ANFIS based multi-sector space vector PWM scheme for sensorless BLDC motor drive

This paper presents a design and development of Multi Sector Space Vector Pulse Width Modulation scheme (MS-SVPWM) for the speed control of brushless direct current (BLDC) motor drive. This control scheme is developed to enhance the performance of BLDC under wide range of speed and load variation. The hardware prototype is developed for 400 W, 30 V, 3000 rpm BLDC motor. The drive consists of uncontrolled rectifier unit for providing DC source to inverterunit. The proposed drive control has been done by implementing MS-SVPWM scheme using ANFIS control. The main function of ANFIS controller is to select the suitable sector for the drive and also predict the mismatching pulses by comparing conventional SVPWM and MS-SVPWM switching patterns. This new switching control technique helps to reduce switching losses of inverter and also improves an efficiency of BLDC system. This MS-SVPWM reduces the DC voltage ripple; Total Harmonic Distortion (THD) and torque ripple to the standard level. To verify and validate the practicality of the proposed system, the simulation is first performed using MATLAB Simulink tool. The hardware system is developed for the MS-SVPWM using DSPIC30F4011controller, the simulation and experimental results are presented.     

Improved torque performance in BLDC-motor-drive through Jaya optimization implemented on Xilinx platform

Robust dynamic speed response with less maintenance and efficient operation makes the Brushless Direct Current (BLDC) motors as an oblivious choice for many motoring applications which needs instantaneous speed control while developing high torque. This paper proposes Jaya optimization Algorithm on Xilinx platform for BLDC Motor and Fractional Order Proportional Integral Derivative controller to improve its efficiency through minimizing the ripples present in the Torque of the motor. The control strategy of torque controller works with the combinations of Xilinx and optimization algorithms. In this proposed controller, the strategy of control is achieved after deriving the BLDC motor's dynamic mode there after the electronic interaction with BLDC motor is enrooted with the Math lab/Simulink model while tactically utilizing Xilinx tools for all kind of co-simulation in the same model. Initially the dynamic model of a BLDC motor drive is derived and the control topology is designed with the help of the proposed controller. The controlling mechanism established in this paper is effective and efficient in minimizing the ripples present in the torque of the motor. The proposed controller is utilized for possible low-cost and high-performance industrial applications. The results are obtained and analyzed with existing methodologies and it shows that the proposed technique outperforms the existing models.     

Speed control of Brushless DC motor using bat algorithm optimized Adaptive Neuro-Fuzzy Inference System

In this paper, speed control of Brushless DC motor using Bat algorithm optimized online Adaptive Neuro-Fuzzy Inference System is presented. Learning parameters of the online ANFIS controller, i.e., Learning Rate (η), Forgetting Factor (λ) and Steepest Descent Momentum Constant (α) are optimized for different operating conditions of Brushless DC motor using Genetic Algorithm, Particle Swarm Optimization, and Bat algorithm. In addition, tuning of the gains of the Proportional Integral Derivative (PID), Fuzzy PID, and Adaptive Fuzzy Logic Controller is optimized using Genetic Algorithm, Particle Swarm Optimization and Bat Algorithm. Time domain specification of the speed response such as rise time, peak overshoot, undershoot, recovery time, settling time and steady state error is obtained and compared for the considered controllers. Also, performance indices such as Root Mean Squared Error, Integral of Absolute Error, Integral of Time Multiplied Absolute Error and Integral of Squared Error are evaluated and compared for the above controllers. In order to validate the effectiveness of the proposed controller, simulation is performed under constant load condition, varying load condition and varying set speed conditions of the Brushless DC motor. The real time experimental verification of the proposed controller is verified using an advanced DSP processor. The simulation and experimental results confirm that bat algorithm optimized online ANFIS controller outperforms the other controllers under all considered operating conditions.     

HIERARCHAL CONTROL SYSTEM FOR A VARIABLE SPEED CAGE MACHINE WIND GENERATION UNIT USING NEURAL NETWORKS

A hierarchal control strategy, that addresses three control objectives for a wind generation system, is proposed in this paper. It controls the local bus voltage (to avoid voltage rise), captures the maximum power in the wind and also minimizes the power loss in the induction generator. In the first level, given the instantaneous wind speed, electrical torque and output power, the designed neural networks calculate the desired rotor speed, air‐gap flux and the grid side reactive power. In the second level, the desired current wave shapes (instantaneous three‐phase currents) of the rectifier and the inverter in a double‐sided PWM converter system are calculated. In the third level, the PWM controller guides the system towards the optimum operating conditions. Simulation results show that even as the wind speed changes randomly, the proposed control strategy leads the system to the optimum operating conditions.     

The intelligent integrated speed controller of DTC for induction motor

The parameters selection of proportional coefficient and integral coefficient (PI) for speed controller is important for direct torque control system. However, it is difficult to adjust these parameters. In this paper, firstly, we use particle swarm optimization to search the appropriate PI values of the speed controller. Secondly, based on the optimized PI parameters, the fuzzy-PI speed control strategy is presented to solve the poor self-adaptability problem. Thus, the proportional coefficient k _p and integral coefficient k _i can be adjusted dynamically to adapt to the speed variations. And finally, to obtain the high-speed parallel processing ability, the well-trained RBF neural network replaces the fuzzy-PI speed controller. The comparison with conventional PI speed controller shows that the proposed intelligent integrated speed controller brings good benefits of fast speed response and good stability and reduces the torque ripple. The validity of the proposed intelligent integrated speed controller is verified by the simulation results.     

Hybrid control for speed sensorless induction motor drive

The dynamic response of a hybrid-controlled speed sensorless induction motor (IM) drive is introduced. First, an adaptive observation system, which comprises speed and flux observers, is derived on the basis of model reference adaptive system (MRAS) theory. The speed observation system is implemented using a digital signal processor (DSP) with a high sampling rate to make it possible to achieve good dynamics. Next, based on the principle of computed torque control, a computed torque controller using the estimated speed signal is developed. Moreover, to relax the requirement of the lumped uncertainty in the design of a computed torque controller, a recurrent fuzzy neural network (RFNN) uncertainty observer is utilized to adapt the lumped uncertainty online. Furthermore, based on Lyapunov stability a hybrid control system, which combines the computed torque controller, the RFNN uncertainty observer and a compensated controller, is proposed to control the rotor speed of the sensorless IM drive. The computed torque controller with RFNN uncertainty observer is the main tracking controller and the compensated controller is designed to compensate the minimum approximation error of the uncertainty observer instead of increasing the rules of the RFNN. Finally, the effectiveness of the proposed observation and control systems is verified by simulated and experimental results     

Speed control of SRM supplied by photovoltaic system via ant colony optimization algorithm

This paper proposes a speed control of switched reluctance motor supplied by photovoltaic system. The proposed design of the speed controller is formulated as an optimization problem. Ant colony optimization (ACO) algorithm is employed to search for the optimal proportional integral (PI) parameters of the proposed controller by minimizing the time domain objective function. The behavior of the proposed ACO has been estimated with the behavior of genetic algorithm (GA) in order to prove the superior efficiency of the proposed ACO in tuning PI controller over GA. Also, the behavior of the proposed controller has been estimated with respect to the change of load torque, variable reference speed, ambient temperature and radiation. Simulation results confirm the better behavior of the optimized PI controller based on ACO compared with optimized PI controller based on GA over a wide range of operating conditions.

     

Compensation of torque ripple in high performance BLDC motor drives

Brushless DC motor drives (BLDC) are finding expanded use in high performance applications where torque smoothness is essential. The nature of the square-wave current excitation waveforms in BLDC motor drives permits some important system simplifications compared to sinusoidal permanent magnet AC (PMAC) machines. However, it is the simplicity of the BLDC motor drive that is responsible for causing an additional source of ripple torque commonly known as commutation torque to develop. In this paper, a compensation technique for reducing the commutation torque ripple is proposed. With the experimental results, the proposed method demonstrates the effectiveness for a control system using the BLDC motors that requires high speed and accuracy.     

基于ML4428的无刷直流电机无传感器的控制

介绍了目前常用的无位置传感器无刷直流电动机的控制策略,分析了反电势检测换相原理以及ML4428的功能、参数设定及应用。ML4428无传感器平滑启动无刷直流电机PWM控制器是一种较理想的无刷直流电机驱动器件。     

Predictive direct torque control with reduced ripples for induction motor drive based on T‐S fuzzy speed controller

Finite‐state model predictive control (FS‐MPC) has been widely used for controlling power converters and electric drives. Predictive torque control strategy (PTC) evaluates flux and torque in a cost function to generate an optimal inverter switching state in a sampling period. However, the existing PTC method relies on a traditional proportional‐integral (PI) controller in the external loop for speed regulation. Consequently, the torque reference may not be generated properly, especially when a sudden variation of load or inertia takes place. This paper proposes an enhanced predictive torque control scheme. A Takagi‐Sugeno fuzzy logic controller replaces PI in the external loop for speed regulation. Besides, the proposed controller generates a proper torque reference since it plays an important role in cost function design. This improvement ensures accurate tracking and robust control against different uncertainties. The effectiveness of the presented algorithms is investigated by simulation and experimental validation using MATLAB/Simulink with dSpace 1104 real‐time interface.     

A study on the improvement of the cam phase control performance of an electric continuous variable valve timing system using a cycloid reducer and BLDC motor

This paper discusses the control performance improvement for an electric-continuous variable valve timing (E-CVVT) system using a brushless direct current (BLDC) motor and cycloid reducer. Each component of the E-CVVT system was implemented with mathematical analysis, and the response performance of the E-CVVT system was determined based on the mathematical model of the cam shaft motion, cam profile, cycloid reducer, BLDC motor, and controller. To control the intake valve timing of the engine, a cycloid speed reducer with a high reduction ratio capable of amplifying the output torque of a small BLDC motor was implemented. The change in valve speed due to the rotation of the cam shaft was represented by the curves described by the vertical movement of the valve using the cam profile. A control performance test apparatus was constructed and the torque of the intake cam shaft was measured and applied to the analysis so that the phase of the cam shaft could be changed using the E-CVVT system. To analyze the operating characteristics of the E-CVVT system, the BLDC motors were modeled using Simulink. The E-CVVT system controls the phase angle of the intake cam shaft. When the E-CVVT system sets the target phase angle, the motor controller generates the optimal motor speed command. The intake cam phase response speed depends on the setting of each PID parameter that changes the phase of the cam shaft. Through analysis and vehicle-based experiments, we confirmed the improvement of the E-CVVT system response performance according to the change of the PID parameter.

     

Stability analysis and implementation of chopper fed DC series motor with hybrid PID-ANN controller

The attempt is made to enhance the performance of a closed loop control of DC series motor fed by DC chopper (DC-DC buck converter) by hybridization of PID controller with an intelligent control using ANN (Artificial Neural Network) controller. This system consists of inner current controller loop and outer PID-ANN based speed controller loop. The current controller allows the PWM (Pulse Width Modulation) signal when the motor current is less than set value. The PID-ANN speed controller controls the motor voltage by controlling the duty cycle of the chopper thereby the motor speed is regulated. The PID-ANN controller performances are analyzed in both steady state and dynamic operating condition with various set speed and various load torque. The rise time, maximum over shoot, settling time, steady state error and speed drops are taken for comparison with conventional PID controller and existing work. The steady state stability analysis of the system also is made by using the transfer function model with MATLAB. The training data for PID-ANN controller is taken from conventional PID controller. The Hybrid PID-ANN controller with DC chopper has better control over the conventional PID controller and the reported existing work. This system is simulated using MATLAB/Simulink and also it is implemented with a NXP 80C51 family Microcontroller (P89V51RD2 BN) based Embedded System.     

基于MATLAB的无刷直流电机调速系统的建模与仿真

从无刷直流电机的数学模型出发,提出了一种无刷直流电机控制系统仿真建模的方法.该方法在Matlab/Simulink中,把独立的功能模块和s函数相结合,构建了无刷直流电机系统的仿真模型.系统采用双闭环控制:速度环采用PID控制,根据滞环电流跟踪型PWM逆变器原理实现电压控制.利用该模型分析了电机的动静态性能,得到了电机运行时的转速、转矩、相电流和反电动势曲线.仿真结果与理论分析一致,验证了该方法的合理性和有效性,为实际电机控制系统的设计和调试提供了新的思路.     

直驱小惯量表贴式永磁同步电机的转速脉动抑制

非线性电流测量误差和电压源逆变器(VSI)非线性畸变电压造成了直驱小惯量表贴式永磁同步电机(SPMSN)的转速脉动.本文将q轴非线性电流测量误差等效为扰动负载电流,提出一种复合PI(CPI)调速器抑制电机转速脉动.该调速器由传统PI调节器与偏差补偿器并联构成,偏差补偿器用以抑制非线性负载电流.同时,用分段线性函数建立IGBT关闭时间模型,并推导了VSI非线性畸变电压表达式.引入积分型模型预测控制(MPC)作为电流环控制器,利用MPC的滚动时域最优预测特性抑制VSI的非线性畸变电压,消除了零电流钳位现象.最后,通过仿真分析验证了所提控制策略的有效性.     

用于永磁同步电机周期性转速脉动抑制的重复控制

本文针对电流测量误差、逆变器死区效应等非理想因素造成永磁同步电机转速周期性脉动的问题,在转速外环设计插入式转速自适应重复控制器,实现对电机转速的平滑稳定控制.首先,分析各种非理想因素引起永磁同步电机稳态转速周期性脉动的机理.其次,为保证附加转速自适应重复控制器后的系统稳定性,设计零相位FIR低通滤波器、线性相位补偿器和...     

Open-loop neuro-fuzzy speed estimator applied to vector and scalar induction motor drives

Scalar and vector drives have been the cornerstones of control of industrial motors for decades. In both the elimination of mechanical speed sensor consists in a trend of modern drives. This work proposes the development of an adaptive neuro-fuzzy inference system (ANFIS) angular rotor speed estimator applied to vector and scalar drives. A multi-frequency training of ANFIS is proposed, initially for a V/f scheme and after that a vector drive with magnetizing flux oriented control is proposed. In the literature ANFIS has been commonly proposed as a speed controller in substitution of the classical PI controller of the speed control loop. This paper investigates the ANFIS as an open-loop speed estimator instead. The subtractive clustering technique was used as strategy for generating the membership functions for all the incoming signal inputs of ANFIS. This provided a better analysis of the training data set improving the comprehension of the estimator. Additionally the subtractive cluster technique allowed the training with experimental data corrupted by noise improving the estimator robustness. Simulations to evaluate the performance of the estimator considering the V/f and vector drive system were realized using the Matlab/Simulink^® software. Finally experimental results are presented to validate the ANFIS open loop estimator.     

基于滑模模型参考自适应观测器的无速度传感器三相永磁同步电机模型预测转矩控制

针对三相永磁同步电机(PMSM)驱动系统,基于滑模变结构模型参考自适应(MRAS)技术,提出了一种新颖的无速度传感器模型预测转矩控制(MPTC)策略.采用滑模变结构模型参考自适应方法构造电机转速观测器,以改善速度估计精度并提高系统鲁棒性;利用模型预测转矩控制策略,以达到减小转矩和磁链纹波并提高系统控制性能的目的.仿真结果表明:就滑模MRAS观测器与MRAS观测器比较而言,基于前者的PMSM无速度传感器MPTC系统比基于后者的PMSM无速度传感器MPTC系统具有较强的鲁棒性和更好的动态性能;就MPTC与直接转矩控制(DTC)和磁场定向控制(FOC)比较而言,采用前者策略的无速度传感器电机驱动系统能够降低逆变器开关频率、减少相电流总谐波失真(THD),从而提高系统可靠性.     

基于自适应神经模糊推理系统的永磁同步电机直接转矩控制

为减小永磁同步电机直接转矩控制系统的转矩脉动,提高系统的稳态精度和动态响应,设计了一种自适应神经模糊推理系统速度控制器,使电动机转子速度快速跟随给定值,并给出了详细的实现方法。仿真实验结果表明,具有ANFIS速度控制器的永磁同步电机直接转矩控制系统不仅动态和稳态性能都得到提高,而且具有较强的鲁棒性。     

磁链跟踪PWM感应电机矢量控制系统

PWM调制方式及控制器参数对感应电机矢量控制系统性能有较大影响,使电机在低速时电流脉动和转矩脉动较大。以磁链、转矩闭环的电机矢量控制方法为基础,提出了磁链跟踪控制(Space Vector PWM,SVPWM)的感应电机矢量控制系统仿真模型及低速变控制器参数的控制方法,高度模拟实际系统,并考虑逆变器死区时间的影响,在不同的调速范围下,进行了系统性能分析。在转子磁链定向准确,转子磁链构造准确的前提下,仿真结果表明磁链跟踪控制的矢量控制系统跟踪磁链为准圆形,在低速下,电机电流脉动和转矩脉动都比较小,使系统能够稳定运行,对于解决感应电机高性能调速的低速问题给出了可行的途径。     

三相六开关容错逆变器驱动异步电机FCS-MPTC

针对异步电机驱动系统中三相六开关逆变器单管故障问题,提出一种逆变器在故障状态下的三相六开关容错控制策略,较三相四开关容错控制可以提供较多的电压矢量,进而可以有效抑制转矩脉动.结合有限集预测控制思想,提出一种基于三相六开关容错逆变器的异步电机有限控制集模型预测转矩控制策略,同时采用模糊PI转速控制器替代固定参数的PI控制器,进一步提高系统的稳定性、快速性和鲁棒性.仿真结果表明, 采用此方案进行控制的异步电机驱动系统能够持续稳定运行,具有良好的动态性能,进一步验证了所提出方法的有效性.