异步电机的模糊PID矢量控制

为了提高异步电机转速性能,文章在原矢量控制系统的基础上,将传统的转速PID控制器转换为模糊自适应PID控制器,然后利用Matlab/Simulink搭建了一种基于三相异步电动机转速控制的模糊PID系统,并分别使用常规PID控制器与模糊PID控制器进行控制。仿真结果表明,采用模糊控制能使系统取得较好的控制性能并具有较强的鲁棒性。     

异步电动机自适应反步控制

针对负载的不确定,将自适应反步控制理论应用到异步电动机的矢量控制系统中,替换传统的磁链和转速外环PI控制器,通过Lyapunov能量函数得到反步控制器和未知负载的自适应率,从而实现异步电动机的高性能调速。在Matlab/Simulink软件上完成了控制平台的建模和实时仿真实验,仿真结果表明自适应反步控制器对负载和转速变化比PI控制器的适应能力更强,具有更好的鲁棒稳定性,动静态性能更优,从而验证了该控制器的有效性。     

自调整模糊控制器在永磁同步电机矢量控制中的应用

模糊控制器对系统模型依赖程度低,性能优异,已经在许多场合得到了应用。传统的永磁同步电机矢量控制系统采用PI控制,在电机参数变化或负载波动较大时性能不佳。针对这一问题,本文设计了一种基于量化因子和比例因子自调整的自适应模糊控制器。仿真结果表明,与传统PI和基本模糊控制器相比,这种参数自调整模糊控制器能使系统具有更好的抗扰性和更优良的稳态性能。     

感应电动机智能软启动器研究

为了解决三相异步电动机起动电流较大的问题,采用了一种软起动模糊自适应控制器。据其起动特性,将电流误差和误差的变化率作为输入量,晶闸管的控制角作为输出量,设计出一种自调整比例因子的模糊自适应控制算法。随后做了Matlab环境下的仿真实验,并且与常规的PID控制方法和常规模糊控制方法进行比较,得到了模糊自适应控制算法具有优越性的结论。     

交流电动机矢量控制变压变频调速系统（七） 第七讲 异步电动机矢量控制系统

异步电动机矢量控制系统可以分为直接矢量控制系统和间接矢量控制系统。首先对具有转矩内环和磁链闭环的商接矢量控制系统进行了介绍，给出了其动态解耦结构图，然后介绍了电压源型和电流源型两种间接矢量控制系统，最后，对无速度传感器异步电动机矢量控制系统进行了简要的介绍。     

带有自校正功能的异步电动机转差型矢量控制系统（一）

为了提高异步电动机转差型矢量控制系统对鲁棒性，从而提高整个调速系统的动态性能，本文介绍了一种具有自校正的转差型矢量控制系统。在分析了前馈矢量控制系统中存在问题的基础上，给出了修正的Gabriel自校正方法的自校正律，利用相关函数的方法证明了这种自校正方法的正确性，以及对转速测量误差和负载变化的鲁棒性。[编者按]     

感应电机矢量控制与直接转矩控制系统的低速性能对比

本文详细分析了感应电机矢量控制和直接转矩控制系统的基本原理。在此基础上,基于matlab6.5／simulink分别建立了基于转子磁场定向的矢量控制系统和基于U—I模型的直接转矩控制系统的仿真模型。仿真研究了定子电阻、转于电阻变化对这两种控制系统低速性能的影响。并进行了对比分析。仿真结果表明,直接转矩控制系统具有更快的转矩响应,转子电阻对矢量控制系统影响较大,而定子电阻对基于U—I模型的直接转矩控制系统的影响较大。     

交流电动机矢量控制变压变频调速系统（五）：第五讲 磁场定向的基本概念和矢量控制系统的基本结构

本讲首先介绍了磁场定向的基本概念,然后基于同步旋转坐标系上的异步电动机动态数学模型,得到了按转子磁场定向的异步电动机电压方程式和转矩方程式,在此基础上,介绍了按转子磁场定向的异步电动机矢量控制系统的基本结构。     

基于MRAS的无速度传感器矢量控制的研究

利用异步电动机定子电压、电流易于检测的特点,运用模型参考自适应（MRAS）的算法对电动机的转速进行辨识,以MATLAB/Simulink里面的Simpower工具箱为基础,搭建了异步电机无速度传感器矢量控制系统的仿真模型。结果表明,本系统设计方法可行、具有良好的速度响应和动静态性能。     

基于MATLAB／SIMULINK异步电机矢量控制系统的仿真

本文根据交流电机矢量控制理论及异步电动机在同步坐标系下仿真结构图的建模设想，构成了异步电动机矢量控制系统的仿真模型，对其进行仿真分析，结果表明系统的稳，动态性能有很好的优越性。     

Decoupled stator-flux-oriented induction motor drive with fuzzyneural network uncertainty observer

A stator-flux-oriented induction motor drive using online rotor time-constant estimation with a robust speed controller is introduced in this paper. The estimation of the rotor time constant is made on the basis of the model reference adaptive system using an energy function. The estimated rotor time-constant is used in the current-decoupled controller, which is designed to decouple the torque and flux in the stator-flux-field-oriented control. Moreover, a robust speed controller, which is comprised of an integral-proportional speed controller and a fuzzy neural network uncertainty observer, is designed to increase the robustness of the speed control loop. The effectiveness of the proposed control scheme is demonstrated by simulation and experimental results     

A fuzzy logic sliding mode controlled electronic differential for a direct wheel drive EV

In this study, a direct wheel drive electric vehicle based on an electronic differential system with a fuzzy logic sliding mode controller (FLSMC) is studied. The conventional sliding surface is modified using a fuzzy rule base to obtain fuzzy dynamic sliding surfaces by changing its slopes using the global error and its derivative in a fuzzy logic inference system. The controller is compared with proportional–integral–derivative (PID) and sliding mode controllers (SMCs), which are usually preferred to be used in industry. The proposed controller provides robustness and flexibility to direct wheel drive electric vehicles. The fuzzy logic sliding mode controller, electronic differential system and the overall electrical vehicle mechanism are modelled and digitally simulated by using the Matlab software. Simulation results show that the system with FLSMC has better efficiency and performance compared to those of PID and SMCs.     

Fuzzy adaptive vector control of induction motor drives

This paper deals with the design and experimental realization of a model reference adaptive control (MRAC) system for the speed control of indirect field-oriented (IFO) induction motor drives based on using fuzzy laws for the adaptive process and a neuro-fuzzy procedure to optimize the fuzzy rules. Variation of the rotor time constant is also accounted for by performing a fuzzy fusion of three simple compensation strategies. A performance comparison between the new controller and a conventional MRAC control scheme is carried out by extensive simulations confirming the superiority of the proposed fuzzy adaptive regulator. A prototype based on an induction motor drive has been assembled and used to practically verify the features of the proposed control strategy     

Fuzzy adaptive speed control of a permanent magnet synchronous motor

A fuzzy adaptive speed controller is proposed for a permanent magnet synchronous motor (PMSM). The proposed fuzzy adaptive speed regulator is insensitive to model parameter and load torque variations because it does not need any accurate knowledge about the motor parameter and load torque values. The stability of the proposed control system is also proven. The proposed adaptive speed regulator system is implemented by using a TMS320F28335 floating point DSP. Simulation and experimental results are presented to verify the effectiveness of the proposed fuzzy adaptive speed controller under uncertainties such as motor parameter and load torque variations using a prototype PMSM drive system.     

Fault-tolerant switched reluctance motor drive using adaptive fuzzy logic controller

An adaptive fuzzy controller has been designed to develop a high-performance fault-tolerant switched reluctance motor (SRM) drive. The fuzzy controller continuously adapts its properties to regulate the machine torque as desired by the drive system even under fault conditions. The adaptation of the fuzzy membership functions results in extended conduction period and increased peak current of the healthy phases to deliver the commanded torque, as much as possible. The adaptive fuzzy controller provides smooth torque output with minimum ripple, even under fault conditions, yielding a high-performance SRM drive with fault-tolerant capability.     

柔性结构振动自适应模糊控制与实验研究

针对机敏柔性结构振动主动控制，提出了一种自适应模糊控制方案。在简单模糊控制器基础上引进了自适应环节，并经大量实验研究后总结了量化因子Ke、Kc与比例因子Ku的调整规则。最后对一机敏柔性梁进行了抑振实验，获得了良好的效果。     

一种基于PLC的模糊自适应PID控制器设计

通过对模糊自适应PID控制器设计过程的详细分析,提出了一种基于PLC查表方式实现模糊自适应PID控制器的方法,实现了基于PLC的自适应模糊PID控制器的设计,并应用于实际的控制系统中。结果表明,用PLC实现模糊自适应PID控制简单实用,适于工业控制系统应用。     

非线性系统的自适应模糊控制器设计

针对非线性系统,为获得更好的控制控制效果,设计了模糊自适应控制器。在模糊控制器的基础上根据反馈控制和调整参数向量的自适应律的求解,综合李雅普诺夫稳定理论设计了模糊自适应控制器,以满足系统的稳定性和控制效果。为验证控制器的有效性,将该控制器应用到二级倒立摆系统的稳定控制,仿真结果表明该控制器的控制效果良好,并与传统的控制方法相比较,其控制效果更佳。     

New robust adaptive control algorithm for high-performance ACdrives

This paper presents a new robust structure for a model reference adaptive control (MRAC) controller for field-oriented-controlled (FOC) drives which requires no prior knowledge of the drive parameters and is guaranteed to provide global asymptotic stability of the closed-loop system. This structure simplifies the design and implementation of the adaptive controller requiring less effort to synthesis than a standard MRAC system. Discussion on theoretical aspects, such as selection of a reference model, stability analysis proof, gain adaptive process, steady-state error elimination, and robustness to unmodeled dynamics are included. The paper describes many practical aspects of the implementation, such as adaptive gain analysis, adaptive rate selection, the gain variation limits, gain windup prevention measure, and initial values. The new robust adaptive controller has been successfully implemented on an FOC drive and experiment results for dynamic tracking, sudden loading and unloading, and gains adaptation under different operation conditions are presented to support the robustness of the proposed controller     

Adaptive Sliding-Mode Neuro-Fuzzy Control of the Two-Mass Induction Motor Drive Without Mechanical Sensors

In this paper, the concept of a model reference adaptive control of a sensorless induction motor (IM) drive with elastic joint is proposed. An adaptive speed controller uses fuzzy neural network equipped with an additional option for online tuning of its chosen parameters. A sliding-mode neuro-fuzzy controller is used as the speed controller, whose connective weights are trained online according to the error between the estimated motor speed and the speed given by the reference model. The speed of the vector-controlled IM is estimated using the $hbox{MRAS}^{rm CC}$ rotor speed and a flux estimator. Such a control structure is proposed to damp torsional vibrations in a two-mass system in an effective way. It is shown that torsional oscillations can be successfully suppressed in the proposed control structure, using only one basic feedback from the motor speed given by the proposed speed estimator. Simulation results are verified by experimental tests over a wide range of motor speed and drive parameter changes.