An Improved Proportional Integral Estimator of the Stator Resistance for a Direct Torque Controlled Induction Motor

In this paper, an improved PI （proportional integral） stator resistance estimation for a DTC （direct torque controlled） induction motor is proposed. This estimation method is based on an on-line stator resistance correction regarding the variations of the stator current estimation error. In fact, the input variable of the P1 estimator is the stator current estimation error. The main idea is to tune accurately the stator resistance value relatively to the evolution of the stator current estimation error gradient to avoid the drive instability and ensure the tracking of the actual value of the stator resistance. But there is an unavoidable steady state error between the filtered stator current modulus and its estimated value from the dq model of the machine which is due to pseudo random commutations of the inverter switches. This may deteriorate the performance of the proposed fuzzy stator resistance estimator. An offset has been introduced in order to overcome this problem, for different speed command values and load torques. Simulation results show that the proposed estimator was able to successfully track the actual value of the stator resistance lbr different operating conditions.

Genetic algorithm and particle swarm optimization tuned fuzzy PID controller on direct torque control of dual star induction motor

This study presents analysis, control and comparison of three hybrid approaches for the direct torque control (DTC) of the dual star induction motor (DSIM) drive. Its objective consists of combining three different heuristic optimization techniques including PID-PSO, Fuzzy-PSO and GA-PSO to improve the DSIM speed controlled loop behavior. The GA and PSO algorithms are developed and implemented into MATLAB. As a result, fuzzy-PSO is the most appropriate scheme. The main performance of fuzzy-PSO is reducing high torque ripples, improving rise time and avoiding disturbances that affect the drive performance.

A Novel Fuzzy Direct Torque Control System for Three-level Inverter-fed Induction Machine

Diode clamped multi-level inverter （DCMLI） has a wide application prospect in high-voltage and adjustable speed drive systems due to its low stress on switching devices, low harmonic output, and simple structure. However, the problem of complexity of selecting vectors and capacitor voltage unbalance needs to be solved when the algorithm of direct torque control （DTC） is implemented on DCMLI. In this paper, a fuzzy DTC system of an induction machine fed by a three-level neutral-point-clamped （NPC） inverter is proposed. After introducing fuzzy logic, optimal selecting switching state is realized by applying various strategies which can distinguish the grade of the errors of stator flux linkage, torque, the neutral-point potential, and the position of stator flux linkage. Consequently, the neutral-point potential unbalance, the dr/dr of output voltage and the switching loss are restrained effectively, and desirable dynamic and steady-state performances of induction machines can be obtained for the DTC scheme. A design method of the fuzzy controller is introduced in detail, and the relevant simulation and experimental results have verified the feasibility of the proposed control algorithm.

Fuzzy-second order sliding mode control optimized by genetic algorithm applied in direct torque control of dual star induction motor

The direct torque control of the dual star induction motor(DTC-DSIM) using conventional PI controllers is characterized by unsatisfactory performance, such as high ripples of torque and flux, and sensitivity to parametric variations. Among the most evoked control strategies adopted in this field to overcome these drawbacks presented in classical drive, it is worth mentioning the use of the second order sliding mode control(SOSMC) based on the super twisting algorithm(STA) combined with the fuzzy logic control(FSOSMC). In order to realize the optimal control performance, the FSOSMC parameters are adjusted using an optimization algorithm based on the genetic algorithm(GA). The performances of the envisaged control scheme, called G-FSOSMC, are investigated against G-SOSMC, G-PI and BBO-FSOSMC algorithms. The proposed controller scheme is efficient in reducing the torque and flux ripples, and successfully suppresses chattering. The effects of parametric uncertainties do not affect system performance.

A novel fuzzy logic direct torque controller for a permanent magnet synchronous motor with a field programmable gate array

A high-performance digital servo system built on the platform of a field programmable gate array (FPGA),a fully digitized hardware design scheme of a direct torque control (DTC) and a low speed permanent magnet synchronous motor (PMSM) is proposed. The DTC strategy of PMSM is described with Verilog hardware description language and is employed on-chip FPGA in accordance with the electronic design automation design methodology. Due to large torque ripples in low speed PMSM,the hysteresis controller in a conventional PMSM DTC was replaced by a fuzzy controller. This FPGA scheme integrates the direct torque controller strategy,the time speed measurement algorithm,the fuzzy regulating technique and the space vector pulse width modulation principle. Experimental results indicate the fuzzy controller can provide a controllable speed at 20 r min-1 and torque at 330 N m with satisfactory dynamic and static performance. Furthermore,the results show that this new control strategy decreases the torque ripple drastically and enhances control performance.

Direct-Torque Neuro-Fuzzy Control of Induction Motor

Fuzzy systems are currently being used in a wide field of industrial and scientific applications.Since the design and especially the optimization process of fuzzy systems can be very time consuming,it is convenient to have algorithms which construct and optimize them automatically.In order to improve the system stability and raise the response speed,a new control scheme,direct-torque neuro-fuzzy control for induction motor drive,was put forward.The design and tuning procedure have been described.Also,the improved stator flux estimation algorithm,which guarantees eccentric estimated flux has been proposed.

Model Predictive Direct Torque Control of Permanent Magnet Synchronous Motor (PMSM) with Online Parameter Estimation Based on Extended Kalman Filter

Aiming at the torque and flux ripples in the direct torque control and the time-varying parameters for permanent magnet synchronous motor (PMSM), a model predictive direct torque control with online parameter estimation based on the extended Kalman filter for PMSM is designed. By predicting the errors of torque and flux based on the model and the current states of the system, the optimal voltage vector is selected to minimize the error of torque and flux. The stator resistance and inductance are estimated online via EKF to reduce the effect of model error and the current estimation can reduce the error caused by measurement noise. The stability of the EKF is proved in theory. The simulation experiment results show the method can estimate the motor parameters, reduce the torque, and flux ripples and improve the performance of direct torque control for permanent magnet synchronous motor (PMSM).

Novel ARC-Fuzzy Coordinated Automatic Tracking Control of Four-Wheeled Mobile Robot

Four-wheeled,individual-driven,nonholonomic structured mobile robots are widely used in industries for automated work,inspection and explora-tion purposes.The trajectory tracking control of the four-wheel individual-driven mobile robot is one of the most blooming research topics due to its nonholonomic structure.The wheel velocities are separately adjusted to follow the trajectory in the old-fashioned kinematic control of skid-steered mobile robots.However,there is no consideration for robot dynamics when using a kinematic controller that solely addresses the robot chassis’s motion.As a result,the mobile robot has lim-ited performance,such as chattering during curved movement.In this research work,a three-tiered adaptive robust control with fuzzy parameter estimation,including dynamic modeling,direct torque control and wheel slip control is pro-posed.Fuzzy logic-based parameter estimation is a valuable tool for adjusting adaptive robust controller(ARC)parameters and tracking the trajectories with less tracking error as well as high tracking accuracy.This research considers the O type and 8 type trajectories for performance analysis of the proposed novel control technique.Our suggested approach outperforms the existing control methods such as Fuzzy,proportional–integral–derivative(PID)and adaptive robust controller with discrete projection(ARC–DP).The experimental results show that the scheduled performance index decreases by 2.77%and 4.76%.All the experimen-tal simulations obviously proved that the proposed ARC-Fuzzy performed well in smooth groud surfaces compared to other approaches.

Artificial Intelligence Application’s for 4WD Electric Vehicle Control System

A novel speed control design of 4WD electric vehicle (EV) to improve the comportment and stability under different road constraints condition is presented in this paper. The control circuit using intelligent adaptive fuzzy PI controller is proposed. Parameters which guide the functioning of PI controller are dynamically adjusted with the assistance of fuzzy control. The 4WD is powered by four motors of 15 kilowatts each one, delivering a 384 N.m total torque. Its high torque (338 N.m) is instantly available to ensure responsive acceleration performance in built-up areas. The electric drive canister of tow directing wheels and tow rear propulsion wheels equipped with tow induction motors thanks to their light weight simplicity and their height performance. Acceleration and steering are ensure by electronic differential, the latter control separately deriving wheels to turn at any curve. Electric vehicle are submitted different constraint of road using direct torque control. Electric vehicle are simulated in MATLAB SIMULINK. The simulation results have proved that the intelligent fuzzy PI control method decreases the transient oscillations and assure efficiency comportment in all topologies road constraints, straight, curved road, descent.

Comparative Study of the DTC-IM Speed Controller Based on Artificial Intelligence

Recently, artificial intelligence technique is increasingly receiving attention in solving complex and practical problem and they are widely applying in electrical machine domain. The authors consider also the direct torque control （DTC） as an alternative to conventional methods of control by pulse width modulation （PWM） and by Field oriented control （FOC）, so the application of the DTC based on artificial intelligence can show more advantages and simplified control algorithms with high performance. For this reason, the objectives of this paper can be divided into two parts, the first part is to apply neural networks and fuzzy logic techniques to the DTC control in the presence of a loop speed control comparing to the conventional regulators （as PI） to show the feasibility of these approaches, the second part is to further improve the performance of the neural network by using a neural-fuzzy regulator which combine the advantages of two techniques. Simulation results confirm the validity of the proposed techniques.

FUZZY+DTC在张力控制中的应用实践

针对张力控制中普遍存在的问题,以一台铜拉线机的电气设备技术改造为载体,介绍了一种有别于同类其它设备直接检测张力,然后再施加相应控制措施的方案,两台交流电机的转速同步调节分阶段进行。升、降速期间,采用直接转矩控制,避开了速度调节的大惯性常数,快捷、高效;平稳运行期间,通过检测线速差,进行模糊控制,避开了因时变、非线性等造成的建模困难,稳定、实用。实践运行结果表明:系统跟踪快捷,运行可靠,具有良好的推广应用价值。

定子电阻在线Fuzzy观测器的设计

在直接力矩控制系统中,电机定子电阻值在运行过程中一直是变化的,从而影响了系统的低速性能.本文就这一问题进行了讨论.并设计了定子电阻在线Fuzzy观测器.从而为改善系统的低速性能奠定基础.

Comparative study of various artificial intelligence approaches applied to direct torque control of induction motor drives

In this paper, three intelligent approaches were proposed, applied to direct torque control （DTC） of induction motor drive to replace conventional hysteresis comparators and selection table, namely fuzzy logic, artificial neural network and adaptive neuro-fuzzy inference system （ANFIS）. The simulated results obtained demonstrate the feasibility of the adaptive network-based fuzzy inference system based direct torque control （ANFIS-DTC）. Compared with the classical direct torque control, fuzzy logic based direct torque control （FL-DTC）, and neural networks based direct torque control （NN- DTC）, the proposed ANFIS-based scheme optimizes the electromagnetic torque and stator flux ripples, and incurs much shorter execution times and hence the errors caused by control time delays are minimized. The validity of the proposed methods is confirmed by simulation results.

Sliding mode approach applied to sensorless direct torque control of cage asynchronous motor via multi-level inverter

To improve the robustness and performance of the dynamic response of a cage asynchronous motor,a direct torque control(DTC)based on sliding mode control(SMC)is adopted to replace traditional proportional-integral(PI)and hysteresis comparators.The combination of the proposed strategy with sinusoidal pulse width modulation(SPWM)applied to a three-level neutral point clamped(NPC)inverter brings many advantages such as a reduction in harmonics,and precise and rapid tracking of the references.Simulations are performed for a three-level inverter with SM-DTC,a two-level inverter with SM-DTC and the three-level inverter with PI-DTC-SPWM.The results show that the SM-DTC method achieves better performance in terms of reference tracking,while adoption of the threelevel inverter topology can effectively reduce the ripples.Applying the SM-DTC to the three-level inverter presents the best solution for achieving efficient and robust control.In addition,the use of a sliding mode speed estimator eliminates the mechanical sensor and this increases the reliability of the system.

Modern improvement techniques of direct torque control for induction motor drives - a review

Conventional direct torque control (DTC) is one of the excellent control strategies available to control the torque of the induction machine (IM). However, the low switching frequency of the DTC causes high ripples in the flux and torque that leads to an acoustic noise which degrades the control performances, especially at low speeds. Many direct torque control techniques were appeared to remedy these problems by focusing specifically on the torque and flux. In this paper, a state of the art review of various modern techniques for improving the performance of DTC control is presented. The objective is to make a critical analysis of these methods in terms of ripples reduction, tracking speed, switching loss, algorithm complexity and parameter sensitivity. Further, it is envisaged that the information presented in this review paper will be a valuable gathering of information for academic and industrial researchers.

Electromagnetic Torque Estimation of a Three-Phase Induction Motor for Setting the Feedrate of a Milling Machine

In this paper, it presents a project of a fuzzy controller and a neural estimator to control a coordinate table powered by three-phase induction motor, aiming to implement an intelligent milling system. The position/speed control is performed using vector techniques of three-phase induction machines. The estimation of the motor electromagnetic torque is used for setting the feedrate of the table. The speed control is developed using TS （Takagi-Sugeno） fuzzy logic model and electromagnetic torque estimation using neural network type LMS （least mean square） algorithm. The induction motor is powered by a frequency inverter driven by a DSP （digital signal processor）. Control strategies are implemented in DSP. Simulation results are presented for evaluating the performance of the system.

无速度传感器的异步电动机模糊自适应PI直接转矩控制

文章针对传统无速度传感器直接转矩控制转速不稳定、超调量大等问题做出两点改进。首先,引入自耦PI对传统MRAS无速度传感器直接转矩控制进行改进,该方法改善无传感器直接转矩控制调速性能、降低超调量、减小稳态误差;其次,针对直接转矩控制具有转矩脉动大、调速性能差等缺点,提出变论域静态模糊自适应PI控制策略,减小转矩脉动并改善调速性能,大大提升了抗负载能力;最后,在Matlab/Simulink仿真环境下进行了仿真并进行分析,证明了上述方法的有效性和可行性。结果表明,相对于传统的无速度传感器直接转矩控制系统,仿真结果显示减小了转速超调量,缩短了转速上升中到达高速工况的响应时间,提升了抗负载能力。

基于模糊空间矢量调制直接转矩控制技术研究

针对SVM-DTC系统电机中低速运行存在电磁转矩脉动大的问题,提出了一种基于模糊SVM-DTC方法。通过优化电压空间矢量合成方法,细化电压空间矢量参数表,构建模糊磁链控制器模型和模糊转矩控制器模型,并将2个模糊控制器模型替换到SVM-DTC系统的PI控制器模型。通过理论分析和仿真对比模糊SVM-DTC模型和SVM-DTC模型,验证了模糊SVM-DTC系统的优越性。结果表明,交流电机在中低转速下,模糊SVM-DTC电机电流平滑,定子磁链光滑,电磁转矩脉动变小,电机转速平稳。

基于模糊自适应PID的SRM直接转矩控制系统研究

针对传统模糊控制器在控制过程中存在超调量大、控制精度不高以及静差等问题,提出了一种基于优化模糊自适应PID的开关磁阻电机控制方法。利用改进蝙蝠算法对模糊控制器进行优化,以外环模糊自适应PID控制器的输出变量为内环控制系统的参考转矩,送入直接转矩控制系统。仿真结果表明,经过算法优化的模糊自适应PID控制器在动态性能和鲁棒性方面明显优于传统模糊PID控制器,成功克服了模糊控制中存在的稳态误差和抗干扰能力不足的问题,有效解决了系统上升时间和超调之间的矛盾,同时显著减少了电机转矩的脉动。

基于模糊控制调节电压矢量作用时间策略的永磁同步电机直接转矩控制仿真研究

讨论了永磁同步电机直接转矩控制转矩脉动问题,针对永磁同步电机直接转矩控制存在较大脉动转矩的缺点,提出了一种模糊调节电压矢量作用时间的永磁同步电机直接转矩控制策略。即在常规永磁同步电机直接转矩控制系统的基础上,根据转矩偏差和转矩偏差的变化率,模糊调节电压矢量作用时间,以减小转矩脉动。仿真结果表明基于模糊调节电压矢量作用时间策略的永磁同步电机直接转矩控制的脉动转矩大大地减小。