originating from aforementioned causes.

Integer PI, fractional PI and fractional PI data trained ANFIS speed controllers for indirect field oriented control of induction motor

Induction motor drives with variable speed applications that employ vector control are quite popular nowadays because they provide strong dynamic performance and flexible speed control. By decoupling the torque-producing current components of stator current from the rotor flux, Indirect Field Oriented Control is recognized for generating excellent performance in induction motor drives. This investigation is being done to show the effectiveness of the novel FPI input-output data-trained ANFIS controller and compare the three controllers' performance in terms of load variation capabilities, motor parameter variation, and speed tracking. Consequently, a comparison of the three controllers is important to select which controller performs high in induction motor drive. Indirect Field Oriented Control of induction motor with Fractional Proportional Integral (FPI), Integer Proportional Integral (IPI), and Adaptive Neuro-Fuzzy Inference System (ANFIS) controllers are all discussed in this work along with their designs and comparative analysis. The square of error was used as a fitness function to genetically optimize the FPI and IPI controller parameters. The suggested Adaptive Neuro-Fuzzy Inference System (ANFIS) controller uses a hybrid learning approach. It is trained by the FPI controller's input-output data. Using the results of MATLAB simulations under various operating situations, the performance of the ANFIS controller was compared with FPI and IPI controllers. Because of FPI controller includes an extra parameter for adjustment, namely integration order, it performed better than IPI controller for speed control of the induction motor. According to the simulation findings, the percentage peak overshoots while employing ANFIS, FPI, and IPI controllers were 0.495 %, 12.062 %, and 14.699 % respectively. As a result, ANFIS exhibits a drastic reduction in overshoot. Additionally, with the ANFIS controlled induction motor drive, the speed achieves the required set value at 0.14 s. For no load, constant, and changing loads, the induction motor drive's performance has been examined.

An Online Energy-Saving Control Allocation Strategy Based on Self-Updating Loss Estimation for Multi-Motor Drive Systems

In this paper, an online energy-saving control allocation strategy based on self-updating loss estimation for multi-motor drive systems is proposed, where the impact of variations in motor parameters and distribution coefficients is considered. Firstly, a drive system model for multi-motor drive systems incorporating iron loss in permanent magnet synchronous motor (PMSM) is established. Then, a self-updating PMSM loss estimation method based on dynamic torque–current mapping is proposed. The torque–current mapping is initially identified based on the conv-fusion curve, and iteratively updated by optimal estimation. Subsequently, an online control allocation method based on line search is proposed, which mitigates the adverse effects caused by variations in distribution coefficients and reduces the total motor loss. Finally, the effectiveness of the proposed strategy is verified on the hardware-in-the-loop (HIL)-based platform. The results demonstrate that the strategy effectively enhances energy efficiency while maintaining the original control performance of the system.

Design of Lumped Disturbance Observer in Current Loop of IPMSM Based on Recursive Integral Sliding Mode Surface

To overcome the problem of current control effect being reduced by unideal factors in a motor control system, such as motor parameter variation, inverter dead time, nonlinearity of the system, etc., a sliding mode disturbance observer for an interior permanent magnet synchronous motor is proposed in this paper. The model of an interior permanent magnet synchronous motor with unideal factors is designed, and the unideal factors are unified into lumped disturbances of motor stator voltage. Then, the observer for lumped disturbance is designed. A recursive integral sliding surface is used to replace the terminal sliding surface to avoid the noise sensitivity and singularity problem of the traditional terminal sliding mode observer. The observer can estimate the lumped disturbance of the current loop without relying on the accurate system model in finite time. Moreover, the structure of the current loop does not need to be adjusted while using the observer to observe and compensate for disturbances. Experiments are carried out to verify the effectiveness of the proposed observer.

Advanced Torque Control of Interior Permanent Magnet Motors for Electrical Hypercars

Nowadays, electric vehicles have gained significant attention as a promising solution to the environmental concerns associated with traditional combustion engine vehicles. With the increasing demand for high-performance hypercars, the need for advanced torque control strategies has become paramount. Field-Oriented Control using Current Vector Control represents a consolidated solution to implement torque control. However, this kind of control must take into account the DC link voltage variation and the variation of motor parameters depending on the magnets’ temperature while providing the maximum torque production for specific inverter current and voltage limitations. Multidimensional lookup tables are needed to provide a robust torque control from zero speed up to maximum speed under deep flux-weakening operation. Therefore, this article aims to explore the application of FOC 4D control in electrical hypercars and its impact on enhancing their overall performance and control stability. The article will delve into the principles underlying FOC 4D control and its advantages, challenges, and potential solutions to optimize the operation of electric hypercars. An electric powertrain model has been developed in the Simulink environment with the Simscape tool using a S-function block for the implementation of digital control in C-code. High-power electric motor electromagnetic parameters, derived from a Finite Element Method magnetic model, have been used in the simulation. The 4D LUTs have been computed from the motor flux maps and implemented in C-code in the S-function. The choice of FOC 4D control has been validated in the main load points of a hypercar application and compared to the conventional FOC. The final part of the research underlines the benefits of the FOC 4D on reliability, critical in motorsport applications.

An enhanced direct torque control strategy with composite controller for permanent magnet synchronous motor

AbstractDirect torque control (DTC) based on sliding mode control (SMC) has been widely applied to permanent magnet synchronous motor (PMSM) drives for torque ripple reduction and flux tracking. However, the control performance may be degraded due to disturbances such as variations in motor parameters or uncertainties. In this paper, a DTC strategy with an improved composite SMC controller is proposed for the PMSM drive system to enhance its performance under various disturbances. The speed controller is first designed based on a proposed adaptive reaching law (ARL) to eliminate chattering that appears in the conventional SMC, accelerate convergence, improve tracking performance, and reduce torque ripples. Then, an enhanced extended sliding mode disturbance observer (E2SMDO) is developed to estimate the lumped disturbances of the PMSM drive system in real time and compensate for a feedforward to the speed controller. By combining the ARL and E2SMDO, a composite controller (denoted ARL + E2SMDO) is established, whose stability is proven through the Lyapunov theory. The effectiveness of the proposed method is validated by simulations and experiments. The results of both simulations and experiments demonstrate that the composite ARL + E2SMDO controller has a fast dynamic response, reduced torque ripples, strong robustness, and good anti‐disturbance compared to other conventional methods.

Hybrid Flatness-Based Control of Dual Star Induction Machine Drive System for More Electrical Aircraft

Abstract This paper develops a precise method control system for tracking control of a power drive system based on a multi-phase machine under motor parameter and load torque variations. By adding a simple feedforward term based on the flatness theory, a conventional flux oriented control (FOC) can be enforced to have a perfect tracking performance under model parameter and load torque variations. Hence, a hybrid flatness-based control (HFBC) technique is applied to the control of a dual star induction machine (DSIM) and compared to a classical vector control strategy regarding tracking behaviour, robustness, and perturbations rejection. Finally, the simulation and experimental results are provided to verify the effectiveness of the proposed HFBC under uncertainties such as motor parameter and load torque variations. Furthermore, an enhancement of the drive system’s control performances is demonstrated by the improvement of the technique of separation of the objectives of tracking and disturbance rejection. The simulation and experimental results are presented, demonstrating the superiority of the HFBC.

Dual Motor Power Sharing Control for Electric Vehicles With Battery Power Management

Induction motor powertrains are emerging as a cost-effective option for high-power electric vehicles (EVs). A differential four-wheel drive (D4WD) EV based on two open end winding induction motor (OEWIM) propulsion is presented in this paper. Each OEWIM is driven by two 2-level voltage source inverters (VSI) with two isolated battery packs as the source. The proposed drive control algorithm integrates the uniform state-of-charge (SoC) distribution and fault-tolerant operation. A two-stage look-up table (LUT) based direct torque control (DTC) scheme is proposed to balance the SoC of batteries by selecting the suitable redundant VSI voltage vectors. The proposed LUT-DTC scheme is less sensitive to variations in motor parameters and provides excellent steady-state and transient performance. The results show the superiority of the proposed controller in terms of battery SoC balancing. The performance of the EV drive with the proposed LUT-DTC is validated by both simulation and laboratory experiments for the FTP75 and HFET driving cycles under different operating modes under normal and fault conditions. The dual motor driven D4WD with the proposed LUT-DTC is found to achieve 89.2% efficiency.

An Adaptive Delay Compensated Position Sensorless PMBLDC Motor Drive With Regenerative Braking for LEV Application

This paper deals with a new terminal voltage-based rotor position sensorless algorithm of permanent magnet brushless DC (PMBLDC) motor with adaptive phase angle compensation along with regenerative braking technique for light electric vehicle (LEV) application. Removal of Hall-Effect sensors from PMBLDC motor reduces the circuit complexity and makes the motor drive system robust and compact. Back EMF based position estimation of PMBLDC motor suffers poor accuracy at low speed. In this paper, position sensorless operation is achieved at very low speed below 100rpm. Position estimation of PMBLDC motor suffers with commutation error due to several factors like non-ideal back EMF, lowpass filter used, unbalanced DC bus voltage etc. An adaptive phase angle compensation strategy is developed to mitigate this issue, considering a generalized nonideal commutation case. This method is suitable for compensation of commutation error during variable speeds over a wide speed range, which is required for EV application. Moreover, the compensation method works with motor parameters variation as well. The proposed drive topology extends the vehicle's driving range with solar PV integration and energy regeneration algorithm. The system is simulated and experimentally validated in real case scenarios.

Torque Ripple Reduction of Nonsinusoidal Brushless DC Motor Based on Super-Twisting Sliding Mode Direct Power Control

In this paper, a second-order sliding mode control, based on super-twisting algorithm, is proposed for direct power control of the brushless DC (BLDC) motor. The proposed controller uses a super-twisting scheme that requires only sliding surface information and can handle system uncertainties and external disturbances, well. This scheme can improve the BLDC motor torque ripple by solving the disadvantages of the conventional sliding mode control method, such as the chattering effect and high frequency switching control. This method is simple and robust for the BLDC motor’s biggest challenge, torque ripple, which does not require any voltage and current control loops or complex reference frame transformations. The simulation results of the proposed method are compared with the direct power control (DPC) and model predictive control (MPC) methods, which indicate the superiority of the proposed method in both the steady- and transient-states. Moreover, the motor parameters variation in the tracking of active and reactive power are discussed. In addition, the practical results of the proposed method in both cases of speed and load variation show the effectiveness of this method in reducing power (torque) ripple and current total harmonic distortion (THD) and increasing the system’s efficiency compared to other methods.

Parametric Observer Controlled Motor Invariant Electronic Commutation of Photovoltaic Powered BLDCM Without Position Sensor

The development of parameter independent position sensor free control of permanent magnet brushless DC (PMBLDC) motor is one of the most challenging tasks. A motor parameter independent parametric observer based electronic commutation control is developed here without using rotor position sensor. The effect of motor parameter variation along with the change of motor, on electronic commutation control without using the rotor position sensor is considered as the disruption parameter for the developed control algorithm to mitigate the external disruption. Mitigation of control loop disturbances is done using the parametric observer based feedback control. A hybrid maximum power extraction algorithm using traverse and incremental conductance is implemented here for effective solar maximum power extraction. The implemented parametric observer motor invariant (POMI) based position sensor free control algorithm is verified using experimental process. Performance of the implemented POMI control is observed better in performance in comparison with the conventional position sensor free control as parametric variation is not considered in conventional control. The solution proves to be the best fit for any type of BLDC motor based water pumping solution irrespective of the pump configuration.

Model-Based Detection and Estimation of DC Offset of Phase Current Sensors for Field Oriented PMSM Drives

Failure of a phase current sensor in Permanent Magnet Synchronous Machine (PMSM) drive can highly impact the performance of the drive. Especially, offset faults can cause torque oscillations, which can lead to an unacceptable deterioration of the drive's performance and mechanical damages. The diagnosis of this fault condition is of fundamental importance in many applications and it is often solved by means of highly computational state estimation techniques. This paper presents a new model-based approach to systematically and accurately detect and estimate phase current sensor offsets in PMSM drives. The steady-state analytical solution of the PMSM drive model, including the control loop of the Field Oriented Control (FOC) and the sensor offset disturbance, has been found, allowing for estimating the effect of the fault on the electric drive performance. Then, a simple and effective diagnostic algorithm has been developed to detect, isolate and accurately estimate the location and the magnitude of the fault eliminating any need for state estimation or observers. Numerical and experimental results have been reported to validate the proposed model and diagnostic algorithm even in case of motor parameters variation.

A Synergistic Predictive Fusion Control Method and Application for Steering Feel Feedback of Steer-by-Wire System

In the steer-by-wire system of an intelligent vehicle, the steering feel feedback system provides accurate and real-time steering feel for a driver by precisely controlling the output torque of the steering feel feedback motor. The precision of output torque depends on the control effect of motor current, which is mainly affected by the key factors, including the dynamic performance of the control system, sampling error, and motor parameter variation. To provide accurate and real-time steering feel, how to design a control method considering the above influence factors has been an acknowledged challenging issue. To solve this problem, a synergistic predictive fusion (SPF) control method is proposed in this article. First, to improve the dynamic performance of the control system, the combined synergistic current control algorithm with the deadbeat predictive (DP) control principle and the synergistic predictive (SP) current control is proposed. Under this framework, a current correction algorithm is designed for the sampling error, and a weighting factor is obtained from the current ratio to adjust the weight of the sampling current. Meanwhile, considering the influence of parameter variation on current control error, an improved Adaline network parameter estimation algorithm is constructed to dynamically adjust motor parameters, and an input signal feedback adjustment step factor is added to enhance the parameter tracking ability. Finally, the simulation and test prove that using the proposed method can track steering feel more quickly (up to 10.61%) and more accurately (up to 13.56%) than using the traditional DP control method.

Current Sensor Fault Localization and Identification of PMSM Drives Using Difference Operator

The permanent magnet synchronous motor (PMSM) has received increasing attention in traction applications. For those closed-loop control systems, the current sensor is one of the most sensitive components, which determines the control performance and safety operation. Therefore, the fault diagnosis of current sensors is of great importance to realize fault tolerance and further maintenance. In this work, a novel fault localization and identification method of the current sensor is proposed for PMSM drives. The residual between the measured current and estimated currents based on a modified <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha \beta $ </tex-math></inline-formula> reference frame is employed for the fault sensors’ localization. Besides, a second-order difference operator is adopted to identify the fault types. Moreover, a function is defined for preventing misdiagnosis, thus enhancing the algorithm’s robustness. The algorithm for the fault type identification would not be affected by the variation of motor parameters, granting it high flexibility to combine with not only the fault location algorithm in this article but also with other model-based methods. The effectiveness of the proposed approach is experimentally validated on an 11-kW platform of the PMSM fed by a three-level T-type inverter.

Adaptive Nonlinear Control of Salient-Pole PMSM for Hybrid Electric Vehicle Applications: Theory and Experiments

This research work deals with the problem of controlling a salient-pole permanent-magnet synchronous motor (SP-PMSM) used in hybrid electric vehicles. An adaptive nonlinear controller based on the backstepping technique is developed to meet the following requirements: control of the reference vehicle speed in the presence of load variation and changes in the internal motor parameters while keeping the reliability and stability of the vehicle. The complexity of the control problem lies on the system nonlinearity, instability and the problem of inaccessibility to measure all the internal parameters, such as inertia, friction and load variation. For this issue, an adaptive backstepping regulator is developed to estimate these parameters. On the basis of formal analysis and simulation, as well as test results, it is clearly shown that the designed controller achieves all the goals, namely robustness and reliability of the controller, stability of the system and speed control, considering the uncertainty parameters’ measurements.

Sliding Mode Control for Wind Turbine Emulator based on Advanced Space Vector Modulation Technique for Two-Phase Induction Motor Drive

This paper proposes an improved Wind Turbine Emulator (WTE) structure using Sliding Mode Control (SMC) technique based on the Two-Phase Induction Motor (TPIM) for software simulation laboratory purposes. Since the power electronic converters application with various control strategies in the AC machine drive field facilitates the wind turbine characteristics imitation, an advanced SVM controlled-2ϕ-inverter is adopted. Considering the asymmetry between the TPIM main and auxiliary windings, mathematical equations were studied allowing control of the speed and the torque under different conditions. The 2-phase-inverter is controlled through an appropriate adjustment of four space voltage vectors. In order to enhance the speed and flux regulation loops and to increase the system robustness against the motor parameters variation and the wind sudden fluctuations, the SMC theory, which contains the adaptive rotor flux observer is used here to develop a control law that governs the system to track the reference speed set by the WT mathematical model to obtain the desired output. Several parameters, such as electromagnetic torque, rotor fluxes, stator currents, and rotor speed, were analyzed. Static and dynamic characteristics and the WTE transient and steady-state responses are satisfactorily reproduced by the proposed emulator scheme.

ANN Optimization of Weighting Factors Using Genetic Algorithm for Model Predictive Control of PMSM Drives

Model predictive control (MPC) has become one of the most attractive control techniques due to its outstanding dynamic performance for permanent magnet synchronous motor (PMSM) drives. However, the tuning of weighting factors for the cost function of MPC is a time-consuming procedure and weighting factors can significantly affect torque ripples of PMSM drives. For the purpose of optimizing the weighting factors in cost functions of MPC, this article proposes an artificial neural network (ANN) based method, which applies genetic algorithm as the back propagation algorithm. Since this method is trained offline, it does not increase any computation complexity of MPC. Consequently, any optimization targets that combine the performance metrics of MPC are defined and the optimal weighting factors for the given targets can be fast and precisely found through the proposed method. Besides, this method is robust to the variations of motor parameters like stator resistance and permanent magnet flux. The superiority of the proposed method over conventional ANN is evaluated by comparative simulations, in terms of current total harmonic distortion, tracking performance, and neutral-point potential balance. Finally, the feasibility and robustness of the proposed method are verified on the platform of PMSM drives fed by three-level T-type inverter.

Robust Design Optimization of a Permanent Magnet Motor from the System-Level Design Perspective

From the system-level design perspective, a robust design optimization method for a permanent magnetic motor is proposed to enhance the dynamic performance of a drive system while maintaining its steady performance. To achieve the goal, an elaborate numerical model for the whole drive system is first constructed by incorporating a control circuit simulator into a finite element analysis tool, and then the influence of motor parameter variations on transient system responses is investigated by means of the method of Taguchi experimental planning. A conventionally customized motor is optimized by the univariate dimension reduction method to ensure the robustness of system performances against manufacturing tolerances. Finally, a comparative performance analysis between two motor drive systems is provided to demonstrate the validity of the proposed method.

Nonlinear Identification of PMSM Rotor Magnetic Linkages Based on an Improved Extended Kalman Filter

The permanent magnet synchronous motor (PMSM) has complex nonlinear, strongly coupled characteristics and the variation of motor parameters makes its control more difficult. Therefore, parameter identification is of great significance for the stable operation of its closed-loop control system. In this paper, a method based on an improved extended Kalman filter (EKF) for the identification of the rotor flux ( ψ f ) of a permanent magnet synchronous motor is investigated for this nonlinear and strongly coupled model. Simulation results show that the method has a more fast convergence rate and more accurate identification result than traditional EKF algorithm.

Research on Fault-Tolerant Field-Oriented Control of a Five-Phase Permanent Magnet Motor Based on Disturbance Adaption

To ensure the high-quality output performance of the five-phase fault-tolerant permanent magnet synchronous motor (FTPMSM) drive system under normal and open-circuit faults and achieve the minimal reconfiguration of the FTPMSM control drive system under various open-circuit faults, in this paper, a fault-tolerant field-oriented control (FOC) strategy based on disturbance adaption is proposed. The speed-loop and current-loop steady-healthy controllers are designed to effectively suppress the torque ripples caused by open-circuit faults and improve the robustness of the drive system to load disturbance and motor parameter variation under fault operation. Moreover, the additional zero-sequence current controller can be omitted. In addition, the modified reduced-order coordinate transformation matrices are proposed to weaken the influence of oscillating neutral. Finally, the fault-tolerant FOC system of the FTPMSM is established, and its experiment is conducted. The experimental results verify the feasibility and effectiveness of the proposed control strategy.