The adaptive control using BP neural networks for a nonlinear servo-motor

The servo-motor possesses a strongly nonlinear property due to the effect of the stimulating input voltage, load-torque and environmental operating conditions. So it is rather difficult to derive a traditional mathematical model which is capable of expressing both its dynamics and steady-state characteristics. A neural network-based adaptive control strategy is proposed in this paper. In this method, two neural networks have been adopted for system identification （NNI） and control （NNC）, respectively. Then, the commonly-used specialized learning has been modified, by taking the NNI output as the approximation output of the servo-motor during the weights training to get sensitivity information. Moreover, the rule for choosing the learning rate is given on the basis of the analysis of Lyapunov stability. Finally, an example of applying the proposed control strategy on a servo-motor is presented to show its effectiveness.

Linearizing Control of Induction Motor Based on Networked Control Systems

A new approach to speed control of induction motors is developed by introducing networked control systems （NCSs） into the induction motor driving system. The control strategy is to stabilize and track the rotor speed of the induction motor when the network time delay occurs in the transport medium of network data. First, a feedback linearization method is used to achieve input-output linearization and decoupling control of the induction motor driving system based on rotor flux model, and then the characteristic of network data is analyzed in terms of the inherent network time delay. A networked control model of an induction motor is established. The sufficient condition of asymptotic stability for the networked induction motor driving system is given, and the state feedback controller is obtained by solving the linear matrix inequalities （LMIs）. Simulation results verify the efficiency of the proposed scheme.

DECOUPLING CONTROL OF TWO MOTORS SYSTEM BASED ON NEURAL NETWORK INVERSE SYSTEM

In accordance with the characteristics of two motors system, the unitedmathematic model of two-motors inverter system with v/f variable frequency speed-regulating isgiven. Two-motor inverter system can be decoupled by the neural network invert system, and changedinto a sub-system of speed and a sub-system of tension. Multiple controllers are designed, and goodresults are obtained. Tie system has good static and dynamic performances and high anti-disturbanceof load.

Application of neural networks for permanent magnet synchronous motor direct torque control

Neural networks require a lot of training to understand the model of a plant or a process. Issues such as learning speed, stability, and weight convergence remain as areas of research and comparison of many training algorithms. The application of neural networks to control interior permanent magnet synchronous motor using direct torque control （DTC） is discussed. A neural network is used to emulate the state selector of the DTC. The neural networks used are the back-propagation and radial basis function. To reduce the training patterns and increase the execution speed of the training process, the inputs of switching table are converted to digital signals, i.e., one bit represent the flux error, one bit the torque error, and three bits the region of stator flux. Computer simulations of the motor and neural-network system using the two approaches are presented and compared. Discussions about the back-propagation and radial basis function as the most promising training techniques are presented, giving its advantages and disadvantages. The system using back-propagation and radial basis function networks controller has quick parallel speed and high torque response.

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.

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.

Single Phase Induction Motor Drive with Restrained Speed and Torque Ripples Using Neural Network Predictive Controller

In industrial drives, electric motors are extensively utilized to impart motion control and induction motors are the most familiar drive at present due to its extensive performance characteristic similar with that of DC drives. Precise control of drives is the main attribute in industries to optimize the performance and to increase its production rate. In motion control, the major considerations are the torque and speed ripples. Design of controllers has become increasingly complex to such systems for better management of energy and raw materials to attain optimal performance. Meager parameter appraisal results are unsuitable, leading to unstable operation. The rapid intensification of digital computer revolutionizes to practice precise control and allows implementation of advanced control strategy to extremely multifaceted systems. To solve complex control problems, model predictive control is an authoritative scheme, which exploits an explicit model of the process to be controlled. This paper presents a predictive control strategy by a neural network predictive controller based single phase induction motor drive to minimize the speed and torque ripples. The proposed method exhibits better performance than the conventional controller and validity of the proposed method is verified by the simulation results using MATLAB software.

Implementation of Adaptive Neuro Fuzzy Inference System in Speed Control of Induction Motor Drives

A new speed control approach based on the Adaptive Neuro-Fuzzy Inference System (ANFIS) to a closed-loop, variable speed induction motor (IM) drive is proposed in this paper. ANFIS provides a nonlinear modeling of motor drive system and the motor speed can accurately track the reference signal. ANFIS has the advantages of employing expert knowledge from the fuzzy inference system and the learning capability of neural networks. The various functional blocks of the system which govern the system behavior for small variations about the operating point are derived, and the transient responses are presented. The proposed (ANFIS) controller is compared with PI controller by computer simulation through the MATLAB/SIMULINK software. The obtained results demonstrate the effectiveness of the proposed control scheme.

Direct-Torque Neuro-Fuzzy Control of Induction Motor

Fuzzy systems are currently being used in a wide field of industrial and scientific applications.Since the design and especially the optimization process of fuzzy systems can be very time consuming,it is convenient to have algorithms which construct and optimize them automatically.In order to improve the system stability and raise the response speed,a new control scheme,direct-torque neuro-fuzzy control for induction motor drive,was put forward.The design and tuning procedure have been described.Also,the improved stator flux estimation algorithm,which guarantees eccentric estimated flux has been proposed.

基于ControlNet的PowerFlex700变频器实验开发

变频器是工业调速传动领域中应用很广泛的设备之一。PowerFlex700变频器是Rockwell自动化公司最新的产品。在Rockwell现场总线网络控制平台上,采用Rockwell的PLC和组态软件,编写变频调速控制梯形图,并进行对应的远程控制设计,设计并实现了Powerflex700变频器控制电机变频调速的综合实验。

A Transfusion Monitor According to Fuzzy Neural Network

With the MSP430 MCU as the core of control, the monitor combines the transfusion sensor, the electric apparatus, the LCDscreen and the keyboard to realize automatic monitor for transfusion process. Because transfusion system is nonlinear and complex,which changes now and then and lags behind hour, conventional control method can hardly obtain benign real time monitor effect.This article introduced a control method according to fuzzy neural network, which integrates the excellences of fuzzy logical controland neural network. The method uses neural network to remember fuzzy rule and achieve fuzzy control and uses error back propaga-tion to realize online self- learning when the control object parameter has changed. So it can colligate, analysis and dispose dataquickly and exactly to achieve intelligent control. Through examination, this monitor manages accurately, runs credibility, and itssmall bulk, low cost and convenient manipulation makes it worth popularizing and applying.

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。

基于神经网络的永磁同步电机模型预测电流控制

针对备选电压矢量有限导致永磁同步电机有限集模型预测电流控制性能较差及计算量较大的问题,提出基于神经网络的永磁同步电机模型预测电流控制。基于7个基本电压矢量和121个扩展电压矢量的永磁同步电机模型预测电流控制分别建立7分类和121分类神经网络。随着备选电压矢量的增加,模型预测电流控制性能提升,对应的神经网络控制性能也得到改善,但分类任务数也随之增加。对于多步模型预测控制,计算量随步长呈指数上升,但输出电压矢量不变。因此,基于两步模型预测电流控制建立7分类神经网络。仿真结果表明:以上神经网络控制均可行,性能与相对应的模型预测电流控制基本相当。实时性实验结果表明相较于单步模型预测电流控制,神经网络控制并不占优势,但相较于两步模型预测电流控制,神经网络实时性有明显优势,计算耗时减小29.58%,表明神经网络控制更适于多步模型预测电流控制。

FBFN-based adaptive repetitive control of nonlinearly parameterized systems

An adaptive repetitive control scheme is presented for a class of nonlinearly parameterized systems based on the fuzzy basis function network （FBFN）. The parameters of the fuzzy rules are tuned with adaptive schemes. To attenuate chattering effectively, the discontinuous control term is approximated by an adaptive PI control structure. The bound of the discontinuous control term is assumed to be unknown and estimated by an adaptive mechanism. Based on the Lyapunov stability theory, an adaptive repetitive control law is proposed to guarantee the closed-loop stability and the tracking performance. By means of FBFNs, which avoid the nonlinear parameterization from entering into the adaptive repetitive control, the controller singularity problem is solved. The proposed approach does not require an exact structure of the system dynamics, and the proposed controller is utilized to control a model of permanent-magnet linear synchronous motor subject to significant disturbances and parameter uncertainties. The simulation results demonstrate the effectiveness of the proposed method.

机床直线同步电机理想控制器设计及仿真分析

将非线性磁悬浮系统作为研究对象,建立合适的误差函数,在逼近过程中合理利用RBF神经网络,制定自适应律,该控制方法能够大大加强整个系统的抗干扰性。并开展仿真研究。突加负载下,NNDAC控制恢复所需的时间为0.068、动态降落为1.2×10^(-5)m;相较于SMC和PID控制恢复速度依次提升38.2%、77.3%,动态降落依次下降64.7%、76%NNDAC有效降低悬浮高度的变化幅度,增强其抗干扰性。模拟端部效应扰动下,NNDAC控制曲线几乎没有任何波动,且相较于SMC和PID控制其动态降落分别减小85.7%与95%,NNDAC控制有利于平缓气隙高度的波动,加强系统的抗干扰性。

基于径向基函数神经网络算法的高频转阀阀芯稳定性

针对伺服电机驱动高频转阀时受液动力矩变化影响造成高频输出精度下降的问题,以液压马达作为动力源,提出一种基于径向基函数神经网络算法的转阀阀芯转速控制策略。首先,搭建高频转阀阀芯转速控制系统的数学模型;其次根据数学模型在MATLAB/Simulink平台搭建仿真模型,对不同算法作用下阀芯转速控制特性进行仿真分析;最后建立高频转阀转速控制系统实验台,对不同算法作用下阀芯转速控制特性进行实验研究和理论验证。结果表明:与常规PID控制方法相比,基于径向基函数神经网络的高频转阀转速控制策略转速控制系统阶跃响应所需调整时间最少为0.16 s,超调量小;三角波与正弦波转速跟踪误差均值下降最大值分别为46.51%、53.69%;6 MPa、10 MPa下,转速稳态误差均值分别下降34.92%、38.26%。径向基函数神经网络算法有效提高了高频转阀阀芯转速控制精度。

基于滑模神经网络直接瞬时转矩控制的优化

针对开关磁阻电机在滞环控制策略中存在的较大转矩脉动问题,在传统直接瞬时转矩控制的基础上引入了反向传播(BP)神经网络控制策略,以转矩误差的平方为性能指标函数对直接瞬时转矩控制进行优化调整,抑制了电机在运行时的转矩脉动。进一步,在整体双闭环控制策略基础上,引入滑模控制对转速环节进行改进,提高了系统的响应速度和鲁棒性。最后,利用Matlab/Simulink对传统直接瞬时转矩控制和改进直接瞬时转矩控制进行仿真测试,验证了所提策略的有效性和可行性。

环形绕组结构开关磁阻电机自选相神经网络无位置传感器控制方法

针对环形绕组结构开关磁阻电机单相采用神经网络角度估计时,转子角度的某些位置估计误差较大的问题,提出了一种适用于环形绕组结构开关磁阻电机的自选相神经网络无位置传感器控制方法。该方法通过分析神经网络拟合的特点,结合转子位置的对称性,采取神经网络输入量优化和电感分区等措施,减小了转子估计误差,实现了对转子位置的准确估计。最后通过仿真验证了所提方法的有效性。

喷雾测量系统坐标架三维位移控制研究

为解决航天发动机试验中,通过三维PDA(Phase Doppler Anemometry)测量系统研究燃料喷注器雾化效果中对测量点精确定位的问题,采用自适应模糊神经网络PID控制算法实现对测量系统三维坐标架的步进电机进行控制。建立了一个步进驱动电机进行位置控制的传递函数模型,通过Matlab的Simulink仿真平台对步进驱动电机位移的PID控制、模糊PID控制及模糊神经网络PID控制的三种算法进行仿真。模拟实验结果表明:模糊神经网络PID控制将响应时间提升到0.05 s,并且大幅减小超调。相比于传统PID此方法可大幅提高坐标架定位的速度和精度。

基于紧格式动态线性化的吊舱推进电机滑模矢量控制

针对复杂海况下吊舱推进电机存在的模型建立困难以及负载扰动问题,提出了基于紧格式动态线性化的滑模矢量控制方法。首先,考虑船舶实际工况建立包含船舶运动模型和螺旋桨负载特性的船-桨数学模型,并通过紧格式动态线性化方法,建立带时变参数和负载扰动项的吊舱推进电机数据模型;然后,设计基于最小转速跟踪误差指标的滑模控制器,同时设计神经网络观测器对控制律中的未知负载扰动项进行估计,并将估计值反馈到控制器中进行扰动补偿。仿真结果表明,所设计的控制器减小了推进电机的转速超调、转矩脉动,提高了吊舱推进电机系统的控制性能。