基于模糊PID算法的电阻炉温度控制系统设计

以单片机为主体,设计一个能处理较复杂数据和控制功能的电加热炉温度控制系统.该系统具有自动检测、数据实时采集、处理及控制结果显示、打印等功能.软件采用模糊控制算法.自适应在线修改PID参数,构成模糊自整定,结合了模糊控制快速性,保持较小超调量的优点和PID控制方法成熟,可消除稳态误差.     

基于自整定PID的烘干炉温度控制软件设计与仿真

传统的烘干炉温度控制系统在烘干过程中,烘干炉温度保持恒温,并不利于产品整体的烘干,而为了达到更好的效果,其温度应由低到高逐渐升高,以利于溶剂的充分挥发.本文分析了PID控制和模糊控制的优缺点,将PID控制和模糊控制的优点结合起来,采用模糊规则在线整定PID的PK、IK、DK三个参数的模糊自整定PID控制方法.基于模糊自整定PID控制算法的控制系统有相当好的灵活性,能进行数据实时采集、利用模糊PID算法进行处理及控制结果显示等功能,可以获得较高的控制精度.     

基于模糊控制理论的PID闭环控制系统的研究与设计

为了满足各种复杂控制任务,适应市场环境的千变万化,快速、准确及优质地完成各种工业控制要求,研究设计了一套基于模糊控制理论的PID闭环控制系统。系统设计以PID算法原理为基础,以数字化为实现方法,运用模糊控制理论,并实现控制系统的参数自整定。研究搭建相应的RC一阶电路硬件调试平台,通过相关软件设计,完成PID闭环控制器的调试,实现基于模糊控制理论的PID控制系统的相关功能,试验过程中使用高精度的AD/DA转换器确保系统的数字化精度要求,足以提升系统的适用性和有效性。实验表明,基于模糊控制理论的PID闭环控制系统能满足各种工业控制要求,具有较强的现实意义。     

模糊PID控制在直流调整系统中的应用

对双闭环直流调速系统中的控制方法进行了研究。目前双闭环调速系统所采用的大多是传统PID调节控制器,针对该类控制器在实际应用中处理动态非线性变化性能不佳的缺点,提出将模糊控制理论与PID技术相结合,设计了模糊-PID双控制器方案,改进后的方案既保留了常规PID控制器对稳态时静差的处理能力,又具备模糊控制对系统动态非线性变化的处理能力。仿真实验证明,该方法比传统的PID控制策略具有更好的控制性能。     

基于模糊PID控制的半导体激光器温控系统

基于模糊控制原理,采用模糊控制与PID控制相结合的模糊控制方法,完成了一种针对半导体激光器温度控制的算法设计。该模糊PID控制算法,能够自适应调节PID的比例、积分和微分系数,从而使半导体制冷器的温度保持恒定。Simulink仿真结果表明,采用该模糊PID控制算法后系统的超调量减少40%,缩短了调节时间,控制效果优于常规PID控制系统。     

自适应模糊PID在温度控制中的应用

温度控制具有非线性、大惯性、时变性等特点,常规PID控制和常规模糊控制还不能使温度的控制达到理想效果。设计一种自适应模糊PID控制器,该控制器在大偏差范围内采用模糊控制,根据偏差和偏差变化的需要实时调整PID参数,小偏差范围内采用PID精确控制。介绍自适应模糊PID控制器的构成和原理,并利用Matlab／Simulink和模糊逻辑工具箱对其进行仿真,仿真结果表明,这种自适应PID控制器既具有模糊控制灵活、响应快、适应性强的特点,又具有PID控制精度高的特点,改善了温度控制的效果。     

模糊PID控制系统的设计与研究

为了解决工程中二阶系统的控制问题,在此对PID控制与模糊控制的原理进行了研究,并将二者的优势相互结合,设计了一种具有参数自整定功能的模糊PID控制系统。对PID参数初值的确定,隶属度函数的选取,模糊控制规则表的设计做了较为深入的研究。并利用Matlab/Simulink软件对控制系统进行了仿真研究。对阶跃输入下PID控制系统与该文设计的模糊PID控制系统的响应情况做出了定量的比较。结果表明对于二阶延迟系统,模糊PID控制器的超调量与调节时间均小于传统的PID控制,能显著提高控制效果。     

基于BP神经网络的模糊PID控制器的设计

工业过程控制中广泛采用PID控制,但传统PID控制因其控制参数的固定,在线整定难等问题。为此本文研究了一种新的自适应模糊PID控制方法,为了解决模糊推理没有学习能力的问题,本文又提出了一种基于BP神经网络的自适应模糊控制方法。此方法是模糊控制、神经网络和PID控制的有效结合。仿真实验表明,这种基于BP神经网络的模糊PID控制算法具有良好的控制效果。     

模糊自适应PID控制算法分析

基于模糊控制和自适应PID控制的模糊自适应PID控制算法可在线实时调整PID参数,使系统具有模糊控制的灵活性和适应性强的优点,又具有PID控制精度高的优势。结果表明,系统动态特性好,鲁棒性强,实现简单。     

基于模糊神经网络的PID控制器研究

工业控制经常会用到一些经典的PID控制,PID控制有很多优点比如应用范围广、参数较易整定、鲁棒性好等.但是随着科技的发展,工业控制对控制对象的要求越来越高,PID控制有时候很难满足控制要求.因此文章在原有的PID控制器的基础上,加入模糊控制、神经网络控制,优化其控制系统.模糊神经网络PID控制器通过实验证明控制效果有很大的提升.模糊控制、神经网络控制结合一起起到互补的效果,比单独的PID控制系统性能更好.     

A Type-2 Fuzzy Switching Control System for Biped Robots

In this paper, a type-2 fuzzy switching control system is proposed for a biped robot, which includes switched nonlinear system modeling, type-2 fuzzy control system design, and a type-2 fuzzy modeling algorithm. A new switched system model is proposed to represent the continuous-time dynamic and discrete-event dynamic of a walking biped as a whole, which is helpful to analyze the closed-loop stability of the biped locomotion. A type-2 fuzzy switching control system is proposed for the switched system model to guarantee the gait stability and to achieve a robust control performance with a simplified control scheme. Finally, we propose a new fuzzy c-mean variance algorithm for the type-2 fuzzy system modeling to capture the variance of each clustering means, which can translate random uncertainties of original data into rule uncertainties. Simulation results are reported to show the performance of the proposed control system model and algorithms.     

Fuzzy logic techniques for rendezvous and docking of two geostationary satellites

Assembling large structures in space requires the ability to manage rendezvous and docking operations. In future these techniques will be required for the gradual build up of big telecommunications platforms in geostationary orbit. This paper discusses the use of fuzzy logic to model and implement a control system for the docking/berthing of two satellites in geostationary orbit. The system mounted in a chaser vehicle determines the actual state of both satellites and generates torques to execute manoeuvres to establish the structural latching. The paper describes the proximity operations to collocate the two satellites in the same orbital window, the fuzzy guidance and navigation of the chaser approaching the target, and the final fuzzy berthing. The fuzzy logic system represents a knowledge-based controller that realizes the closed-loop operations autonomously, replacing the conventional control algorithms. The goal is to produce smooth control actions in the proximity of the target and during the docking to avoid disturbance torques in the final assembly orbit. The knowledge of the fuzzy controller consists of a data base of rules and the definitions of the fuzzy sets. The knowledge of an experienced spacecraft controller is captured into a set of rules forming the rules data base.     

火控计算机系统可靠性的模糊故障树分析

传统故障树分析方法需要获取事件发生概率的精确值,忽略了数据的不完备性和模糊性.在火控计算机系统可靠性研究中提出模糊故障树分析方法.建立火控计算机系统的故障树,采用三角模糊数描述基本事件发生概率,计算得到顶事件发生概率的置信区间,并采用中值法计算基本事件的模糊重要度.结果更加符合实际情况,表明该方法用于可靠性分析合理可行.     

CASCADED FUZZY SYSTEM AND ITS ROBUST ANALYSIS BASED ON SYLLOGISTIC FUZZY REASONING

Syllogistic fuzzy reasoning is introduced into fuzzy system, and the new Cascaded Fuzzy System(CFS) is presented. The thoroughly theoretical analysis and experimental results show that syllogistic fuzzy reasoning is more robust than all other implication inferences for noise data and that CFS has better robustness than conventional fuzzy systems, which provide the solid foundation for CFS‘s potential application in fuzzy control and modeling and so on.     

Design of a fuzzy traffic controller for ATM networks

This paper presents the design of a fuzzy traffic controller that simultaneously manages congestion control and call admission control for asynchronous transfer mode (ATM) networks. The fuzzy traffic controller is a fuzzy implementation of the two-threshold congestion control method and the equivalent capacity admission control method extensively studied in the literature. It is an improved, intelligent implementation that not only utilizes the mathematical formulation of classical control but also mimics the expert knowledge of traffic control. We appropriately choose input linguistic variables of the fuzzy traffic controller so that the controller is a closed-loop system with stable and robust operation. We extract knowledge of conventional control methods from numerous analytical data using a clustering technique and then use this knowledge to set parameters of the membership functions and fuzzy control rules via fuzzy set manipulation (linguistically stated but mathematically treated) with the aid of an optimization technique named genetic algorithm (GA). Simulation results show that the proposed fuzzy admission control improves system utilization by a significant 11%, while maintaining the quality of service (QoS) contract comparable with that of the conventional equivalent capacity method. The performance of the proposed fuzzy congestion control method is also 4% better than that of the conventional two-threshold congestion control method     

波前校正的变论域模糊控制方法

自适应光学校正技术中常用的PID控制依赖于变形镜的响应模型,响应模型易受扰动影响因此标定过程较为复杂。针对此问题提出一种不依赖变形镜响应模型的变论域模糊控制方法,将哈特曼重构波前中提取的波面评价指标的残差参数与残差差分参数作为变论域模糊控制器的输入,对输出的比例积分微分参数进行自整定,实现自适应控制。利用Matlab进行变论域模糊控制方法与传统模糊控制方法进行对比的相关仿真。波前校正实验结果表明,变论域方法校正后波面数据优于传统模糊控制方法,波前残差的均方根值也更小。控制器系统性能实验中,传统模糊控制器的超调量偏大,而变论域模糊控制器的超调量几乎为零,调节时间也更短。变论域模糊控制方法在保证系统的鲁棒性,提升系统的快速性的同时,利用参数自整定的特性克服了扰动因素对变形镜响应模型造成的不利影响,提高了校正效率,对于波前校正更具有实用性。     

基于模糊理论的多传感器数据融合系统

利用模糊理论解决不确定性问题的优点,将其应用于数据融合系统;根据模糊控制数据融合模型,对模型中涉及到的容错处理、时空对准问题进行研究;分析了模糊理论在融合算法中的处理过程;并根据不同的对应情况加以讨论说明;最后通过仿真检验分析,证明该融合系统能有效地提高测量数据精度.     

基于鲁棒优化算法的小型无人机模糊控制器研究

简要论述了小型无人机的经典H2/H∞鲁棒优化算法,并在此优化控制算法基础上,对模糊控制器开展了研究,模糊控制器的知识库主要依据原型无人机的实验数据搭建。为了验证模糊控制器的控制品质和鲁棒性,在小型无人机的静态参数(高度、角度)控制回路上将模糊控制器与原型控制器进行了结合,使之成为鲁棒模糊控制器。对小型无人机鲁棒模糊控制系统参数的控制品质和鲁棒性进行了计算,并将这些特性和原型机进行了比较。仿真结果表明,采用模糊控制器的控制系统鲁棒性提高了近一个数量级。     

A novel driver assist stability system for all-wheel-drive electric vehicles

A novel driver-assist stability system for all-wheel-drive electric vehicles is introduced. The system helps drivers maintain control in the event of a driving emergency, including heavy braking or obstacle avoidance. The system comprises a fuzzy logic system that independently controls wheel torque to prevent vehicle spin. Another fuzzy wheel slip controller is used to enhance vehicle stability and safety. A neural network is trained to generate the required reference for yaw rate. Vehicle true speed is estimated by a sensor data fusion method. The intrinsic robustness of fuzzy controllers allows the system to operate in different road conditions successfully. Moreover, the ease of implementing fuzzy controllers gives a potential for vehicle stability enhancement.