带滞后补偿的快速适应自校正调节器

本文提出了一种Smith预估自校正算法,它是把Smith预估器引入到自校正调节器中,直接使用系统模型构成自校正调节器。这种自校正调节器具有快速适应性、跟踪精度高、辨识参数少和稳定工作等优点。     

自校正预估PID控制器

本文将Clarke和Gawthrop提出的自校正k步前向预估器代替Smith预估控制器,导出自校正预估PID控制器算法,并通过仿真验证了算法的有效性。     

多变量解耦预估自校正控制器及其应用

本文在 Chien 的工作基础上提出了一种多变量解耦预估广义最小方差自校正控制器.该算法可克服变量间的干扰,实现静、动态解耦并提高系统的稳定性.由于同时引入了对角矩阵解耦法及 Smith 预估器,自校正控制器的设计过程可简化为单变量无时延系统.仿真研究与实际应用表明:本算法响应速度快、超调小,结果是令人满意的.     

智能自适应控制及其在轧辊退火炉上的应用

本文结合轧辊退火炉计算机温度智能自适应群控系统,研究了被控对象的数学模型、自 适应控制算法、智能控制器设计及有关控制结果.所提出的控制算法,一种为PID自校正调 节器(PIDSTR),另一种为输入输出维数不等的多变量极点配置自校正预测控制器(MPSTPC). 智能控制器采用产生式结构,参与过程的复合调节.实控结果表明,系统鲁棒性增强,跟踪和 调节性能好,控温偏差和炉内温度场不均匀度均在士3℃以内.     

一类时滞系统的自校正模糊Smith控制器设计

Smith预估控制是解决时滞系统控制的有效方法,但其对系统模型参数的精确要求限制了其应用.而模糊控制本质上属于PD控制,无法消除系统的稳态误差.文中设计了一种自校正模糊Smith 控制器,结合模糊控制、Smith预估器及自校正控制的优点,既保持了模糊控制鲁棒性较强的特点,又消除了系统的稳态误差,且改善了系统的动态性能.仿真结果表明了自校正模糊控制器的有效性和优越性.     

一种简单的多变量自校正控制器及其在电加热炉上的应用

本文提出一种新的多变量自校正控制器,该控制器将最小方差策略和极点配置策略结合起来,从而具有两者的优点;该控制器可以控制具有不同传输延时的系统;算法简单。本文讨论了该自校正控制器在双输入双输出电加热炉上的工程应用。实时控制结果表明,该控制器不仅能适应电网电压波动等因素引起的模型参数的改变,控温效果优于常规PID调节器,而且使被控变量之间的耦合作用大大减小。     

基于退火遗传算法的传感器非线性校正方法

针对实际传感器的非线性问题,结合遗传算法和模拟退火算法的优点,提出了一种基于退火遗传算法的传感器非线性校正方法.该算法通过计算传感器自校正方程中的待定常数,实现非线性特性的线性化.实验结果表明:该方法不但可以实现非线性校正,而且校正后的精度也高于传统的最小二乘法.     

基于PSO的变结构变时滞自校正控制

针对变结构、变时滞被控对象,将粒子群优化(PSO)算法与广义最小方差相结合,采用实时自校正过程对其进行控制,提出基于PSO自校正控制器算法.该算法应用隐式辨识方式,可减少辨识计算量,通过跟踪误差来改变辨识精度.以工业上典型的一阶、二阶和三阶系统的结构变化并伴随着有时滞突变的复杂被控对象进行仿真,并和基于最小二乘的传统自校正控制方法比较得知,在运用PSO自校正控制器的控制下,系统输出量与期望输出之间的方差趋于更小,控制跟随性和鲁棒性均较好.仿真结果表明该自校正控制器的有效性与应用价值.     

工业过程控制算法设计与仿真

传统的Smith预估器在理论上解决了纯滞后系统控制问题,但由于它必须依赖被控对象精确的数学模型,在实际应用中存在局限。要克服Smith算法的局限性,采用一种具有比例、积分和微分神经元的PID神经元网络(PIDNN)与Smith预估器结合的算法,利用Smith对纯滞后系统进行预估补偿以及PID神经元网络的自适应、自学习和在线自调整控制器的参数的功能,解决大纯滞后系统的控制问题。仿真结果表明该Smith-PIDNN算法简单,稳定且收敛速度快,能够很好地改善大纯滞后系统的性能指标。     

基于自适应Smith算法的烧制窑温控策略

陶瓷、页岩砖、硅砖烧制过程中的烧制窑温度控制是过程控制研究主要课题.自适应Smith算法在对强扰动系统、大滞后温度系统的控制上有一定优势.该文提出一种自适应Smith算法,构成一种新的自适应Smith烧制窑温度控制策略.软件仿真及实际应用表明:该自适应Smith算法是一种有效解决温控问题的算法,抗干扰能力强,应用前景广...     

一种基于Matlab的参数自调整模糊控制器的设计方法

本文介绍了一种在MATLAB的模糊控制工具箱中,通过编写S函数实现对量化因子和比例因子的在线自动调整来设计模糊控制器,从而有效地实现参数自调整模糊控制器的设计方法。为了验证参数自调整模糊控制器的优越性,分别进行了空调温度控制系统的PID控制、常规模糊控制和参数自调整模糊控制的仿真研究。结果表明,参数自调整模糊控制器较之常规的模糊控制器,在被控对象特性变化或较大扰动的情况下,控制系统能保持较好的性能,是一种较理想的控制方法,具有广阔的发展前景。     

基于模糊神经网络的积分自适应控制

将模糊神经网络应用于传统线性积分自适应控制,构造了一类模糊神经自适应方法,用于消除非线性系统响应偏差.模糊神经网构成直接非线性自适应控制器.对线性及非线性对象的仿真控制以及与经典自适应控制的比较,表明了模糊神经自适应控制器的有效性.     

Robust adaptive speed regulator with self-tuning law for surfaced-mounted permanent magnet synchronous motor

This study exhibits a self-tuning speed control scheme for the surface-mounted permanent magnet synchronous motor (SPMSM) against the parameter variations through the multivariable approach. The proposed method has two novelties. The first one is to combine the adaptive controller with the self-tuning algorithm so as to make the decay ratio of the tracking errors higher in the transient period. The second one is to provide a systematical way to find a robust stabilizing control gain corresponding the speed and current tracking errors by solving an optimization problem. The efficacy of the proposed method was experimentally investigated using a 3-kW SPMSM.     

基于模式识别的自整定控制及应用

本文提出一种基于模式识别的自整定控制方法,以模式表示和模式分类来描述系统的动态特性和调节器的结构,用模式识别优化方法求取调节器的参数,当系统动态模型参数未知时,这种自整定方法是建立在系统测量数据序列基础上的。该方法在实验性二元精馏塔上用IBM-PC微机获得成功应用。其结果优于常规PID调节器。     

用继电自整定实现模糊PID智能控制

从提高控制器的智能化水平出发，文中提出了模糊PID自适应控制与继电自整定相结合构成PID双模智能控制器的方法。即用继电自整定法整定出PID控制的初始参数，然后切换到模糊PID自适应控制，完成模糊PID智能控制。将该算法应用于一温控系统中，得到了令人满意的效果。     

A multivariable self-tuning controller

A multivariable self-tuning controller is derived extending the scalar version of Clarke and Gawthrop (1975). Because the control terms are penalized in the cost function, fluctuations and peaking in control signals are reduced compared with the multivariable minimum variance self-tuning controller (Borisson, 1979). Time-varying reference signals as well as certain nonminimum-phase systems can be handled without difficulty with the proposed controller. Several examples illustrate the power of the derived self-tuning controller.     

Position Control of a Flexible Manipulator Using a New Nonlinear Self-Tuning PID Controller

In this paper, a new nonlinear self-tuning PID controller(NSPIDC) is proposed to control the joint position and link deflection of a flexible-link manipulator(FLM) while it is subjected to carry different payloads. Since, payload is a critical parameter of the FLM whose variation greatly influences the controller performance. The proposed controller guarantees stability under change in payload by attenuating the non-modeled higher order dynamics using a new nonlinear autoregressive moving average with exogenous-input(NARMAX) model of the FLM. The parameters of the FLM are identified on-line using recursive least square(RLS) algorithm and using minimum variance control(MVC) laws the control parameters are updated in real-time. This proposed NSPID controller has been implemented in real-time on an experimental set-up. The joint tracking and link deflection performances of the proposed adaptive controller are compared with that of a popular direct adaptive controller(DAC). From the obtained results, it is confirmed that the proposed controller exhibits improved performance over the DAC both in terms of accurate position tracking and quick damping of link deflections when subjected to variable payloads.     

隐式多变量广义自校正控制器

本文提出了具有在线选择加权阵的两种多变量自校正控制器.加权阵的在线选择和自校 正控制算法都采用隐式方案来实现.本文还证明了该自校正控制器具有全局收敛性.     

Self-tuning optimum start control of heating plant

The problem of controlling the start-up of heating plant in industrial and domestic buildings is considered and the need for a self-tuning start control scheme is explained. Using a simple semi-empirically derived relationship between the time required to preheat the building before occupancy and the measured temperatures, a self-tuning start controller is developed based on an on-line adaptive predictor. The controller assumes no prior knowledge of the thermal characteristics of the heating plant and building, and is suitable for low-cost microprocessor implementation. Results obtained from a hybrid simulation of a heating plant, building and environment show that the self-tuning controller behaves well and has superior performance characteristics compared to conventional start controllers. The results of preliminary experimental tests, using a microprocessor-based controller, suggest that, in certain circumstances, further simplifications to the control algorithms can be made so that implementation on small single-chip microcomputer systems, which are suitable for the control of domestic heating equipment, is feasible.     

A Real Time Self-Tuning Motion Controller for Mobile Robot Systems

This paper proposes an intelligent controller for motion control of robotic systems to obtain high precision tracking without the need for a real-time trial and error method. In addition, a new self-tuning algorithm has been developed based on both the ant colony algorithm and a fuzzy system for real-time tuning of controller parameters. Simulations and experiments using a real robot have been addressed to demonstrate the success of the proposed controller and validate the theoretical analysis. Obtained results confirm that the proposed controller ensures robust performance in the presence of disturbances and parametric uncertainties without the need for adjustment of control law parameters by a trial and error method.