Application of single neuron adaptive PID controller during the process of timber drying

The paper presents a method of using single neuron adaptive PID control for adjusting system or servo system to implement timber drying process control, which combines the thought of parameter adaptive PID control and the character of neural network on exactly describing nonlinear and uncertainty dynamic process organically. The method implements functions of adaptive and self-learning by adjusting weighting parameters. Adaptive neural network can make some output trail given hoping value to decouple in static state. The simulation result indicates the validity, veracity and robustness of the method used in the timber drying process

Application of Single Neuron Adaptive PID Regulators with Auto-tuning Gain in Industrial Parameter(Pressure)Closed-loop Process Control Systems

A type of single neuron adaptive PID regulator with auto-tuning gain is proposed and applied to the work control of fans, waterpumps and air-pressers etc. in Handan Iron & Steel Compel China. The robusthess of induStrial parameter closed-loop process controlsystems is improved, and the work quality of the systems bettered.

Improved single-neuron PID controller for DC motor applied in robot system

An improved single-neuron proportional integral derivative （ PID ） controller and a new method to build the DC motor system were presented in the article. In the simulation, the robot arm is considered as an external load to DC motor. Both the motor module and the load module are crea- ted in Simulink to achieve simulation results closer to real robot system. In this way, it can well veri- fy the performance of the improved single-neuron PID controller, which is a combined controller of normal PID controller and single-neuron PID controller. Besides, an intelligent switcher can help to realize the function of choosing a better control algorithm according to motor＇ s velocity output. Sim- ulated results confirm the rapid and stable response of the improved PID controller. Moreover, the improved single-neuron PID controller has an excellent ability to overcome the load impact and su- press the jamming signals. At last, a GUI interface platform is built to make the controller easier to be applied in other robot systems.

Self-adaptive PID controller of microwave drying rotary device tuning on-line by genetic algorithms

The control design, based on self-adaptive PID with genetic algorithms(GA) tuning on-line was investigated, for the temperature control of industrial microwave drying rotary device with the multi-layer(IMDRDWM) and with multivariable nonlinear interaction of microwave and materials. The conventional PID control strategy incorporated with optimization GA was put forward to maintain the optimum drying temperature in order to keep the moisture content below 1%, whose adaptation ability included the cost function of optimization GA according to the output change. Simulations on five different industrial process models and practical temperature process control system for selenium-enriched slag drying intensively by using IMDRDWM were carried out systematically, indicating the reliability and effectiveness of control design. The parameters of proposed control design are all on-line implemented without iterative predictive calculations, and the closed-loop system stability is guaranteed, which makes the developed scheme simpler in its synthesis and application, providing the practical guidelines for the control implementation and the parameter design.

An Intelligent Single Adaptive Neuron Controller Based on Pattern Recognition

The trajectory of the controlled system in phase space has been investigated, and different learning methods are applied to the single adaptive neuron controller according to the pattem of the control system. The advantage of the controller presented has been shown by simulation of a satellite attitude stability control system.

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.

基于Adaptive Interaction理论的PID控制器的设计及应用

本文在Adaptive Interaction理论的基础上,提出了一种新的自调整 PID 控制器。这种新的控制器根据输入及其误差信号进行在线训练,通过误差评价函数的最小化,在模型未知的情况下能很好地调整比例、积分、微分三个参数。对于被控对象的变化具有鲁棒性,很大程度上解决了传统的 PID 控制器对于非线性、不稳定系统控制效果不佳及在线调整困难的问题。通过仿真实例,验证了应用 Adaptive Interaction 理论的 PID 控制器的有效性和实用性。

足式机器人腿部关节改进单神经网络PID控制算法研究

为了满足液压足式机器人在复杂环境中实现精确、快速的腿部关节控制需求,把单神经网络PID能够实时调节参数的优点运用到足式机器人液压机械腿关节的控制中,在单神经网络PID的基础上增加机械腿关节的位置和速度控制算法,形成改进单神经网络PID,实现了对神经元比例参数自调整、PID参数的自整定,能够较好地适应内、外参数的变化,增强了腿部关节的快速性、精确性。在Simulink中进行建模仿真以及在设计的以STM32为中央处理芯片的控制平台上进行实验测试,结果表明:改进单神经网络PID在足式液压机器人的腿部关节控制中具有响应速度快、超调量小、控制精度高、鲁棒性强等优点。

基于单神经元自适应PID控制的高炮随动系统扰动补偿

为提升自行高炮随动系统的抗干扰能力以及自行高炮行进间射击精度,采用了一种基于单神经元自适应PID的控制方法,利用神经元的自学习及自校正能力对控制效果进行改善,以提升随动系统的扰动补偿能力。仿真结果表明:当高炮在B级、D级以及F级路面下行驶时,采用该方法的随动系统输出曲线的波动范围分别减小了0.14%、0.65%和4.72%,在突加扰动的情况下,再次跟踪到目标信号的时间分别缩短了0.04 s、0.03 s和0.02 s,根据实验数据可知在单神经元自适应PID控制器的作用下,当系统受到干扰后能够更快地跟踪到目标信号,且稳态精度更高。

中频加热电源模糊自适应PID控制系统的设计与实现

针对中频感应加热电源控制系统存在着严重的非线性、参数时变性以及滞后性和惯性缺陷,同时考虑采用常规PID控制很难取得较好的控制效果,设计了一种模糊自适应PID控制系统。该系统结合模糊控制和常规PID控制两种算法的优势,利用模糊控制在线性整定和修改PID的控制参数,较好地解决了系统的存在问题,获得了良好的动态性能和静态性能。在分析模糊自适应PID控制算法的基础上,详细介绍了模糊化处理、模糊控制规则建立表以及模糊判决和决策表等,分别给出了模糊自适应PID控制参数在线整定程序流程,上位机程序的设计方法,系统硬件组成和工作原理,以及主要部件特性等。通过对系统MATLAB仿真分析,其结果表明:该系统与常规PID控制相比较,具有控制精度高、响应速度快、稳定性好的特点,明显改善了中频感应加热电源的控制效果,具有一定的推广价值。

基于模糊自适应PID控制的磨煤机系统

使用发电厂钢球磨煤机进行实验,收集相关数据,获取辨识结果,以其为研究对象,在MATLAB/Simulink平台上分别搭建磨煤机控制系统的PID控制和模糊自适应PID控制的仿真模型获取仿真效果图像。针对控制系统的强相关性,提出单位矩阵法对该系统进行动态解耦经解耦可获得三个单回路,为三个单回路分别建立控制系统,优化对磨煤机出口温度控制、入口负压控制以及负荷控制方案。仿真结果表明:模糊自适应PID控制能有效改善控制效果。

变速风力发电机组的模糊-单神经元PID控制

对风力发电机组的数学模型及常规PID控制器进行了分析设计,由于变速风力发电机组的转速变化范围很宽,表现出高度的非线性性和时变性,常规的PID控制器难以在全范围内得到理想的控制性能。为此,提出采用单神经元智能控制器来替代常规的PID控制器,以改善机组控制性能。在分析单神经元控制器的结构和控制原理基础上,为了进一步提高单神经元控制器的动静态性能,引入了模糊控技术,实现了单神经元控制器输出增益的参数自整定。在仿真基础上,建立了一套完整的实验系统,对几种控制方法进行了实验研究。仿真和实验结果表明,基于模糊自整定的单神经元控制器可有效地改善风力发电机组的控制性能,具有较强的自适应能力和鲁棒性。

单神经元自适应PID控制交流调速系统

针对转子磁场定向矢量控制中转速PI调节器鲁棒性能较差的问题,提出了用单神经元自适应PID控制器代替转速PI控制器,进一步改善了异步电动机矢量控制系统的性能；将无监督的Hebb学习规则与有监督的Delta学习规則相结合,提高了单神经元自适应PID控制器的学习能力,实现了单神经元控制器的参数优化与在线自调。构造了基于单神经元自适应PID控制器和空间矢量脉宽调制(SVPWM)的异步电机矢量控制系统。仿真实验结果表明,该系统不仅具有很好的静、动态性能,而且又具有很强的自适应性和鲁棒性。

基于单神经元的液压挖掘机自适应PID节能控制

在分析单神经元PID特性的基础上,设计了基于单神经元的液压挖掘机自适应PID节能控制器。该控制器通过神经元的自学习,对传统PID控制器的比例、积分、微分系数进行在线调整,实现了挖掘机功率匹配自适应控制。仿真结果显示单神经元PID自适应控制器具有较强的鲁棒性。

单神经元自适应PID控制器及其应用

研究了单神经元自适应PID控制器,阐述了该控制器的特点、控制律、适用对象及工程整定方法,在和利时公司的SmartPro系统平台上开发出单神经元自适应PID控制器,进行了单神经元自适应PID控制器的典型一、二阶对象闭环仿真,最终将单神经元自适应PID控制器应用于制药厂发酵罐温度控制回路中。单神经元控制器具有可调参数少、易于整定、控制输出平稳、鲁棒性强的独特优点,适用于大滞后且要求平稳控制输出的工业过程。

自动离合器位置跟踪神经元自适应PID控制

针对自动离合器位置难以准确快速跟踪控制的问题,在采用蜗卷弹簧作为补偿装置改善驱动机构性能基础上,设计了一种具有前馈作用的增益可调神经元自适应PID控制器,利用前馈控制加快控制器的响应速度,利用神经元的自学习功能实现权值的自动调整,采用PSD算法实现增益的自动调整。实验结果表明,与常规控制器相比,该控制器用于离合器位置跟踪控制动、静态误差小,能够适应离合器负荷和目标接合速度的非线性变化,具有较好的鲁棒性,能够满足离合器位置跟踪控制要求。

单神经元自适应PID控制器的研究

将二次型性能指标引入单神经元并利用自学习功能构成了自适应 Ｐ Ｉ Ｄ 控制器；分析了神经元自适应控制系统的稳定性和学习算法的收敛性．仿真结果表明，这种控制方案具有良好的自适应性．

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。

基于单神经元的盾构推进速度自适应PID控制

在分析单神经元PID特性的基础上,设计了基于单神经元的盾构推进液压系统速度自适应PID控制器。该控制器通过神经元自学习,可对传统PID控制器的比例、积分、微分系数进行在线调整,实现了盾构掘进机推进速度的自适应控制。仿真结果显示单神经元自适应PID控制器具有较强的鲁棒性,对盾构推进之类非线性过程具有较好的控制效果。

基于单神经元PID的挖掘机铲斗位姿自适应控制

为实现挖掘机作业过程自动控制,需要对铲斗位姿准确定位。建立了挖掘机工作装置运动学模型及电液控制系统模型,针对挖掘机电液控制系统的时变性与外界干扰的不确定性特点,设计了一个单神经元自适应PID控制器,并采用二次型性能指标对控制器参数进行在线优化。仿真试验表明,该控制器响应快速、平稳,对参数时变与不确定扰动具有一定鲁棒性,能有效地对挖掘机铲斗位姿进行精确控制。