被控过程

本文以PID控制能实现的最小方差为基准，当被控过程模型未知，通过对正常运行数据拟合模型，再估计PID能实现的最小方差及其相应的PID控制器参数。

When the process model is unknown , estimate the PID-achievable minimum variance and corresponding parameters by routine closed-loop operation data .

CBR方法不需对被控过程建模，而是利用历史经验案例解决新遇到的问题，并能动态更新案例库。

The CBR method solves new problems using historical cases without modeling of the controlled process , and update the case base dynamically .

在实际过程中，被控过程大多具有非线性、时变性和不确定性等特点，常规PID控制难以满足控制的要求。

In the practical operation , most of control processes have characteristics of non-linear , time-varying and uncertain . Conventional PID control can not meet the control requirements .

本文提出一种克服时变大时滞、扰动及被控过程参数时变的极点配置最优预报自校正PID控制算法。

A pole assignment optimal predictive self-tuning PID controller which can be used for the plants with time-varying large delay , disturbances and time-varying parameters is presented in this paper .

用模糊聚类辨识方法建立被控过程的TS模糊模型及其逆模型，引入内模控制，形成模糊内模控制策略。

This paper constructs TS fuzzy model and its inverse model based on FCM clustering techniques for controlled process and forms fuzzy internal model control strategy .

鉴于传统的基于继电反馈的PID参数整定方法，需要被控过程有较大的过冲，存在着一种对资源的浪费，文中提出了一种避免过冲控制过程的PID参数整定方法。

The traditional relay-based PID feedback tuning method , you need to have large controlled processes overshoot , there is a waste of resources . This paper proposes a control process without overshoot tuning method of PID parameter .

研究含时变大时滞、扰动及被控过程参数时交的控制综合问题，提出了神经元自适应预测PID控制设计算法，该算法采用预测控制克服时滞，采用智能方法优化PID控制器的参数。

The controller synthesis problem of unknown time varying delay system with disturbance and time-varying parameters is investigated . An adaptive neuron PID controller with optimal prediction is proposed , which conquers time delay based on prediction controller and optimizes parameters based on intelligent method .

在过程控制中，经常遇到高阶有自平衡被控过程。

High-order self-balancing controlled process is met in process control .

高阶有自平衡被控过程的状态积分反馈

State integral feedback with high-order self-balancing controlled process

开环是指这样的条件：控制器不与被控过程相连接。

Open loop refers to the condition in which the controller is disconnected from the process .

该界面能直观的显示被控过程，并形成各参数的曲线，以便记录和比较。

The interface can show the control process directly and form variable curve to record and compare .

模糊控制是一种不依赖于被控过程数学模型的仿人思维的控制技术。

Fuzzy control is a kind of human imitating technique which is independent onthe controlled plant mathematical model .

本论文从保证实时计算机控制系统可靠性出发，分析计算时间延迟对被控过程的影响，论述实时控制系统中硬期限的推导过程。

In this study , how to derive the hard deadlines of the real-time control system is proposed .

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。

A single neuron PID controller mathematical model is formed by taking values of output error square as performance index .

讨论了被控过程动态方程的建立、参数的辨识、最小方差闭环控制以及双微型机联机分级处理等问题。

The problems discussed are the derivation of the dynamical expression of the process , the identification of parameters ; minimal variance control ;

三容液位过程具有大时滞等特性，可以在很大程度上模拟实际工业生产过程中整体或局部的被控过程。

The three-tank level process with a big time lag can largely simulate the whole or partial practical controlled objects during the industrial processes .

它的最大特点是采用智能化的模糊模型预估器作为被控过程的内部模型，对实际输出起预测作用，从而克服时滞对系统带来的不利影响。

The most excellent characteristic of the system is that it adopts the intelligent fuzzy predictive controller as the internal model of the controlling process .

针对被控过程存在状态多重时滞，提出一种状态多重时滞系统的预测控制算法，给出了系统的稳定性和鲁棒稳定性条件。

A new predictive control algorithm is presented for state multi-time-delay systems . Sufficient conditions of stability and robust stability of the closed system are given .

并可以通过调整内模控制器的滤波器时间常数来补偿被控过程的模型不确定性所导致的控制品质下降。

As result , the poor control performance caused by the mismatch between plant and the process model can be improved by adjusting the time constant of the filter .

针对被控过程的非线性、时变性和复杂性，提出了一种模糊神经控制器与动态辨识器组成的控制系统。

In view of the nonlinearity and complexity of the controlled process , a control system is proposed which consists of a fuzzy neural network controller and dynamical identifier .

然而，与其它基于预测模型的模型预测控制一样，预测函数控制仍然存在一旦预测模型与实际被控过程出现不一致时，会出现控制性能下降的问题。

However , same as other model predict control , once predict model happens not in line with actual controlled process , it would meet with degrade of controlling performance .

本文针对树脂聚合反应是一类不确定性系统的特点，提出了对被控过程模型依赖性较小的预测控制和模糊控制算法，并给出了工业现场的实际运行输出响应曲线，证明了方法的有效性。

This paper describes the application of the predictive and fuzzy algorithms to the control of Resin Polymerization Processes . The experimental results show that the processes can be controlled effectively .

通过分析被控过程产生反向响应的内在机理，基于过程阶跃响应系数利用预测函数控制算法实现反向响应过程的控制。

After analyzing the characteristics of inverse response process , the step response model is used to serve as the internal model , then predictive functional control algorithm is proposed for inverse response process .

因此，本文通过实际对象实验得到炉温的阶跃响应由线，获取被控过程的时间常数、纯滞后时间和比例系数，建立近似的数学模型。

Thus the temperature step response experimentation is done , the time constant , dead time , and magnification factor of the controlled process are obtained . The approximate mathematical model is then established .

本文提出的改进型模型参考自适应预估控制算法是克服大时滞、有扰动及被控过程参数时变的有效新型算法。

An algorithm for modified model-referenced adaptive predictor control is presented in this paper . This new effective algorithm is very suitable for control system with large delay , disturbance , and time-varying parameters .

模糊控制就其实质而言属于非线形控制，它既适用于简单的控制对象，又可用来对机理较为复杂的被控过程进行仿人智能控制。

It works well with the simple control objects , for the much complex procedures about which not enough is known yet or too intricate to peer through , the Fuzzy control succeeds as well .

针对多输入多输出被控过程同时存在状态滞后和控制输入滞后问题，利用预测函数控制算法对预测模型没有特殊要求和在线计算量小的特点，基于预测函数控制设计机理得到系统的控制律。

By taking advantages of little online computation and loose requirement for the predictive model of the predictive functional control algorithm , a predictive algorithm is proposed for multi-input and multi-output systems with state delays and input delays .

为了解决实际应用中非线性被控过程的预测控制问题，把神经网络、广义预测控制和极点配置技术融为一体，提出一个新型自适应神经网络广义预测极点配置加权控制器（GPPWC）。

For solving predictive control questions of nonlinear system in practical application , the neural network , generalized predictive control and pole assignment technology were integrated , and a new adaptive neural network predictive pole assignment weighted controller was proposed .

同时，对于存在乘性不确定性的被控过程，分析了控制系统满足标称性能和鲁棒稳定性的充要条件，并由此确定出控制器参数的调节范围。

Meanwhile , sufficient and necessary conditions for holding the control system nominal performance and robust stability were analyzed in the presence of the process multiplicative uncertainty , and consequently , the tuning range of the controller parameters was obtained .

通过对已知被控过程模型和控制结构信息进行分析，定量展示出了被控过程包含的耦合和时滞因子对系统性能的不利影响，并进一步解析设计了几类先进的解耦控制系统。

By analyzing the known information about controlled process and control structure , the negative effects of process interaction and time delays on the system performance are quantitatively depicted , and subsequently , several advanced decoupling control schemes are developed .