Field Oriented Sliding Mode Control of Surface-Mounted Permanent Magnet AC Motors: Theory and Applications to Electrified Vehicles

Permanent magnet ac motors have been extensively utilized for adjustable-speed traction motor drives, due to their inherent advantages including higher power density, superior efficiency and reliability, more precise and rapid torque control, larger power factor, longer bearing, and insulation life-time. Without any proportional-and-integral (PI) controllers, this paper introduces novel first- and higher-order field-oriented sliding mode control schemes. Compared with the traditional PI-based vector control techniques, it is shown that the proposed field oriented sliding mode control methods improve the dynamic torque and speed response, and enhance the robustness to parameter variations, modeling uncertainties, and external load perturbations. While both first- and higher-order controllers display excellent performance, computer simulations show that the higher-order field-oriented sliding mode scheme offers better performance by reducing the chattering phenomenon, which is presented in the first-order scheme. The higher-order field-oriented sliding mode controller, based on the hierarchical use of supertwisting algorithm, is then implemented with a Texas Instruments TMS320F28335 DSP hardware platform to prototype the surface-mounted permanent magnet ac motor drive. Last, computer simulation studies demonstrate that the proposed field-oriented sliding mode control approach is able to effectively meet the speed and torque requirements of a heavy-duty electrified vehicle during the EPA urban driving schedule

Development and Implementation of Some Controllers for Performance Enhancement and Effective Utilization of Induction Motor Drive

The technological development in the field of power electronics and DSP technology is rapidly changing the aspect of drive technology. Implementations of advanced control strategies like field oriented control, linearization control, etc. to AC drives with variable voltage, and variable frequency source is possible because of the advent of high modulating frequency PWM inverters. The modeling complexity in the drive system and the subsequent requirement for modern control algorithms are being easily taken care by high computational power, low-cost DSP controllers. The present work is directed to study, design, development, and implementation of various controllers and their comparative evaluations to identify the proper controller for high-performance induction motor (IM) drives. The dynamic modeling for decoupling control of IM is developed by making the flux and torque decoupled. The simulation is carried out in the stationary reference frame with linearized control based on state-space linearization technique. Further, comprehensive and systematic design procedures are derived to tune the PI controllers for both electrical and mechanical subsystems. However, the PI-controller performance is not satisfactory under various disturbances and system uncertainties. Also, precise mathematical model, gain values, and continuous tuning are required for the controller design to obtain high performance. Thus, to overcome these drawbacks, an adapted control strategy based on Adaptive Neuro-Fuzzy Inference System (ANFIS) based controller is developed and implemented in real-time to validate different control strategies. The superiority of the proposed controller is analyzed and is contrasted with the conventional PI controller-based linearized IM drive. The simplified neuro-fuzzy control (NFC) integrates the concept of fuzzy logic and neural network structure like conventional NFC, but it has the advantages of simplicity and improved computational efficiency over conventional NFC as the single input introduced here is an error instead of two inputs error and change in error as in conventional NFC. This structure makes the proposed NFC robust and simple as compared to conventional NFC and thus, can be easily applied to real-time industrial applications. The proposed system incorporated with different control methods is also validated with extensive experimental results using DSP2812. The effectiveness of the proposed method using feedback linearization of IM drive is investigated in simulation as well as in experiment with different working modes. It is evident from the comparative results that the system performance is not deteriorated using proposed simplified NFC as compared to the conventional NFC, rather it shows superior performance over PI-controller-based drive. A hybrid fuel cell (FC) supply system to deliver the power demanded by the feedback linearization (FBL) based IM drive is designed and implemented. The modified simple hybrid neuro-fuzzy sliding-mode control (NFSMC) incorporated with the intuitive FBL substantially reduces torque chattering and improves speed response, giving optimal drive performance under system uncertainties and disturbances. This novel technique also has the benefit of reduced computational burden over conventional NFSMC and thus, suitable for real-time industrial applications. The parameters of the modified NFC is tuned by an adaptive mechanism based on sliding-mode control (SMC). A FC stack with a dc/dc boost converter is considered here as a separate external source during interruption of main supply for maintaining the supply to the motor drive control through the inverter, thereby reducing the burden and average rating of the inverter. A rechargeable battery used as an energy storage supplements the FC during different operating conditions of the drive system. The effectiveness of the proposed method using FC-based linearized IM drive is investigated in simulation, and the efficacy of the proposed controller is validated in real-time. It is evident from the results that the system provides optimal dynamic performance in terms of ripples, overshoot, and settling time responses and is robust in terms of parameters variation and external load

Fuzzy control system review

Overall intelligent control system which runs on fuzzy, genetic and neural algorithm is a promising engine for large –scale development of control systems . Its development relies on creating environments where anthropomorphic tasks can be performed autonomously or proactively with a human operator. Certainly, the ability to control processes with a degree of autonomy is depended on the quality of an intelligent control system envisioned. In this paper, a summary of published techniques for intelligent fuzzy control system is presented to enable a design engineer choose architecture for his particular purpose. Published concepts are grouped according to their functionality. Their respective performances are compared. The various fuzzy techniques are analyzed in terms of their complexity, efficiency, flexibility, start-up behavior and utilization of the controller with reference to an optimum control system condition

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

Stability analysis and speed control of brushless DC motor based on self-ameliorate soft switching control methods

In recent years, electric vehicles are the large-scale spread of the transportation field has led to the emergence of brushless direct current (DC) motors (BLDCM), which are mostly utilized in electrical vehicle systems. The speed control of a BLDCM is a subsystem, consisting of torque, flux hysteresis comparators, and appropriate switching logic of an inverter. Due to the sudden load torque variation and improper switching pulse, the speed of the BLDCM is not maintained properly. In recent research, the BLDC current control method gives a better way to control the speed of the motor. Also, the rotor position information should be the need for feedback control of the power electronic converters to varying the appropriate pulse width modulation (PWM) of the inverter. The proposed optimization work controls the switching device to manage the power supply BLDCM. In this proposed self-ameliorate soft switching (SASS) system is a simple and effective way for BLDC motor current control technology, a proposed control strategy is intended to stabilize the speed of the BLDCM at different load torque conditions. The proposed SASS system method is analyzing hall-based sensor values continuously. The suggested model is simulated using the MATLAB Simulink tool, and the results reveal that the maximum steady-state error value achieved is 4.2, as well as a speedy recovery of the BLDCM's speed

Modeling and control of a brushless DC motor

Permanent magnet brushless DC motors (PMBLDC) find wide applications in industries due to their high power density and ease of control. These motors are generally controlled using a three phase power semiconductor bridge. For starting and the providing proper commutation sequence to turn on the power devices in the inverter bridge the rotor position sensors required. Based on the rotor position, the power devices are commutated sequentially every 60 degrees. To achieve desired level of performance the motor requires suitable speed controllers. In case of permanent magnet motors, usually speed control is achieved by using proportional-integral(PI) controller. Although conventional PI controllers are widely used in the industry due to their simple control structure and ease of implementation, these controllers pose difficulties where there are some control complexity such as nonlinearity, load disturbances and parametric variations. Moreover PI controllers require precise linear mathematical models. This thesis presents a Fuzzy Logic Controller(FLC) for speed control of a BLDC by using. The Fuzzy Logic(FL) approach applied to speed control leads to an improved dynamic behavior of the motor drive system and an immune to load perturbations and parameter variations. The FLC is designed using based on a simple analogy between the control surfaces of the FLC and a given Proportional-Integral controller(PIC) for the same application. Fuzzy logic control offers an improvement in the quality of the speed response, compared to PI control. This work focuses on investigation and evaluation of the performance of a permanent magnet brushless DC motor (PMBLDC) drive, controlled by PI, and Fuzzy logic speed controllers. The Controllers are for the PMBLDC motor drive simulated using MATLAB soft ware package. Further, the PI controller has been implemented on an experimental BLDC motor set up

Performance analysis of interior permanent magnet synchronous motor (IPMSM) drive system using different speed controllers

The present research is indicating that the Permanent magnet motor drive could become serious competitor to the induction motor drive for servo application. Further, with the evolution of permanent magnet materials and control technology, the Permanent Magnet Synchronous Motor (PMSM) has become a pronounced choice for low and mid power applications such as computer peripheral equipments, robotics, adjustable speed drives and electric vehicles due to its special features like high power density, high torque/inertia ratio, high operating efficiency, variable speed operation, reliability, and low cost etc. Here we deals with the detailed modeling of an IPMSM drive system with Hybrid PI-Fuzzy logic controller (PI-FLC) as speed controller and Adaptive Hysteresis Current Controller as torque controller by controlling the current components of torque.In this thesis we deals with a simulation for speed control and improvement in the performance of a closed loop vector controlled IPMSM drive which employ two loops for better speed tracking and fast dynamic response during transient as well as steady state conditions by controlling the torque component of current. The outer loop employ Hybrid PIFuzzy logic controller (PI-FLC) while inner loop as Adaptive Hysteresis Band Current Controller (AHBCC) designed to reduce the torque ripple. Despite proportional plus Integral (PI) controller are usually preferred as speed controller due to its fixed gain (Kp) and Integral time constant (Ki), the performance of PI controller are affected by parameters variations, speed change and load disturbances in PMSM, due to which it results to unsatisfied operation under transient conditions. The drawbacks of PI controller are minimized using fuzzy logic controller (FLC). So for this a fuzzy control technique is also designed using mamdani type, triangular based 5x5 MFs and selecting the superior functionalities of PI and FLC, a Hybrid PI-FLC designed for effective speed control under transient and steady state condition.The complete viability of above mentioned integrated control strategy is implemented and tested in the MATLAB/Simulink environment and a performance comparison of proposed drive system with conventional PI, fuzzy logic controller and Hybrid PI-Fuzzy Logic Controller integrated separately as speed controller in terms of steady state and transient analysis with fixed step, variable step load and variable speed condition has been presented. Beside this a detailed comparative study of AHBCC is also done with Conventional Hysteresis Current Control(CHCC) scheme. The simulation circuits parameters for IPMSM, inverter, speed and current controllers of the drive system are given in Appendix-A

Low Speed Estimation of Sensorless DTC Induction Motor Drive Using MRAS with Neuro Fuzzy Adaptive Controller

This paper presents a closed loop Model Reference Adaptive system (MRAS) observer with artificial intelligent Nuero fuzzy controller (NFC) as the adaptation technique to mitigate the low speed estimation issues and to improvise the performance of the Sensorless Direct Torque Controlled (DTC) Induction Motor Drives (IMD). Rotor flux MRAS and reactive power MRAS with NFC is explored and detailed analysis is carried out for low speed estimation. Comparative analysis between rotor flux MRAS and reactive power MRAS with PI as well as NFC as adaptive controller is performed and results are presented in this paper. The comparative analysis among these four speed estimation methods shows that reactive power MRAS with NFC as adaptation mechanism shows reduced speed estimation error and actual speed error at steady state operating conditions when the drive is subjected to low speed operation. Simulation carried out using MATLAB-Simulink software to validate the performance of the drive especially at low speeds with rated and variable load conditions

A New Fixed Switching Frequency Direct Torque Controlled PMSM Drives with Low Ripple in Flux and Torque

Direct Torque Control (DTC) has gained popularity for development of advanced motor control due to its simplicity and offers fast instantaneous torque and flux controls. However, the conventional DTC which is based on hysteresis controller has major drawbacks, namely high torque ripple and variable inverter switching frequency. This paper presents an improved switching strategy for reducing flux and torque ripples in DTC of PMSM drives; wherein the torque hysteresis controller and the look-up table used in the conventional DTC are replaced with a constant frequency torque controller (CFTC) and an optimized look-up table, respectively. It can be shown that a constant switching frequency is established due to the use of the CFTC while the reduction of torque and flux ripples is achieved mainly because of the selection of optimized voltage vector (i.e. with an optimized look-up table). This paper also will explain the construction of DTC schemes implemented using MATLAB-Simulink blocks. Simulation results were shown that a significant reduction of flux and torque ripples which is about 90% can be achieved through the proposed DTC scheme