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 tra...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.展开更多
Pigeon-inspired optimization(PIO) is a new swarm intelligence optimization algorithm, which is inspired by the behavior of homing pigeons. A variant of pigeon-inspired optimization named multi-objective pigeon-inspire...Pigeon-inspired optimization(PIO) is a new swarm intelligence optimization algorithm, which is inspired by the behavior of homing pigeons. A variant of pigeon-inspired optimization named multi-objective pigeon-inspired optimization(MPIO) is proposed in this paper. It is also adopted to solve the multi-objective optimization problems in designing the parameters of brushless direct current motors, which has two objective variables, five design variables, and five constraint variables. Furthermore, comparative experimental results with the modified non-dominated sorting genetic algorithm are given to show the feasibility, validity and superiority of our proposed MIPO algorithm.展开更多
In this paper, a novel approach is proposed for solving the parameter design problem of brushless direct current(BLDC) motor, which is based on the membrane computing(MC) and pigeon-inspired optimization(PIO) algorith...In this paper, a novel approach is proposed for solving the parameter design problem of brushless direct current(BLDC) motor, which is based on the membrane computing(MC) and pigeon-inspired optimization(PIO) algorithm. The motor parameter design problem is converted to an optimization problem with five design parameters and six constraints. The PIO algorithm is introduced into the framework of MC for improving the global convergence performance. The hybrid algorithm can improve the population diversity with better searching efficiency. Comparative simulations are conducted, and comparative results are given to show the feasibility and effectiveness of our proposed hybrid algorithm for high nonlinear optimization problems.展开更多
In this paper, the main objective is to identify the parameters of motors, which includes a brushless direct current (BLDC) motor and an induction motor. The motor systems are dynamically formulated by the mechanical ...In this paper, the main objective is to identify the parameters of motors, which includes a brushless direct current (BLDC) motor and an induction motor. The motor systems are dynamically formulated by the mechanical and electrical equations. The real-coded genetic algorithm (RGA) is adopted to identify all parameters of motors, and the standard genetic algorithm (SRGA) and various adaptive genetic algorithm (ARGAs) are compared in the rotational angular speeds and fitness values, which are the inverse of square differences of angular speeds. From numerical simulations and experimental results, it is found that the SRGA and ARGA are feasible, the ARGA can effectively solve the problems with slow convergent speed and premature phenomenon, and is more accurate in identifying system’s parameters than the SRGA. From the comparisons of the ARGAs in identifying parameters of motors, the best ARGA method is obtained and could be applied to any other mechatronic systems.展开更多
作动器系统作为直升机结构响应主动控制系统(active control of structure response, ACSR)执行元件的重要核心部件,其控制性能直接关系到振动的抑制效果。研制了一套基于永磁无刷直流电机驱动的离心式作动器原理样机,详细阐述了系统组...作动器系统作为直升机结构响应主动控制系统(active control of structure response, ACSR)执行元件的重要核心部件,其控制性能直接关系到振动的抑制效果。研制了一套基于永磁无刷直流电机驱动的离心式作动器原理样机,详细阐述了系统组成及工作原理,论述了作动器输出力频率、幅值和相位的调节方式,建立了偏心块的输出力方程,选取了MAXON公司生产的EC60 flat 411 678无刷电机作为驱动源。设计了作动器伺服控制系统,针对功率驱动中的隔离直流电源、自举二极管、自举电容、电流驱动能力及母线支撑电容等关键参数进行了详细设计。通过实验验证,电机转速误差最终被控制为1%以内,位置响应时间小于50 ms,振动抑制在0.001 5g(g为重力加速度)以下,取得了较好的控制性能,为离心式作动器系统在直升机平台的应用奠定了基础。展开更多
电动摩托车的驱动部件无刷直流电机(brushless direct current motor,BLDCM)的控制策略研究,能提高电动摩托车的电机控制性能。采用BLDCM矢量控制策略,在Simulink中搭建控制系统仿真模型。在仿真分析的基础上,开发硬件实验平台,并进一...电动摩托车的驱动部件无刷直流电机(brushless direct current motor,BLDCM)的控制策略研究,能提高电动摩托车的电机控制性能。采用BLDCM矢量控制策略,在Simulink中搭建控制系统仿真模型。在仿真分析的基础上,开发硬件实验平台,并进一步验证其可行性。结果表明,该实验平台设计合理、易于使用、性能可靠、调试方便,尤其在研究BLDCM的控制算法时,可以缩短周期,提高效率。展开更多
文摘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.
基金partially supported by the National Natural Science Foundation of China(Grant Nos.61425008,61333004 and 61273054)National Key Basic Research Program of China("973"Project)(Grant Nos.2014CB046401 and 2013CB035503)Top-Notch Young Talents Program of China,Aeronautical Foundation of China(Grant No.20135851042)
文摘Pigeon-inspired optimization(PIO) is a new swarm intelligence optimization algorithm, which is inspired by the behavior of homing pigeons. A variant of pigeon-inspired optimization named multi-objective pigeon-inspired optimization(MPIO) is proposed in this paper. It is also adopted to solve the multi-objective optimization problems in designing the parameters of brushless direct current motors, which has two objective variables, five design variables, and five constraint variables. Furthermore, comparative experimental results with the modified non-dominated sorting genetic algorithm are given to show the feasibility, validity and superiority of our proposed MIPO algorithm.
基金supported by the National Natural Science Foundation of China(Grant Nos.61425008,61333004&61273054)Aeronautical Foundation of China(Grant No.2015ZA51013)
文摘In this paper, a novel approach is proposed for solving the parameter design problem of brushless direct current(BLDC) motor, which is based on the membrane computing(MC) and pigeon-inspired optimization(PIO) algorithm. The motor parameter design problem is converted to an optimization problem with five design parameters and six constraints. The PIO algorithm is introduced into the framework of MC for improving the global convergence performance. The hybrid algorithm can improve the population diversity with better searching efficiency. Comparative simulations are conducted, and comparative results are given to show the feasibility and effectiveness of our proposed hybrid algorithm for high nonlinear optimization problems.
文摘In this paper, the main objective is to identify the parameters of motors, which includes a brushless direct current (BLDC) motor and an induction motor. The motor systems are dynamically formulated by the mechanical and electrical equations. The real-coded genetic algorithm (RGA) is adopted to identify all parameters of motors, and the standard genetic algorithm (SRGA) and various adaptive genetic algorithm (ARGAs) are compared in the rotational angular speeds and fitness values, which are the inverse of square differences of angular speeds. From numerical simulations and experimental results, it is found that the SRGA and ARGA are feasible, the ARGA can effectively solve the problems with slow convergent speed and premature phenomenon, and is more accurate in identifying system’s parameters than the SRGA. From the comparisons of the ARGAs in identifying parameters of motors, the best ARGA method is obtained and could be applied to any other mechatronic systems.
文摘电动摩托车的驱动部件无刷直流电机(brushless direct current motor,BLDCM)的控制策略研究,能提高电动摩托车的电机控制性能。采用BLDCM矢量控制策略,在Simulink中搭建控制系统仿真模型。在仿真分析的基础上,开发硬件实验平台,并进一步验证其可行性。结果表明,该实验平台设计合理、易于使用、性能可靠、调试方便,尤其在研究BLDCM的控制算法时,可以缩短周期,提高效率。
