AN EVALUATION OF THE GAUTAM-MUTHARASAN CONTROL ALGORITHM

This work experimentally verified and evaluated a relatively new direct-digital control algorithm by Gautam and Mutharasan (G-M) which was designed to perform equally well for load and for set-point changes and to accommodate shifting process parameters. The algorithm was compared to a Datilin controller and a discrete P-I law for temperature control on a steam-water heat exchanger, operating each algorithm in cascade with a Dahlin steam-flowrate controller. Based upon integral criteria, the G-M algorithm showed marked improvement over the other two, especially for load disturbances affecting process parameters. However, ringing was found to be a problem     

A nonlinear pi(d) controller

The PID control algorithm remains the most widely used in the process industries. This is due to its simplicity (three tuning parameters, fixed structure) and its ability to satisfy the majority of process control needs. Here, a new nonlinear algorithm (the PI(D)) is presented, which maintains the simplicity of the PID, but is nevertheless able to outperform it in both load and set point changes. Tuning formulae and response curves for the PI(D) controller are presented.     

Constraint handling optimal PI control of open-loop unstable process: Analytical approach

This paper proposes the closed-form analytical design of proportional-integral (PI) controller parameters for the optimal control of an open-loop unstable first order process subject to operational constraints. The main idea of the design process is not only to minimize the control performance index, but also to cope with the constraints in the process variable, controller output, and its rate of change. To derive an analytical design formula, the constrained optimal control problem in the time domain was transformed to an unconstrained optimization in a parameter space associated with closed-loop dynamics. By taking advantage of the proposed analytical approach, a convenient shortcut algorithm was also provided for finding the optimal PI parameters quickly, based on the graphical analysis for the optimal solution of the corresponding optimization problem in the parameter space. The resulting optimal PI controller guarantees the globally optimal closed-loop response and handles the operational constraints precisely.     

抄纸过程模型参考自适应内模控制仿真研究

针对强耦合、大时滞、非线性对抄纸过程控制性能的影响,提出一种模型参考自适应内模控制算法,通过建立控制器模型,设计自适应调节律和控制算法来实现对增益和时滞的在线自调整,使系统在模型参数失配的情况下,仍具有良好的鲁棒性和跟踪性,表现出较强的解耦能力和自适应能力。通过与常规内模控制仿真结果对比,表明该控制算法改善控制品质的有效性。     

电镀电流高精度控制系统的设计与实现

为提高电镀电流的稳定性和精度,提出了一种基于单神经元的电镀电流控制算法。介绍了电镀控制系统,包括主电路和主控制器。主电路为电镀过程提供直流电源,主控制器则确保电镀参数的准确性。以电流调控为主要研究对象,将单神经元控制与PID相结合,设计了一种单神经元PID控制器,其参数可自适应调整,能够适应复杂多变的工作环境。仿真和实验结果表明:电镀电流的控制精度明显提高,该控制系统具有响应速率快、控制输出稳定等优点,有利于提升镀件质量。     

一种新型的浊度控制算法

针对城市供水过程中,待滤水浊度控制过程的大惯性、大滞后、模型参数时变和干扰多等特点,采用一种新型的控制算法来实现对该过程的控制,其具体思想是:分别从对设定值的跟踪性要求和对扰动的抑制性要求出发,采用双控制器分别实现各自的控制,且两个独立的控制器分别采用常规PI和带非线性补偿的PI算法。并将该种新型控制算法和传统PID反馈控制算法、Smith预估控制算法和双PI控制算法进行了仿真比较。最后,还将该种算法实际应用到某水厂的中试实验基地。仿真和实验结果表明:所采用算法能够及时克服扰动,并对模型参数变化不敏感,具有很强的鲁棒性,对设定值具有良好的跟踪能力,确保了供水质量的稳定性。     

Intelligent control of a grain drying system using a GA-SVM-IMPC controller

The grain drying is difficult to control because of its characteristics of long delay, strong nonlinearity, and uncertainty parameters. The aim of the paper is to design a suitable controller for a newly designed grain dryer. First, a nonlinear math model of the wheat mixed flow drying was established and analyzed based on the fundamental laws of simultaneous heat and mass transfer. The simulations for the batch circulating drying process and the continuous grain drying process have been made and the simulation results show that it fits well with the actual drying process. Second, an internal model proportional integral derivative (PID) controller (IMPC) based on the support vector machine (SVM) algorithm and the genetic algorithm (GA-SVM-IMPC) was proposed from the view of the energy loss and the dried grain quality. The structure of the GA-SVM-IMPC controller consists of a SVM prediction model, a SVM inverse model controller, a PID controller and a genetic optimization algorithm. Finally, the effectiveness of this controller was demonstrated by computer simulations, and the comparative study with the other controllers further confirmed the superiority of the proposed dryer controller.     

FPGA based QDMC control for reverse-osmosis water desalination system

Model-based predictive control (MPC) is a widely used advanced control algorithm in process industries, such as chemical plants and oil refineries. Quadratic dynamic matrix control (QDMC) is one of the popular MPC strategies. In this paper, the QDMC controller is used to control a simulated reverse-osmosis (RO) water desalination system with spiral wound element (SWM). A cascaded control system was designed with the QDMC controller and a PID controller for the desalination process, where the QDMC controller optimizes the set point of the PID controller and directly controls one output. In order to make the implementation more convenient, this paper also studies the implementation of QDMC control system based on a field programmable gate array (FPGA) chip. A support software is developed to help engineers adjust the parameters of the QDMC controller. Since the QDMC controller is implemented in an embedded system, the overall system cost is reduced, which is helpful to the application of RO desalination system.     

基于模糊神经网络的污水处理生化除磷过程控制

针对污水处理生化除磷过程中出水总磷难以实时达标的问题,提出了一种基于模糊神经网络(fuzzy neural network,FNN)的出水总磷控制方法。首先,通过分析污水处理生化除磷机理,确定了控制器的操作变量为生化反应池第五分区外部碳源(external carbon, EC)与溶解氧(dissolved oxygen, DO)传递系数。其次,设计了一种基于FNN的出水总磷控制器,采用梯度下降算法更新控制器参数;最后,将基于FNN的出水总磷控制器应用于污水处理过程基准仿真平台(benchmark simulation model No.1,BSM1),实验结果表明,基于FNN的出水总磷控制器能够保证出水总磷的达标排放,具有较好的控制效果。     

一阶时滞不稳定对象的PID控制器多目标优化设计

针对一阶时滞（FOPDT）不稳定对象,提出了一种基于参数稳定空间的多目标遗传算法进行PID控制器的优化设计。该算法首先采用两级控制结构,以保证PID控制器参数稳定空间的存在性,然后,基于该稳定空间提出了一种多目标遗传算法,用来求解带约束的多目标PID控制器参数整定问题。数值例子和生物反应器仿真研究表明所提方法的有效性。     

基于DMC-PID的循环流化床锅炉床层温度控制设计与实现

首先分析了循环流化床锅炉燃烧部分床层温度多种影响因素,得到在各严重耦合关系影响因素下床层温度变化过程难以建立一个准确的数学模型来描述。针对此特点,设计采用蒸汽负荷作为前馈信号的床层温度-燃料量串级控制方案。其中床层温度控制器采用动态矩阵算法,具体地给出可实现的动态矩阵控制算法过程。介绍了VC通过OPC接口技术与DCS控制系统进行数据交换,实现了VC环境下的DMC-PID控制算法在循环流化床锅炉床层温度中的成功应用,并证明了该算法的实用性、有效性和可靠性。     

Multi‐controller scheme for load rejection and set‐point tracking

To acheive complete compensation for loads, a novel multi‐controller scheme with feedforward control is proposed. This scheme has four controllers, a set‐point controller, two load controllers, and a feedforward controller. This results in the separation of the load response from the set‐point response in a closed‐loop system. These four controllers can then be designed independently to achieve good system performance for both set‐point tracking and load rejection. One of the load controllers can be chosen as a proportional controller; this guarantees physical realizability and provides excellent compensation. The results of simulation and real time control show that the proposed multi‐controller scheme is superior to a double‐controller system and a Smith predictor in the presence of large uncertainty in process dynamics especially for load disturbances.     

湿磨干烧水泥生产线分解炉温度控制

湿磨干烧水泥生产线的分解炉中生料量波动大,利用HoneywellC200系统自带的带规PID无法满足温度控制工艺要求,本工作利用线性神经元参数自学习PID控制和前馈控制,通过Control Build编制成控制模块,下载到底层控制器实现温度控制,与利用API函数编制用户算法在上层计算机上运行相比,该方案克服了采样和控制周期受计算机负菏影响而变化的缺陷,而不受通讯影响,实际应用结果表明了本文提供出的控制方法的有效性。     

一种新型多智能体固定目标控制算法

提出一种新型的多智能体固定目标控制算法。某个智能体到达固定目标的附近区域时,在该智能体与目标之间增加反馈控制器,使得智能体与固定目标之间的通信量减少。最后通过仿真证明了该算法的有效性。     

一类化工过程多变量系统的自适应非线性预测控制

针对化工过程的一类多变量非线性系统,提出了一种自适应非线性预测控制(ANMPC)算法。在采用递归最小二乘法进行预测模型参数在线辨识的基础上,将系统的静态非线性关系用一个反向传播(BP)神经网络稳态模型来表示,通过稳态模型求得的动态增益来进一步校正预测模型的参数。详述了ANMPC控制器设计步骤,通过在一个多变量pH中和过程中的仿真验证了本算法的可行性和有效性。     

PVC胶槽温度模糊PID控制系统

为提高PVC胶槽温度控制的可靠性和精度,将模糊算法与PID算法结合并应用于PVC胶槽温度控制系统中,通过两种控制算法的优势互补,利用模糊控制算法求得PID参数修正系数,实现PID控制器3个参数的在线自调整。仿真结果表明:PVC胶槽温度模糊PID控制器的控制性能明显地优于常规PID控制器,温度控制精度达到±1℃,满足了PVC胶槽温度参数动态变化实时调节控制的需要,具有较好的自适应调节和鲁棒性。     

基于IGA和Hammerstein模型的非线性系统广义预测控制

针对非线性程度较高的系统,设计了一种广义预测控制器。该设计基于Hammerstein模型的动静态可分离特性,首先利用具有全局搜索能力的免疫遗传算法（IGA,Immune Genetic Algorithm）在线辨识模型的一些关键参数,然后运用广义预测控制策略实现对该系统的预测控制。仿真试验结果表明,该设计能够准确预测,而且稳定性好、稳态误差小。     

基于MLP网络的智能控制及应用

讨论了一种基于ＭＬＰ网络的智能控制系统。它利用一个多层感知器（ＭＬＰ）作为被控对象的在线辨识器，而用另一线性神经网络结构构成ＰＩＤ控制器。采用变步长ＢＰ加速算法求得多层感知器的各联接权重与阀值的学习规律与控制器参数的自整定算法。通过化肥厂变换炉控制的仿真研究与一列管换热器温度控制系统的仿真试验，说明该算法是有效的。     

塑料挤出机的温度自适应滑模控制系统

作为塑料挤出过程的关键参数,塑料挤出机的温度在实际操作中存在非线性和滞后性,严重影响了温度控制的稳定性和控制精度。基于塑料挤出机的整体式料筒建模,并设计了自适应滑模温度控制系统。由于滑模控制器对参数变化和外界干扰不敏感、控制器结构相对简单而被广泛应用于工程实践。自适应滑模控制在普通滑模控制的基础上,进一步采用自适应律以自动适应实际系统参数的变化,具有更高的控制精度和系统鲁棒性。研究采用自适应滑模算法对整体式料筒温度设计相应控制器,并通过仿真实验验证了控制器的控制精度和鲁棒性。     

FUZZY GAIN SCHEDULED CSTR WITH GA-BASED PID

In this article, a new control scheme, the gain scheduled genetic algorithm (GA)-based PID is proposed for a continuous stirred tank reactor (CSTR). A CSTR is a highly nonlinear process that exhibits stability in certain regions and instability in other regions. The proposed control scheme implements the characteristics of the genetic algorithm's (GA) global optimization to optimize the PID's three control parameters, k_p, k_i, k_d, to obtain the best control effect by minimizing the integral square error online. The PID controller parameters tuned by the GA for each region are gain scheduled by a fuzzy logic scheduler. Fuzzy gain scheduling is a special form of fuzzy control that uses linguistic rules and fuzzy reasoning to determine the controller parameter transition policy for the dynamic plant subject to large changes in its operating state. Simulation results show the feasibility of using the proposed controller for the control of the dynamical nonlinear CSTR.