EFFECTIVE DOUBLE LOOP CONTROL FOR A SQUARE WAVE BRUSHLESS DC MOTOR

A new type of brushless DC motor has been developed by using a square wave rare earth permanent magnet synchronous motor with its double loop control circuit. The double loop control scheme of the drive system yields a combination of desired characteristics including simplified control structure, small ripple torque, high speed accuracy, wide operating speed range, and fast dynamic response. Experimental results confirm excellent characteristics of the motor.

Improved single-neuron PID controller for DC motor applied in robot system

An improved single-neuron proportional integral derivative （ PID ） controller and a new method to build the DC motor system were presented in the article. In the simulation, the robot arm is considered as an external load to DC motor. Both the motor module and the load module are crea- ted in Simulink to achieve simulation results closer to real robot system. In this way, it can well veri- fy the performance of the improved single-neuron PID controller, which is a combined controller of normal PID controller and single-neuron PID controller. Besides, an intelligent switcher can help to realize the function of choosing a better control algorithm according to motor＇ s velocity output. Sim- ulated results confirm the rapid and stable response of the improved PID controller. Moreover, the improved single-neuron PID controller has an excellent ability to overcome the load impact and su- press the jamming signals. At last, a GUI interface platform is built to make the controller easier to be applied in other robot systems.

Robust Control Strategy for the Speed Control of Brushless DC Motor

Brushless DC motor ( BLDCM) speed servo system is multivariable,nonlinear and strong coupling. The parameter variation, the cogging torque and the load disturbance easily influence its performance. Therefore,it is difficult to achieve superior performance by using the conventional PID controller. To solve the deficiency,the paper represents the algorithm of active-disturbance rejection control ( ADRC) based on back-propagation ( BP) neural network. The ADRC is independent on accurate system and its extended-state observer can estimate the disturbance of the system accurately. However,the parameters of Nonlinear Feedback ( NF) in ADRC are difficult to obtain. So in this paper,these parameters are self-turned by the BP neural network. The simulation and experiment results indicate that the ADRC based on BP neural network can improve the performances of the servo system in rapidity,control accuracy,adaptability and robustness.

Fuzzy PID Controllers Using FPGA Technique for Real Time DC Motor Speed Control

The design of intelligent control systems has become an area of intense research interest. The development of an effective methodology for the design of such control systems undoubtedly requires the synthesis of many concepts from artificial intelligence. The most commonly used controller in the industry field is the proportional-plus-integral-plus-derivative (PID) controller. Fuzzy logic controller (FLC) provides an alternative to PID controller, especially when the available system models are inexact or unavailable. Also rapid advances in digital technologies have given designers the option of implementing controllers using Field Programmable Gate Array (FPGA) which depends on parallel programming. This method has many advantages over classical microprocessors. In this research, A model of the fuzzy PID control system is implemented in real time with a Xilinx FPGA (Spartan-3A, Xilinx Company, 2007). It is introduced to maintain a constant speed to when the load varies.,The model of a DC motor is considered as a second order system with load variation as a an example for complex model systems. For comparison purpose, two widely used controllers “PID and Fuzzy” have been implemented in the same FPGA card to examine the performance of the proposed system. These controllers have been tested using Matlab/Simulink program under speed and load variation conditions. The controllers were implemented to run the motor as real time application under speed and load variation conditions and showed the superiority of Fuzzy-PID.

Steering Control of Wheeled Armored Vehicle with Brushless DC Motor

Considering the steering characters of one type of wheeled armored vehicle, a brushless direct current （DC） motor is adapted as the actuator for steering control. After investigating the known algorithms, one kind of algorithm, which combines the fuzzy logic control with the self-adapting PID control and the startup and pre-hrake control, is put forward. Then a test-bed is constructed, and an experiment is conducted. The result of experiment confirms the validity of this algorithm in steering control of wheeled armored vehicle with brushless DC motor.

An Advance Commutation Control Method for Low Torque Ripple of Brushless DC Motors

该文给出一种优化的无刷直流电动机变母线电压六拍控制方法。既解决了传统六拍控制策略中由于绕组电感所造成的电流滞后问题,又改善了由于换相所造成的电流脉动问题。该方法是通过在每两拍之间插入一小段缓冲区来实现的,该缓冲区使下一拍将要切入的电流提前切入,同时使当前拍将要切出的电流延迟切出。通过确定缓冲区的起始位置,即超前换相角的大小,保证每一拍所对应的相电流的中心和反电动势的中心重合。通过选取缓冲区开关器件PWM调制的占空比,降低电机运行过程中的换相转矩脉动。该文方法只需根据负载大小选取超前换相角,一旦选定适用于所有转速。文中给出了该方法的理论推导过程和实验验证波形,从实验结果可以发现,电流滞后被消除,换相电流脉动显著减小,电机的利用率得到提高。

Fault Tolerant Neuro-Robust Position Control of DC Motors

DC motors are widely used in industry such as mechanics, robotics, and aerospace engineering. In this paper, we present a high performance control method for position control of DC motors. Fault-tolerant control model are also addressed to combine with neuro-robust control approach. It is shown that with the proposed control algorithms, external disturbances and coupled dynamics inherent in the system are effectively compensated using neural network unit in which no analytical estimation on the upper bound of the reconstruction error and uncertainties is needed. Simulations on various flight conditions also confirm the effectiveness of the proposed methods.

Type-2 Fuzzy Logic Controllers Based Genetic Algorithm for the Position Control of DC Motor

Type-2 fuzzy logic systems have recently been utilized in many control processes due to their ability to model uncertainty. This research article proposes the position control of (DC) motor. The proposed algorithm of this article lies in the application of a genetic algorithm interval type-2 fuzzy logic controller (GAIT2FLC) in the design of fuzzy controller for the position control of DC Motor. The entire system has been modeled using MATLAB R11a. The performance of the proposed GAIT2FLC is compared with that of its corresponding conventional genetic algorithm type-1 FLC in terms of several performance measures such as rise time, peak overshoot, settling time, integral absolute error (IAE) and integral of time multiplied absolute error (ITAE) and in each case, the proposed scheme shows improved performance over its conventional counterpart. Extensive simulation studies are conducted to compare the response of the given system with the conventional genetic algorithm type-1 fuzzy controller to the response given with the proposed GAIT2FLC scheme.

Design and implementation of a position sensorless control method for brushless DC motors

Because brushless direct current(BLDC) motors have the advantages of a compact size, high power density, high efficiency, and long operating life time, they are widely used in many industrial products and electric traction systems. It is known that the BLDC motors have no brushes for commutation. They are commutated with electronically commutation. So, the rotor position information of the BLDC motors must be known to understand which winding will be energized according to the energizing sequence. In most of the existing BLDC motor drivers, rotor position information is detected by Hall effect sensors. This kind of mechanical position sensors will bring additional connections and costs, reliability decrease and noise increase. In order to improve the control performance and extend the range of speed regulation for BLDC motors, a position sensorless control method is proposed in this paper. In the proposed control method, rotor position information of the BLDC motors is detected from the back electromagnetic forces(back-EMFs) which are estimated by an unknown-input observer with line to line currents and line to line voltages. For the purpose of verifying the effectiveness of the proposed control method, a model is built and simulated on the Matlab/Simulink platform. The simulation results show that the speed regulation performance of BLDC motors is improved compared with using Hall effect sensors. At the same time, the reliability of the BLDC motors is improved and the costs of them are reduced because the position sensor is eliminated.

Application of Diagonal Recurrent Neural Network toDC Motor Speed Control Systems

A new kind of dynamic neural network--diagonal recurrent neural network (DRNN) and its learning method and architecture are presented. A direct adaptive control scheme is also developed that is applied to a DC (Direct Current) speed control system with the ability to auto-tune PI (Proportion Integral) parameters based on combining DRNN with PI controller. The simulation results of DRNN show better control performances and potential practical use in comparison with PI controller.

Current Search: Performance Evaluation and Application to DC Motor Speed Control System Design

This paper proposes the current search (CS) metaheuristics conceptualized from the electric current flowing through electric networks for optimization problems with continuous design variables. The CS algorithm possesses two powerful strategies, exploration and exploitation, for searching the global optimum. Based on the stochastic process, the derivatives of the objective function is unnecessary for the proposed CS. To evaluate its performance, the CS is tested against several unconstrained optimization problems. The results obtained are compared to those obtained by the popular search techniques, i.e., the genetic algorithm (GA), the particle swarm optimization (PSO), and the adaptive tabu search (ATS). As results, the CS outperforms other algorithms and provides superior results. The CS is also applied to a constrained design of the optimum PID controller for the dc motor speed control system. From experimental results, the CS has been successfully applied to the speed control of the dc motor.

Adaptive Internal Model Control of a DC Motor Drive System Using Dynamic Neural Network

This work concerns the study of problems relating to the adaptive internal model control of DC motor in both cases conventional and neural. The most important aspects of design building blocks of adaptive internal model control are the choice of architectures, learning algorithms, and examples of learning. The choice of parametric adaptation algorithm for updating elements of the conventional adaptive internal model control shows limitations. To overcome these limitations, we chose the architectures of neural networks deduced from the conventional models and the Levenberg-marquardt during the adjustment of system parameters of the adaptive neural internal model control. The results of this latest control showed compensation for disturbance, good trajectory tracking performance and system stability.

A Novel Control Strategy Based onπ-VSG for Inter-Face Converter in Hybrid Microgrid

The rapid development of new energy power generation technology and the transformation of power electronics in the core equipment of source-grid-load drives the power system towards the“double-high”development pattern of“high proportion of renewable energy”and“high proportion of power electronic equipment”.To enhance the transient performance of AC/DC hybrid microgrid(HMG)in the context of“double-high,”aπtype virtual synchronous generator(π-VSG)control strategy is applied to bidirectional interface converter(BIC)to address the issues of lacking inertia and poor disturbance immunity caused by the high penetration rate of power electronic equipment and new energy.Firstly,the virtual synchronous generator mechanical motion equations and virtual capacitance equations are used to introduce the virtual inertia control equations that consider the transient performance of HMG;based on the equations,theπ-type equivalent control model of the BIC is established.Next,the inertia power is actively transferred through the BIC according to the load fluctuation to compensate for the system’s inertia deficit.Secondly,theπ-VSG control utilizes small-signal analysis to investigate howthe fundamental parameters affect the overall stability of the HMG and incorporates power step response curves to reveal the relationship between the control’s virtual parameters and transient performance.Finally,the PSCAD/EMTDC simulation results show that theπ-VSG control effectively improves the immunity of AC frequency and DC voltage in the HMG system under the load fluctuation condition,increases the stability of the HMG system and satisfies the power-sharing control objective between the AC and DC subgrids.

Design of BLDCM Driving and Control System for Motorized Treadmill

To satisfy the requirement of developing a new generation of motorized treadmill for a famous domestic manufacturer, a brushless DC motor （BLDCM） driving and control system for motorized treadmill is developed. High integration and reliability of this system are ensured under the condition that intelligent power module （IPM） is used and the protection module is included. Periodic current control method is applied to reduce the average current flowing through the armature winding of the motor when the treadmill is required to start with low speed while large load is added. Piecewise proportion-integration-differentiation （PID） control algorithm is applied to solve the problem of speed fluctuation when impulse load is added. The motorized treadmill of a new generation with the driving and control system has the advantages of high reliability, good speed stability, wide timing scope, low cost, and long life-span. And it is very promising for practical applications.

Sensorless Control of Four-Switch Inverter for Brushless DC Motor Drive and Its Simulation

The major function of this proposed research is to control the speed of the brushless DC motor with sensor less control for four-switch three phase inverter. This proposed system is simplified the topological structure of the conventional six-switch three phase inverter. In this proposed method, a new structure of four-switch three phase inverter [1] with reduced number of switches for system is introduced to reduce the mechanical commutation, switching losses that occur in the six-switch method. The proposed inverter fed brushless DC motor used in sensorless control schemes which is used for sensing positioning signals. To improve sensor less control performance, four-switch electronic commutation modes based proportional intergral controller scheme is implemented. In this four-switch three phase inverter reduction of switches, low cost control and saving of hall sensor were incorporated. The feasibility of the proposed sensor less control four-switch three phase inverter fed brushless DC motor drive is implemented, analysed using MATLAB/SIMULINK, effective simulation results have been validated out successfully.

Anti-reverse rotation startup method for sensorless brushless DC motor

In order to start up the brushless DC motor （BLDCM） without reverse rotation and smoothly switch the running state of the motor, a novel startup and smoothly switching method for a sensodess BLDCM is presented. Based on the saturation effect of the stator iron, six short voltage pulses are applied to determine the initial rotor position and the rotor can be found within 60°. After that, a series of short and long voltage pulses are used to accelerate the motor and the variation of the response current is utilized to detect the rotor position dynamically. When the motor reaches a certain speed at which the back-electromotive force （EMF） method can be applied, all the power devices are turned off and the running state of the motor is smoothly switched at the moment determined by the relationship between the terminal voltage waveform and the commutation phases. The experimental results verify the feasibility and validity of the proposed method.

基于DCS-惯性权重组合的柔性机械臂运动轨迹控制系统研究

为了有效调控机械臂运动幅度,避免柔性机械臂实际运动轨迹与目标轨迹发生较大的偏差,设计一种基于分散控制系统(DCS)-惯性权重组合的柔性机械臂运动轨迹控制系统,它是一个集合了计算机、通信、显示和控制的计算机系统,该系统由过程控制和过程监控。通过DCS算法控制监测对象行为幅度并计算柔性机械臂惯性权重,利用DCS-惯性权重组合求解素数控制指标的具体数值,实现了对柔性机械臂运动轨迹控制系统的软件设计。实验结果表明,DCS-惯性权重组合算法作用下的机械臂末端坐标在横轴、纵轴、空间轴方向上的运动幅值均可以被控制在10个单位长度之内,并且在0.52 s内进入机械臂运动轨迹最佳控制状态,运动轨迹差值最大仅为0.01 rad,验证了该系统具备可行性和有效性。

基于模糊BP神经网络的智能轮椅BLDCM控制

现阶段多数轮椅电机仍使用传统PID控制,该控制方式存在控制精准度较低、超调量较大以及抗扰动能力差等问题。为解决以上问题,通过对无刷直流电机进行研究,在分析了其控制方法后,提出一种基于模糊BP神经网络的BLDCM控制方法。首先,研究了BLDCM结构并搭建数学模型。其次,在模型基础上构建了模糊BP神经网络PID控制器。最后,在Matlab/Simulink中搭建整个电机控制系统进行三种不同工况下的运动控制仿真,并与传统PID控制算法进行对比。实验结果表明:模糊BP神经网络PID控制策略能获得更好的PID控制参数,具有良好的抗扰动能力,有效的改善了整个轮椅控制系统的动态性能。

Comparative Study between the PID Regulator and the Fuzzy Regulator Applied to the Operation of a Brushless DC Motor

This paper presents the results of research on speed regulation of a brushless DC motor. This is mainly a comparative study between a PID regulator and a fuzzy regulator applied to the operation of this type of engine in order to find the best control. The BLDC engine must operate under various speed and load conditions with improved performance and robust and complex speed control. Because of this complexity, the traditional PID command encounters difficulties in controlling the speed of a BLDC. Another control technique is currently developing and is producing good results. This is the fuzzy controller that handles process control problems, that is, managing a process based on a given set point per action on the variables that describe the process. To achieve the desired results, the brushless DC machine model will be studied. With the model obtained, both types of regulator will be tested. A synthesis of the observed comparison results will enable a conclusion to be drawn on the performance of the two types of regulators driving a BLDC (Brushless DC).