Adaptive Quasi-PID Control Method for Switching Power Amplifiers

Quasi-PID control method that is able to effectively inhibit the inherent tracking error of PI control method is proposed on the basis of a rounded theoretical analysis of a model of switching power amplifiers (SPAs). To avoid the harmful impacts of the circuit parameter variations and the random disturbances on quasi-PID control method, a single neuron is introduced to endow it with self-adaptability. Quasi-PID control method and the single neuron combine with each other perfectly, and their formation is named as single-neuron adaptive quasi-PID control method. Simulation and experimental results show that single-neuron adaptive quasi-PID control method can accurately track both the predictable and the unpredictable waveforms. Quantitative analysis demonstrates that the accuracy of single-neuron adaptive quasi-PID control method is comparable to that of linear power amplifiers (LPAs) and so can fulfill the requirements of some high-accuracy applications, such as protective relay test. Such accuracy is very difficult to be achieved by many modern control methods for converter controls. Compared with other modern control methods, the programming realization of single-neuron adaptive quasi-PID control method is more suitable for real-time applications and realization on low-end microprocessors for its simple structure and lower computational complexity.

Model Reference Adaptive Controller for Simultaneous Voltage and Frequency Restoration of Autonomous AC Microgrids

In an autonomous droop-based microgrid,the system voltage and frequency(VaF)are subject to deviations as load changes.Despite the existence of various control methods aimed at correcting system frequency deviations at the secondary control level without any communication network,the challenges associated with these methods and their abilities to simul-taneously restore microgrid VaF have not been fully investigated.In this paper,a multi-input multi-output(MIMO)model reference adaptive controller(MRAC)is proposed to achieve VaF restoration while accurate power sharing among distributed generators(DGs)is maintained.The proposed MRAC,without any communication network,is designed based on two methods:droop-based and inertia-based methods.For the microgrid,the suggested design procedure is started by defining a model reference in which the control objectives,such as the desired settling time,the maximum tolerable overshoot,and steady-state error,are considered.Then,a feedback-feedforward con-troller is established,of which the gains are adaptively tuned by some rules derived from the Lyapunov stability theory.Through some simulations in MATLAB/SimPowerSystem Tool-box,the proposed MRAC demonstrates satisfactory perfor-mance.

An Advanced FMRL Controller for FACTS Devices to Enhance Dynamic Performance of Power Systems

The parameters of power system slowly change with time due to environmental effects or may change rapidly due to faults. It is preferable that the control technique in this system possesses robustness for various fault conditions and disturbances. The used flexible alternating current transmission system （FACTS） in this paper is an advanced super-conducting magnetic energy storage （ASMES）. Many control techniques that use ASMES to improve power system stability have been proposed. While fuzzy controller has proven its value in some applications, the researches applying fuzzy controller with ASMES have been actively reported. However, it is sometimes very difficult to specify the rule base for some plants, when the parameters change. To solve this problem, a fuzzy model reference learning controller （FMRLC） is proposed in this paper, which investigates multi-input multi-output FMRLC for time-variant nonlinear system. This control method provides the motivation for adaptive fuzzy control, where the focus is on the automatic online synthesis and tuning of fuzzy controller parameters （i.e., using online data to continually learn the fuzzy controller that will ensure that the performance objectives are met）. Simulation results show that the proposed robust controller is able to work with nonlinear and nonstationary power system （i.e., single machine-infinite bus （SMIB） system）, under various fault conditions and disturbances.

Two New Control Signal Approaches for Obtaining the MRAS-CDM and a Real-time Application

The coefficient diagram method （CDM） is one of the most effective control design methods. It creates control systems that are very stable and robust with responses without the overshoot and small settling time. Furthermore, all control parameters of the control systems are changed by varying some adjustment parameters in CDM depending on the demands. The model reference adaptive systems （MRAS） are the systems that follow and change the control parameters according to a given model reference system. There are several methods to combine the CDM with MRAS. One of these is to use the MRAS parameters as a gain of the CDM parameters. Another is to directly use the CDM parameters as the MRAS parameters. In the industrial applications, the system parameters can be changed frequently, but if the controller, by self-tuning, recalculates and develops its own parameters continuously, the system becomes more robust. Also, if the poles of the controlled systems approach the jw axis, the response of the closed-loop MRAS becomes more and more insufficient. In order to obtain better results, CDM is combined with a self-tuning model reference adaptive system. Systems controlled by a model reference adaptive controller give responses with small or without overshoot, have small settling times, and are more robust. Thus, in this paper, a hybrid combination of MRAS and CDM is developed and two different control structures of the control signal are investigated. The two methods are compared with MRAS and applied to real-time process control systems.

非对称液压缸对称性控制

阀控非对称缸两个运动方向上系统的开环增益及某些参数的不同,使得两个方向上的动态特性不对称,这种本质上的非线性和非对称性给系统控制带来困难.PБ-211机器人后3个关节是对称阀控制非对称液压缸伺服系统,为了使机器人运行平稳和提高控制精度,在控制中必须加以适当补偿.在分析非对称液压缸工作原理基础上,考虑液压缸伸出和缩回性能不同,提出了模型参考变增益自适应控制算法对非对称液压缸进行补偿.该算法包括单神经元自适应PID控制、模型参考自适应控制和输出增益的自动调节3个部分.仿真分析和实验表明该算法可以基本实现非对称液压缸的对称性控制,较普通PID控制算法具有明显优势.

单参数PID的Hebb学习控制

基于模型的设计方法可以获得较为满意的控制性能,但这种方法在设计难以建模或模型时变的系统时会遇到很多困难。本文对简化的Z-N方法进行了根轨迹分析,表明:对于常见的过程控制对象,基于简化Z-N方法整定的单参数单神经元PID存在使系统稳定的可行性解。利用人工神经网络的Hebb学习规则对PID增益进行学习,可以实现常见工业过程的无模型自适应控制。与其他单神经元PID相比,PID调整参数少。仿真和实验表明,本文算法可以获得较好的仿真效果,具有满意的实验性能指标。

基于模糊神经模型的自适应单神经元控制系统设计

提出一种基于模糊神经模型的自适应单神经元控制系统。该控制系统首先根据采集到的输入输出数据建立被控过程模型,并在此基础上引入单神经元控制器。通过李亚普诺夫方法对控制器参数进行在线调节,从而使得系统输出值能够较快跟踪设定值。理论分析和仿真结果表明:本文提出的单神经元控制器和传统的PID控制器具有极其相似的结构,因此,具有结构简单、易于操作的特点,具有较快的跟踪速度,并且控制参数可以在线调节。

基于模型参考自适应的单神经元PID控制器设计

将神经网络和模型参考自适应控制系统相结合,研究基于模型参考自适应理论永磁同步电动机(PMSM)BP神经网络速度辨识的单神经元比例积分微分(PID)速度控制器。该系统基于模型参考自适应理论利用BP神经网络辨识器得到的参数信息,使用单神经元PID控制器取代传统PID控制在线调整权值。根据李亚谱诺夫稳定性理论验证了该控制器的稳定性,仿真与实验研究证明该方法改善了PMSM速度控制系统的动态响应与自适应控制能力。

火电机组主蒸汽温度神经模糊-PID串级控制

如何将机组主蒸汽温度快速地控制在额定范围之内是火电厂热工过程控制面临的主要难题。针对超临界锅炉机组的主蒸汽温控制问题,提出一种基于神经模糊模型的火电机组主蒸汽温度自适应PID控制方案。根据采集到的减温水流量、高温过热器入口温度、主蒸汽流量、主蒸汽温度数据建立机组主蒸汽温度神经模糊模型,以此模型为基础实施主蒸汽温度自适应PID串级控制。内环采用PI控制器克服内部扰动,从而减少扰动对于导前汽温的影响,再将内环PI闭环控制系统及惰性区作为广义被控对象实行单神经元PID控制,通过主蒸汽温度额定值和实际输出值之间的误差对控制器参数进行在线调节,最终达到对过热器的控制。仿真实例验证了所提出的方法的有效性。

基于递归小波神经网络的无人机反演控制

针对无人机受到参数摄动和外界扰动的问题,为提高系统的稳定性,提出一种采用递归小波神经网络的无人机反演控制方法。引入二阶参考模型对输入受限问题建立数学模型,仿真状态量和输入量的幅值、速率和带宽约束。采用递归小波神经网络逼近面向控制模型中的非线性函数,弱化控制律对模型的依赖。并将神经网络逼近方法与自适应反演设计方法相结合,建立无人机在复杂情况下的输入受限自适应反演控制方法。通过控制系统的轨迹跟踪仿真分析,验证了改进方法的可行性和有效性,提高了无人机飞行控制的实时性和鲁棒性。

基于模型参考自适应的单相脉冲整流器无网压传感器控制方法

该文以单相脉冲宽度调制整流器为研究对象,以实现无网侧电压传感器控制为研究目的,提出一种基于模型参考自适应(model reference adaptive system,MRAS)的网压估算方法。首先,根据MRAS的基本原理,分别建立参考模型和可调模型中系统有功、无功功率数学模型,从而实现电网电压基波幅值、频率及角度的三角函数的估算。在此基础上,结合基于功率前馈解耦直接功率控制,给出基于MRAS的无网压传感器控制方法。然后,针对该方法对网侧电感参数敏感性问题,给出一种电感参数在线辨识算法。同时,针对控制系统启动问题,给出系统参数初始化设计方案。最后,对所提方法进行计算机仿真和实验验证,通过仿真和实验结果验证该方法的正确性和有效性。

农用车辆单神经元自适应PID轨迹跟踪控制

为了实现复杂环境下的农用车辆路径跟踪控制,提出了基于单神经元自适应PID的路径跟踪控制方法。首先,建立了农用车辆运动学模型和电动助力转向系统模型,为后续的控制算法的设计打下模型基础。其次,设计了单神经元自适应PID控制策略,为了消除频繁控制引起的振荡,根据实际转角控制精度的要求,设置合理死区作为转角跟踪误差,利用带监督的赫步学习规则对神经元进行训练,在Matlab/Simulink环境下构建控制模型。试验和仿真结果表明,基于SNAPID的农用车辆路径跟踪控制精度较高,在直线行驶工况下,最大偏差在3.2cm以内,平均偏差在0.92cm以内;在曲线行驶工况下,最大偏差在4.3cm以内,平均偏差在1.03cm以内。

基于MRAS的双馈风力发电机无速度传感器直接转矩控制

针对速度传感器安装不方便、成本高,磁链观测对电阻依赖性强的问题,提出一种基于模型参考自适应策略的速度、电阻自适应转子磁链观测器,该观测器以转子电流和转子磁链为状态变量,应用李雅普诺夫稳定理论获得转速自适应律,实现转子转速和定子电阻辨识。建立了Matlab/Simulik环境下的双馈风力发电机无速度传感器直接转矩控制系统的仿真模型,仿真结果表明该方法可实现转子磁链的精确辨识,有效减少双馈发电机的定子电阻对系统特性的影响,提高了系统的稳定性和鲁棒性。

用于电力故障波形重现的逆变器建模及其自适应控制

从频域角度对用于故障波形重现的逆变器进行建模和分析。通过引入导通时间偏移量和时间–电压转换系数的概念提出一种改进模型,实现了占空比到输出电压的线性变换;克服了近似模型由于线性化造成的频带受限。引用能消除固有跟踪误差的准PID控制器对改进模型进行控制,为消除参数漂移等对其性能的影响,提出了基于"有感知器监督的Hebb学习规则"的单神经元自适应准PID控制器,很好地改善了控制性能,满足了故障波形重现的精度要求;且该算法运算量小,适合于实时实现和现有软件的更新升级。

基于神经网络延时预测的自适应网络控制系统

针对网络控制系统存在着随机、时变、不确定的信息传输延时,采用带有时间戳的线性神经网络(TSLNN)进行在线延时预测,实时地获得当前采样周期的网络传输延时预测值.该方法选取3个先验的网络实测延时值作为神经网络的输入样本,选用widrow-hoff学习规则作为神经网络的训练算法;应用网络传输延时预测值,并采用一阶Pade方法,对数学模型中的延时环节进行线性化处理,从而获得无刷直流电机调速网络控制系统的线性数学模型;最后,利用模型参考自适应控制方法(MRAC)设计闭环控制器.仿真结果表明,将基于TSLNN在线延时预测的MRAC方法应用于无刷直流电机调速网络控制系统中,可以获得令人满意的系统动、静态性能.

先进PID控制在组态王上的扩展与应用

针对组态王不具备控制组态功能,需要用户进行二次开发的情况,提出了为组态王扩展先进PID控制功能的方法。用Matlab和VB混合编程开发单神经元PID控制器ActiveX控件,并嵌入到组态王应用程序中,通过脚本来访问ActiveX控件的方法和事件,完成对过程控制系统的实时控制。仿真实验证明了该方法操作简单、成本低,充分利用了软件资源,提高了生产过程控制精度。

基于DSP的电脑刺绣机主轴电机伺服控制系统

介绍了一种为实现最大刺绣效率所采用的,基于单神经元自适应控制的电脑刺绣机主轴交流伺服驱动系统。主轴电机采用交漉异步电机,设计了单神经元直接模型参考自适应下的,空间矢量控制方式速度控制策略,并通过DSP实现。仿真和实际验证表明,所设计的控制策略及交流伺服驱动系统能够使刺绣机主轴电机跟踪随机速度、位置控制信号,可在较大的范围内实现无级平滑调速,满足了高效刺绣的工艺要求,是一种新型的性价比高的电脑刺绣机的主轴控制系统。

基于ActiveX控件的InTouch控制功能扩展

针对目前组态软件InTouch控制功能不够完善,需要用户进行二次开发的情况,提出一种组态软件扩展控制功能的方法。用Matlab COM生成器生成先进控制算法COM组件,在Visual Basic中开发调用算法组件的控制器ActiveX控件,并将控件引入到组态软件InTouch中,为其扩展单神经元自适应PID控制器功能。仿真实验证明了该方法的可行性。

热电偶检定炉炉温模型参考自适应控制的研究

热电偶校检定炉在对热偶进行高温点的校验时,炉温难于稳定,主要原因在于控高温时加热时间较长环境温度产生较大变化,从而造成检定炉加热模型产生时变,导致控制器效果明显变差,本文利用模型参考自适应控制方法在线调整PID控制器参数以适应环境温度变化而造成的加热炉模型时变,试验结果表明该方法适应性良好,从而为热电偶检定炉的温度控制提供了新的方法。

基于模型参考自适应的直线电机转速辨识

针对直线永磁同步电动机全闭环控制系统易受干扰、降低系统性能指标、存在不稳定因素等问题,提出了基于模型参考的自适应模糊神经网络在线辨识方法,用梯度法实时修正模糊控制器的输入和输出隶属度参数,建立了模型参考自适应模糊神经网络速度伺服系统模型,给出了模糊神经网络控制器的设计方法。仿真和实验结果表明:该方法提高了速度检测装置的分辨率和动态响应能力,系统具有很强的鲁棒性。