Predictive Functional Control Based on Fuzzy Model: Comparison with Linear Predictive Functional Control and PID Control

The implementation of the fuzzy predictive functional control (FPFC) on the magnetic suspension system is presented in the paper. The magnetic suspension system was in our case the pilot plant for magnetic bearing and is an open-loop unstable process, therefore a lead compensator was used to stabilize it. The high quality control requirements were a-periodical step response and zero steady-state error. Adding the integrator to a feedback causes overshoot. The solution to the problem was cascade control with fuzzy predictive functional controller in the outer loop. To cope with the unknown model parameters and the nonlinear nature of the magnetic system, a fuzzy identification based on FNARX model was used. After successful validation the obtained fuzzy model was used for controller design. The FPFC is compared with a cascade linear predictive functional control (PFC) and PID control. The results we obtained with the FPFC are very promising and hardly comparable with conventional control techniques.     

A neural network-based proportional integral derivative controller

A new method of designing direct controllers of the PID type for nonlinear plants by using RBF neural networks is proposed, and its satisfactory performance is demonstrated through simulations. This method does not put too much restriction on the type of plant to be controlled, and it has a stable performance for the type of inputs for which it has been trained. Unlike backpropagation or other supervised methods of training, this approach does not require knowledge of the appropriate form of controller output for each given input, and neither does it require identification of the plant or its inverse model. The PID controller design methodology presented here has certain advantages over conventional methodologies.     

基于模糊控制器的LNB温度控制系统

针对低噪声功率放大器(LNB)温度控制系统非线性、大滞后、物理模型不精确等特性和对温度控制的要求，提出了一种带有自调整因子和比例积分校正环节的双模糊控制策略来实现温度控制，给出了DSP控制实现的具体方案。该双模糊控制器采用模糊推理完成两组控制器的平稳过渡。实验结果表明了该系统的控制效果优于常规PID控制器，满足LNB控制系统温度控制要求。     

气候环境实验室建模及温度控制系统仿真

气候环境实验室是一个复杂的热力系统,室内温度参数受多种因素的影响.通过分析气候环境实验室仓体、空调系统热交换器、温度传感器和调节阀的特性,推导出它们的传递函数,利用Matlab/Simu-link建立环境室温度控制系统的模型,根据临界比例度法整定PID参数,并对常规PID控制器和积分分离PID控制器的控制效果进行仿真比较.仿真结果表明该模型和温度控制系统在理论研究和工程实践中具有实际的指导意义和应用价值.     

原油脱水过程单神经元PID解耦控制研究

集输联合站的油水分离是保证产出原油质量的关键工艺过程。在对油水分离沉降过程动态特性深入研究的基础上,给出了其数学模型。联合神经网络PID控制和解耦系统的特点,给出了一种单神经元PID解耦算法。这种方法具有自学习和自适应能力,在一定程度上解决了传统PID调节器不易在线实时整定参数的不足,提高了控制器对系统和环境变化的适应能力。仿真结果表明这种单神经元PID解耦算法可有效改善控制质量,比常规的PID解耦具有更好的动、静态特性和鲁棒性。     

焦炉集气管压力的自校正控制

结合炼焦生产工艺,分析焦炉集气管压力的运行特点,建立了该被控对象的受控自回归(CAR)数学模型;结合自校正动态调整参数的优势和常规PID的优点,设计适合该模型的PID自校正控制器,并选取合适的递推参数估计算法;借助S7-300PLC的硬件高可靠、运算高速性和Step7软件的编程高效灵活性,在实际工程中实现了对集气管压力的过程控制。控制结果表明运行压力稳定,控制方法鲁棒性好,具有推广价值。     

热工过程时滞对象的神经网络内模控制

针对火电厂热工过程的时滞对象，提出采用基于神经网络的内模控制方法，即用神经网络对复杂系统的辨识能力来实现内模控制中被控对象的正模型及内模控制器。仿真研究表明，文中所采用的控制方案比常规PID控制表现出更好的控制品质，在实际应用中具有一定的实用价值。     

基于专家PID的锅炉蒸汽压力控制策略

采用基于专家PID控制方法,给出了母管制运行锅炉蒸汽压力系统新的控制策略。母管制运行锅炉的主要动态特性包括工况多变、非线性、时滞性、不稳定性和负荷干扰。它们无法建立精确的数学模型。针对常规控制方法难以实施有效控制的问题,将专家系统的思想和方法引入控制系统,通过系统模态辩识、控制性能辩识、知识推理,给出了PID控制器参数的整定和优化方法。该控制策略应用于母管制运行锅炉压力系统,其母管压力稳态偏差≤±0.1MPa,在30 s内,20%额定负荷扰动下,其主要参数满足运行工艺要求。     

基于模糊控制的锅炉汽包水位串级控制系统

锅炉汽包水位控制系统是火力发电厂中一个重要的热工控制系统，它的正常运行直接关系到电厂的安全、经济运行。本文首先对锅炉汽包水位控制系统采用三冲量串级控制系统进行论证，之后分别用常规PID控制器和GENERAL模糊控制器，采用ITAE控制指标对该系统进行实时控制。最后，用MATLAB软件对该系统进行仿真。仿真结果表明，相比于常规PID控制器，模糊控制器具有良好的控制特性。     

针对广义控制对象的一维鲁棒PID控制器

在现代鲁棒控制理论基础上，针对广义控制对象提出一种一维鲁棒ＰＩＤ控制器设计方法，其优点是控制器参数可以解析得到，控制器只有一个可调的参数，被调参数与系统的鲁棒性有直接的联系，特别地控制器可以很好地用于大纯滞后控制对象。仿真结果表明本文方法与其他方法相比具有本质的改进。     

Model reference adaptive controller for enhancing depth of penetration and bead width during Cold Metal Transfer joining process

In this paper, an adaptive control scheme is employed for joining Aluminium 6061 alloy sheets by Cold Metal Transfer (CMT) process. The transfer function model of the CMT welding system is derived using empirical equations. The CMT plant transfer function is estimated using system identification technique. For the estimated plant model, a conventional PID controller is initially designed by tuning the controller parameters. The designed control system is tested for its ability to control the welding current when short circuit phase and arcing phase are detected. Following the conventional PID controller, a Model Reference Adaptive Controller is implemented to maintain the welding current at desired range during melting and electrode wire short circuiting. The performance analysis for the proposed adaptive control scheme and the conventional PID controller is compared. The simulation results indicate that the conventional PID controller is unable to retrieve the desired current during short circuit phase and arcing phase. Nevertheless, the proposed MRAC for CMT process successfully maintains the welding current at the setpoint when subjected to arcing phases and short circuit respectively, while ensuring arc stability. The experimental validation is carried out in the CMT welding set up using the designed MRAC. The experimental results emphasize that the MRAC improves the welding performance by yielding good weld joints swiftly and enhanced quality besides minimizing the design complexities.     

电加热炉系统预测函数PID控制算法的研究

由于电加热炉系统具有大时滞、时变性、非线性等特点,提出了一种新型预测函数PID控制算法.该算法将PID控制算法和预测函数控制算法结合起来,通过预测被控对象的未来输出值,得到一个新的优化目标函数,实时优化控制参数,得到控制量的解析解.仿真结果表明,与常规PID控制和预测函数PID串级控制相比,所设计的控制器满足系统对快速性和稳态精度的要求,系统具有较强的抗干扰性和鲁棒性,能够实现对电加热炉系统的有效控制.     

模糊PID复合控制在变频空调中应用研究

空调房间温度控制是一复杂的控制过程,传统的模糊或PID控制很难得到较好的控制效果,模糊控制和PID控制适当结合控制空调房间温度可使控制效果大大提高。本文建立了房间温度控制模型,介绍了模糊和PID控制并联结合方式并对各复合控制器进行了设计。对设计的变频空调控制系统进行了仿真,结合仿真图,从鲁棒性和稳定性方面分析了设计的各模糊和PID复合控制器控制效果。     

Multi-loop design of multi-scale controllers for multivariable processes

Based on the recently proposed (SISO) multi-scale control scheme, a new approach is introduced to design multi-loop controllers for multivariable processes. The basic feature of the multi-scale control scheme is to decompose a given plant into a sum of basic modes. To achieve good nominal control performance and performance robustness, a set of sub-controllers are designed based on the plant modes in such a way that they are mutually enhanced with each other so as to optimize the overall control objective. It is shown that the designed multi-scale controller is equivalent to a conventional PID controller augmented with a filter. The multi-scale control scheme offers a systematic approach to designing multi-loop PID controllers augmented with filters. Numerical studies show that the proposed multi-loop multi-scale controllers provide improved nominal performance and performance robustness over some well-established multi-loop PID controller schemes.     

A practical implementation of Robust Evolving Cloud-based Controller with normalized data space for heat-exchanger plant

The RECCo control algorithm, presented in this article, is based on the fuzzy rule-based (FRB) system named ANYA which has non-parametric antecedent part. It starts with zero fuzzy rules (clouds) in the rule base and evolves its structure while performing the control of the plant. For the consequent part of RECCo PID-type controller is used and the parameters are adapted in an online manner. The RECCo does not require any off-line training or any type of model of the controlled process (e.g. differential equations). Moreover, in this article we propose a normalization of the cloud (data) space and an improved adaptation law of the controller. Due to the normalization some of the evolving parameters can be fixed while the new adaptation law improves the performance of the controller in the starting phase of the process control. To assess the performance of the RECCo algorithm, firstly a comparison study with classical PID controller was performed on a model of a plate heat-exchanger (PHE). Tuning the PID parameters was done using three different techniques (Ziegler–Nichols, Cohen–Coon and pole placement). Furthermore, a practical implementation of the RECCo controller for a real PHE plant is presented. The PHE system has nonlinear static characteristic and a time delay. Additionally, the real sensor's and actuator's limitations represent a serious problem from the control point of view. Besides this, the RECCo control algorithm autonomously learns and evolves the structure and adapts its parameters in an online unsupervised manner.     

专家PID控制在过程控制装置上的仿真与应用

简单介绍了常规PID控制的优缺点和专家控制的基本原理,介绍了专家PID控制的系统结构,针对过程控制装置中的液位对象,经过系统辨识得出数学模型。基于Matlab仿真和组态王控制软件进行应用实现,仿真和应用实现结果均表明,专家PID控制具有比常规PID更好的控制效果,且具有实现简单和专家规则容易获取的优点。     

PID控制在火箭炮伺服系统中的仿真实现——改进的专家自适应PID控制

针对传统PID（Proportional-Integral-Derivative）控制无法兼顾部分系统的静态性能和动态性能,结合专家PID控制原理,提出了一种改进的专家自适应PID控制器的设计方案,对某火箭炮伺服系统进行仿真跟踪。给出了伺服系统的分析设计过程,利用MATLAB/Simulink完成了改进的专家自适应PID控制器在某伺服系统中的仿真应用,得到了良好的跟踪特性图,说明了该方法的有效性。     

Nonlinear system PID-type multi-step predictive control

A compound neural network was constructed during the process of identification and multi-step prediction. Under the PID-type long-range predictive cost function, the control signal was calculated based on gradient algorithm. The nonlinear controller‘ s structure was similar to the conventional PID controller. The parameters of this controller were tuned by using a local recurrent neural network on-line. The controller has a better effect than the conventional PID controller. Sinmlation study shows the effectiveness and good performance.     

在线知识获得智能模糊控制器研究

对一些复杂的系统。传统PID或模糊控制很难得到满意控制效果,本文提出采用基于RBF神经网络和遗传算法的自适应模糊控制器来进行控制。由遗传算法在线优化模糊控制器的比例因子、模糊推理规则和隶属函数。并由RBF网络辨识被控对象的动态特性,以评价模糊控制器控制性能。仿真实验表明。优化后的Fuzzy控制器具有较强的学习和自适应控制能力,控制效果优于没有寻优的Fuzzy控制。     

一种分数阶预测控制器的研究与实现

本论文研究了一种新型预测控制器RTD-A的分数阶实现方法及应用. 与常规控制器比较, RTD-A控制器具有参数意义明确, 易于整定和实施的优点. 论文将RTD-A控制器扩展到分数阶形式, 并与经Z-N法整定的分数阶PI?D1控制器和Wang-Juang-Chan法整定的PID控制器进行了比较. 所提出的分数阶预测控制器在设定值跟踪, 克服负荷扰动, 鲁棒性等方面都有较理想的控制性能. 仿真结果验证了这种分数阶预测控制器的有效性.