基于单神经元PID的直流脉宽调速系统研究

针对传统直流调速系统存在因PID控制器参数固定而导致调速性能差的问题,提出了基于单神经元PID的双闭环直流脉宽调速系统的设计方法,即采用单神经元PID控制器替代传统的转速PI调节器,并用S函数编写该控制器的算法程序。Matlab仿真结果表明,基于单神经元PID的双闭环直流脉宽调速系统具有较快的响应速度和较强的抗干扰能力,调速性能得到了较好的改善。     

基于单神经元PID控制的直流无刷电机调速系统的建模与仿真

采用单神经元PID控制器的调速系统具有更好的动态性能和启动性能。针对直流无刷电机调速系统,采用了一种基于改进的单神经元PID控制算法,用MATLAB／Simulink仿真工具对调速系统进行了建模,矛对控制算法进行了仿真分析。仿真结果表明,使用该算法对直流无刷电机进行调速控制具有较高的可行性。与传统的PID控莉算法相比,新算法具有更好的鲁棒性,且易于实现。     

神经网络在现代化推力轴承试验台主传动控制中的应用

介绍了基于单神经元控制的直流调速系统的结构组成，单神经元控制器及其学习算法设计，单神经元直滚调速系统参数设计和激机仿真试验。     

补偿 PID控制在直流调速系统中的应用

针对传统PID自适应能力差的缺点，提出了一种基于单神经元补偿的PID控制策略，即将单神经元作为传统PID控制器的补偿环节，并将其用于直流调速系统中。仿真结果表明，该控制结构简单，易于实现，被控系统较传统PID有良好的动态特性和很强的鲁棒性，并为直流调速系统的智能控制提供一条新途径。     

基于模糊控制的直流双闭环调速系统仿真

将模糊控制应用于直流双闭环调速系统中的转速调节器,设计出模糊转速控制器,并与传统的双闭环调速系统进行了比较。利用MATLAB语言对这两种直流调速系统进行了仿真分析。仿真结果表明,用模糊控制方法得到的转速调节器参数比传统的工程整定方法得到的参数控制性能好,响应快,过渡过程时间短,基本无超调。     

直流调速系统中常规PI 与模糊PI 控制器的比较

转速电流双闭环直流调速系统中的PI控制器直接影响其调速性能,利用MATLAB分别建立了由常规PI与模糊PI控制器组成的转速电流双闭环控制直流调速系统的仿真模型,并进行了仿真。仿真结果表明,模糊PI控制器与常规PI控制器相比具有更好的动态稳定性和跟踪性能,对外界干扰具有较强的鲁棒性。     

基于TMS320LF2407的神经元控制直流调速系统

本文介绍基于TMS320LF2407的神经网络控制直流调速系统设计及实现.主要内容涉及神经元控制器、硬件、软件设计,并对该系统进行了仿真实验.从仿真结果可知该系统具有良好的控制性能,并能达到很高的控制精度.     

基于TMS320LF2407的神经元控制直流调速系统

本文介绍基于TMS320LF2407的神经网络控制直流调速系统设计及实现。主要内容涉及神经元控制器、硬件、软件设计,并对该系统进行了仿真实验。从仿真结果可知该系统具有良好的控制性能,并能达到很高的控制精度。     

PSO优化分数阶PIλ控制的双闭环直流调速控制

将分数阶PIλ控制器应用在双闭环直流调速系统的速度调节器上,以提高调速系统的控制性能。PI和PIλ控制器参数的整定通过粒子群优化算法。重点研究了分数阶PIλ控制器作为速度控制器的双闭环直流调速系统的稳定性、快速性。为了分析比较两种控制器的鲁棒性,将系统的前向通道的增益增加了10%～60%,在相同条件下,对基于PI/PIλ的双闭环直流调速系统进行了仿真实验。仿真研究结果表明,采用分数阶PIλ控制器的双闭环直流调速系统的稳定性、快速性和鲁棒性均优于普通PI控制器的系统。     

直流电动机调速控制系统的仿真研究

研究直流调速系统控制性能优化问题,直流调速系统是一种具有时变性和非线性的复杂控制系统,无法建立精确数学模型,传统PID控制的控制效果依赖于被控对象的精确数学模型,传统PID无法动态适应直流调速系统,难以获得较高的控制精度.SVM具有非线性控制能力,不需要建立精确的直流调速系统数学模型就可以进行精确逼近,为提高了直流调速系统控制精度,提出一种支持向量机和传统PID相结合的直流调速系统控制方法(SVM-PID).通过支持向量机对传统PID控制器3个参数进行在线整定,使PID控制器能够适应直流调速系统非线性变化,从而实现对适应直流调速系统精确控制,最后采用仿真验证SVM-PID性能.仿真结果表明,SVM-PID较好的克服了传统PID控制器存在的不足,提高了系统的抗干扰能力,减小了系统的超调量,为提高控制系统的性能提供了依据.     

Design of the speed controller for sensorless electric drives based on AI techniques: a comparative study

The paper investigates applicability of different artificial intelligence (AI) techniques in the design of a speed controller for electric drives. A speed-sensorless drive system is considered. A controller structure consisting of a load torque observer, a speed estimator and a speed predictor is developed. Next, different AI based approaches to speed controller design are investigated. The speed controllers based on (1) feed-forward neural network, (2) neuro-fuzzy network, and (3) self-organising Takagi–Sugeno (TS) rule based model are designed. A comparative analysis of the drive behaviour with these three types of AI based speed controllers is performed. In addition, a comparison is made with respect to the drive performance obtained with a conventional optimised PI controller. A detailed simulation study of a number of transients indicates that the best performance, in terms of accuracy and computational complexity, is offered by the self-organising Takagi–Sugeno controller. The controllers are developed and tested for a plant comprising a variable-speed separately excited DC motor.     

Generation of an optimal architecture of neuro force controllers for robot manipulators in unknown environments using genetic programming with fuzzy fitness evaluation

 In this paper, we have applied genetic programming to generate an optimal architecture of neuro force controllers for robot manipulators in any environment. In order to perform precise force control in unknown environments, the optimal structured neuro force controller is generated using genetic programming with fuzzy fitness evaluation. After the architecture of the neuro controller has been optimized for any kinds of environments, it can be applied for a robot contact task with an unknown environment in on-line manner using its own adaptation ability. An effective crossover operation is proposed for the efficient evolution of the controllers. The simulation has been carried out to evaluate the effectiveness of the proposed robot force controller.     

Bioreactor profile control by a nonlinear auto regressive moving average neuro and two degree of freedom PID controllers

This paper presents the use of nonlinear auto regressive moving average (NARMA) neuro controller for temperature control and two degree of freedom PID (2DOF-PID) for pH and dissolved oxygen (DO) of a biochemical reactor in comparison with the industry standard anti-windup PID (AWU-PID) controllers. The process model of yeast fermentation described in terms of temperature, pH and dissolved oxygen has been used in this study. Nonlinear auto regressive moving average (NARMA) neuro controller used for temperature control has been trained by Levenberg–Marquardt training algorithm. The 2DOF-PID controllers used for pH and dissolved oxygen have been tuned by MATLAB's auto tune feature along with manual tuning. Random training data with input varying from 0 to 100 l/h have been obtained by using NARMA graphical interface. The data samples used for training, validation and testing are 20,000, 10,000 and 10,000 respectively. Random profiles have been used for simulation. The NARMA neuro controller and the 2DOF-PID controllers have shown improvement in rise time, residual error and overshoot. The proposed controllers have been implemented on TMS320 Digital Signal Processing board using code composure studio. Arduino Mega board has been used for input/output interface.     

无刷直流电机的电流跟踪控制

基于无刷直流电机数学模型,采用Simulink建立了无刷直流电机调速系统的仿真模型,研究了两种电流跟踪控制技术。系统采用双闭环控制,速度环采用PI调节器,电流环分别采用滞环比较电流跟踪控制和三角波比较电流跟踪控制。对两种电流跟踪控制方案进行了仿真比较,得出的结论为高性能无刷直流电机控制器的设计提供了参考。     

无刷直流电动机系统的有限时间同步

有限时间同步能够确保两个系统在有限的时间内实现同步,具有重要的研究意义.但常见的控制器往往只能确保两个系统渐近同步,而能够实现有限时间同步的控制器目前尚不多见,且存在控制器复杂,不连续易产生抖振现象等缺陷.为此,本文设计了一种简单连续的有限时间同步控制器,实现了主–从无刷直流电动机系统(the brushless DC motor system,BLDCM)的有限时间同步.首先建立了基于该控制器的主–从有限时间同步框架.然后,从理论上证明了两个相同的BLDCM系统有限时间同步的充分性同步判据.最后,通过数值实例验证了所得判据的有效性.     

PI controller design using ABC algorithm for MPPT of PV system supplying DC motor pump load

Maximum power point tracking (MPPT) is used in photovoltaic (PV) systems to maximize its output power. A new MPPT system has been suggested for PV–DC motor pump system by designing two PI controllers. The first one is used to reach MPPT by monitoring the voltage and current of the PV array and adjusting the duty cycle of the DC/DC converter. The second PI controller is designed for speed control of DC series motor by setting the voltage fed to the DC series motor through another DC/DC converter. The suggested design problem of MPPT and speed controller is formulated as an optimization task which is solved by artificial bee colony (ABC) to search for optimal parameters of PI controllers. Simulation results have shown the validity of the developed technique in delivering MPPT to DC series motor pump system under atmospheric conditions and tracking the reference speed of motor. Moreover, the performance of the ABC algorithm is compared with genetic algorithm for various disturbances to prove its robustness.

     

半浮连轧管机组限动链传动控制系统

分析了由位置控制器、直流传动装置和PLC组成的半浮芯棒连轧机组限动链控制系统的硬件配置及软件组成。此系统在通用的速度、电流双闭环逻辑无环流直流可逆系统的基础上，利用位置控制器作为位置环，给定位置和实际位置的偏差通过位置PI调节器运算生成相应的速度值vk，与设定速度vt，叠加，作为双闭环系统速度环的速度给定，构成位置、速度、电流三闭环。利用中断实现芯棒定位和毛管定位之间的切换。经测试证明，此系统具有定位精度高、动态性能好等特点。     

A multirule-base controller using the robust property of a fuzzycontroller and its design method

This paper suggests a new fuzzy adaptive controller, which is able to solve the problems of classical adaptive controllers and conventional fuzzy adaptive controllers. It explains the architecture of a fuzzy adaptive controller using the robust property of a fuzzy controller. The basic idea of new adaptive control scheme is that an adaptive controller can be constructed with parallel combination of robust controllers. This new adaptive controller uses a multirule-base architecture which has several independent fuzzy controllers in parallel, each with different robust stability area. Out of several independent fuzzy controllers, the most suited one is selected by a system identifier which observes variations in the controlled system parameter. Here, we propose a design procedure which can be carried out mathematically and systematically from the model of a controlled system; related mathematical theorems and their proofs are also given. The performance of the proposed adaptive control algorithm is analyzed through a design example and a DC motor control simulation