High-Order Volterra Model Predictive Control and Its Application to a Nonlinear Polymerisation Process

Model Predictive Control (MPC) has recently found wide acceptance in the process industry, but existing design and implementation methods are restricted to linear process models. A chemical process, however, involves severe nonlinearity which cannot be ignored in practice. This paper aims to solve this nonlinear control problem by extending MPC to accommodate nonlinear models. It develops an analytical framework for nonlinear model predictive control (NMPC). It also offers a third-order Volterra series based nonparametric nonlinear modelling technique for NMPC design, which relieves practising engineers from the need for deriving a physical-principles based model first. An on-line realisation technique for implementing NMPC is then developed and applied to a Mitsubishi Chemicals polymerisation reaction process. Results show that this nonlinear MPC technique is feasible and very effective. It considerably outperforms linear and low-order Volterra model based methods. The advantages of the developed approach lie not only in control performance superior to existing NMPC methods, but also in eliminating the need for converting an analytical model and then convert it to a Volterra model obtainable only up to the second order. Keywords Model predictive control - Volterra series - process control - nonlinear control Yun Li is a senior lecturer at University of Glasgow, UK, where has taught and researched in evolutionary computation and control engineering since 1991. He worked in the UK National Engineering Laboratory and Industrial Systems and Control Ltd, Glasgow in 1989 and 1990. In 1998, he established the IEEE CACSD Evolutionary Computation Working Group and the European Network of Excellence in Evolutionary Computing (EvoNet) Workgroup on Systems, Control, and Drives. In summer 2002, he served as a visiting professor to Kumamoto University, Japan. He is also a visiting professor at University of Electronic Science and Technology of China. His research interests are in parallel processing, design automation and discovery of engineering systems using evolutionary learning and intelligent search techniques. Applications include control, system modelling and prediction, circuit design, microwave engineering, and operations management. He has advised 12 Ph.D.s in evolutionary computation and has 140 publications.Hiroshi Kashiwagi received B.E, M.E. and Ph.D. degrees in measurement and control engineering from the University of Tokyo, Japan, in 1962, 1964 and 1967 respectively. In 1967 he became an Associate Professor and in 1976 a Professor at Kumamoto University. From 1973 to 1974, he served as a visiting Associate Professor at Purdue University, Indiana, USA. From 1990 to 1994, he was the Director at Computer Center of Kumamoto University. He has also served as a member of Board of Trustees of Society of Instrument and Control Engineers (SICE), Japan, Chairman of Kyushu Branch of SICE and General Chair of many international conferences held in Japan, Korea, Chin and India. In 1994, he was awarded SICE Fellow for his contributions to the field of measurement and control engineering through his various academic activities. He also received the Gold Medal Prize at ICAUTO’95 held in India. In 1997, he received the “Best Book Award” from SICE for his new book entitled “M-sequence and its application” written in Japanese and published in 1996 by Shoukoudou Publishing Co. in Japan. In 1999, he received the “Best Paper Award” from SICE for his paper “M-transform and its application to system identification”. His research interests include signal processing and applications, especially pseudorandom sequence and its applications to measurement and control engineering.

A Real-time Updated Model Predictive Control Strategy for Batch Processes Based on State Estimation

Nonlinear model predictive control(NMPC) is an appealing control technique for improving the performance of batch processes, but its implementation in industry is not always possible due to its heavy on-line computation. To facilitate the implementation of NMPC in batch processes, we propose a real-time updated model predictive control method based on state estimation. The method includes two strategies: a multiple model building strategy and a real-time model updated strategy. The multiple model building strategy is to produce a series of sim-plified models to reduce the on-line computational complexity of NMPC. The real-time model updated strategy is to update the simplified models to keep the accuracy of the models describing dynamic process behavior. The me-thod is validated with a typical batch reactor. Simulation studies show that the new method is efficient and robust with respect to model mismatch and changes in process parameters.

The Application and Design of the Economical Nonlinear Controller

This anticle gives a design method of the economical nonlinear controller. The controller is composed of an expert intelligent coordination controller, a fuzzy prediction controller, a fuzzy feedforward controller, a nonlinear controller and so on. The consistence of a distributed control system based on this controller is also shown briefly.

Wiener model identification and nonlinear model predictive control of a pH neutralization process based on Laguerre filters and least squares support vector machines

This paper deals with Wiener model based predictive control of a pH neutralization process.The dynamic linear block of the Wiener model is parameterized using Laguerre filters while the nonlinear block is constructed using least squares support vector machines (LSSVM).Input-output data from the first principle model of the pH neutralization process are used for the Wiener model identification.Simulation results show that the proposed Wiener model has higher prediction accuracy than Laguerre-support vector regression (SVR) Wiener models,Laguerre-polynomial Wiener models,and linear Laguerre models.The identified Wiener model is used here for nonlinear model predictive control (NMPC) of the pH neutralization process.The set-point tracking performance of the proposed NMPC is compared with those of the Laguerre-SVR Wiener model based NMPC,Laguerre-polynomial Wiener model based NMPC,and linear model predictive control (LMPC).Validation results show that the proposed NMPC outperforms the other three controllers.

Nonlinear Model Algorithmic Control of a pH Neutralization Process

Control of pH neutralization processes is challenging in the chemical process industry because of their inherent strong nonlinearity. In this paper, the model algorithmic control (MAC) strategy is extended to nonlinear processes using Hammerstein model that consists of a static nonlinear polynomial function followed in series by a linear impulse response dynamic element. A new nonlinear Hammerstein MAC algorithm (named NLH-MAC) is presented in detail. The simulation control results of a pH neutralization process show that NLH-MAC gives better control performance than linear MAC and the commonly used industrial nonlinear propotional plus integral plus derivative (PID) controller. Further simulation experiment demonstrates that NLH-MAC not only gives good control response, but also possesses good stability and robustness even with large modeling errors.

An integrated approach for machine-learning-based system identification of dynamical systems under control:application towards the model predictive control of a highly nonlinear reactor system

Advanced model-based control strategies,e.g.,model predictive control,can offer superior control of key process variables for multiple-input multiple-output systems.The quality of the system model is critical to controller performance and should adequately describe the process dynamics across its operating range while remaining amenable to fast optimization.This work articulates an integrated system identification procedure for deriving black-box nonlinear continuous-time multiple-input multiple-output system models for nonlinear model predictive control.To showcase this approach,five candidate models for polynomial and interaction features of both output and manipulated variables were trained on simulated data and integrated into a nonlinear model predictive controller for a highly nonlinear continuous stirred tank reactor system.This procedure successfully identified system models that enabled effective control in both servo and regulator problems across wider operating ranges.These controllers also had reasonable per-iteration times of ca.0.1 s.This demonstration of how such system models could be identified for nonlinear model predictive control without prior knowledge of system dynamics opens further possibilities for direct data-driven methodologies for model-based control which,in the face of process uncertainties or modelling limitations,allow rapid and stable control over wider operating ranges.

A Compensation Controller Based on a Nonlinear Wavelet Neural Network for Continuous Material Processing Operations

Continuous material processing operations like printing and textiles manufacturing are conducted under highly variable conditions due to changes in the environment and/or in the materials being processed.As such,the processing parameters require robust real-time adjustment appropriate to the conditions of a nonlinear system.This paper addresses this issue by presenting a hybrid feedforward-feedback nonlinear model predictive controller for continuous material processing operations.The adaptive feedback control strategy of the controller augments the standard feedforward control to ensure improved robustness and compensation for environmental disturbances and/or parameter uncertainties.Thus,the controller can reduce the need for manual adjustments.The controller applies nonlinear generalized predictive control to generate an adaptive control signal for attaining robust performance.A wavelet-based neural network model is adopted as the prediction model with high prediction precision and time-frequency localization characteristics.Online training is utilized to predict uncertain system dynamics by tuning the wavelet neural network parameters and the controller parameters adaptively.The performance of the controller algorithm is verified by both simulation,and in a real-time practical application involving a single-input single-output double-zone sliver drafting system used in textiles manufacturing.Both the simulation and practical results demonstrate an excellent control performance in terms of the mean thickness and coefficient of variation of output slivers,which verifies the effectiveness of this approach in improving the long-term uniformity of slivers.

A systematic review of current and emergent manipulator control approaches

Pressing demands of productivity and accuracy in today＇s robotic applications have highlighted an urge to replace classical control strategies with their modem control counterparts. This recent trend is further justified by the fact that the robotic manipulators have complex nonlinear dynamic structure with uncertain parameters. Highlighting the authors＇ research achievements in the domain of manipulator design and control, this paper presents a systematic and comprehensive review of the state-of-the-art control techniques that find enormous potential in controlling manipulators to execute cutting- edge applications. In particular, three kinds of strategies, i.e., intelligent proportional-integral-derivative （PID） scheme, robust control and adaptation based approaches, are reviewed. Future trend in the subject area is commented. Open-source simulators to facilitate controller design are also tabulated. With a comprehensive list of references, it is anticipated that the review will act as a firsthand reference for researchers, engineers and industrialinterns to realize the control laws for multi-degree of freedom （DOF） manipulators.

机器学习视角下风味分子研究及其在茉莉花茶中的应用

旨在探讨机器学习在风味分子研究领域的应用,尤其是其在茉莉花茶风味分析中的实践。风味分子的研究是理解和优化食品、特别是茶类饮品味道和品质的基础。机器学习技术的引入为风味分子的识别和分析打开了新的视野。概述了风味分子的基本概念和研究方法,详细讨论了机器学习在解析分子结构与风味特性关系、茉莉花茶品质预测与控制、风味分析、预测与优化、智能化加工等方面的应用,并提出了研究展望,以期为提升茉莉花茶的品质和茶产业发展提供技术支持。

先进控制技术(APC)在辛醇装置氢碳比控制上的应用

辛醇装置以合成气(CO+H_(2))、丙烯为原料,合成气中氢碳比不稳定,变化频繁,根据原料中氢碳比(H_(2)/CO比)变化及时准确补充氢气,是羰基合成反应平稳控制的关键。代表性的国产先进控制(APC)技术有石化盈科PROCET-APC、中控APC Suite。以齐鲁石化第二化肥厂辛醇装置为工业应用背景,为实现辛醇氢碳比自动控制,采用石化盈科PROCET-APC先进控制软件设计一个氢碳比控制器:(1)氢碳比控制器投用后表现出良好的鲁棒性能,实现了高压氢气自动控制,投用率一直保持在95%以上,减轻了操作人员劳动强度。(2)辛醇氢碳比和反应器R101一氧化碳含量平稳性明显提高,波动方差分别降低-76.43%和-57.27%。(3)辛醇氢碳比的平稳控制,提高了丙烯效率,降低了羰基合成反应中丙烯的单耗。实施结果表明先进控制在辛醇装置氢碳比上的应用取得了显著的效果。

镍浮选过程智能控制系统开发与应用

针对国内某选矿厂镍浮选工艺来矿性质不稳定、精矿品位波动大、回收率不理想的特点,结合浮选生产现场检测设备不完备或者检测周期长、费用高的现状,设计了一套基于品位预测模型的浮选过程智能控制系统,系统投入运行后,泡沫流速稳定性显著提高,精矿品位波动性明显减小,证明了系统的实用性。

中药炮制传承创新与饮片产业高质量发展现状及展望

中药饮片是在中医药理论指导下,根据中医临床辨证论治及制剂的需要,对中药材进行加工炮制后制成的药品。中药炮制是我国医药领域最具自主知识产权的制药技术,中药饮片的“生熟异治”是中药有别于天然药物的重要标志,中药饮片产业高质量发展是促进中医药传承创新的关键要素。当前国家相关科技政策及法规的发布,逐渐引导中药产业朝着传承创新与智能制造方向的转型发展。但中药饮片产业仍面临诸多挑战,例如中药炮制传承创新发展不足,饮片企业“小而全”的生产模式及较为分散的竞争格局,导致产业生产过程的规范化、标准化、智能化发展不足;饮片的质量控制缺乏整体性及特征性的质量标准,等级划分标准的科学性仍有待商榷等。针对上述问题,本文提出通过挖掘中药特色炮制技术及品种,探究传统中药炮制理论科学内涵,促进中药炮制的传承创新;加强饮片产地加工过程控制,提升生产过程质量控制水平,推进中药饮片行业集约化、规模化、智能化发展;解析饮片质量评价关键识别技术,积极探索饮片质量等级评价标准,构建体现中药饮片特色的质量评价体系;利用现代物联网技术建立饮片溯源体系。通过以中药饮片为主体,以促进中药传承创新发展为导向,实现饮片产业高质量发展。

工业光总线控制系统在流程工业中的应用研究

传统分散控制系统信息传输过程中出现的中间断点,会影响线路阻值和信号稳定性。提出了一种工业光总线控制系统,介绍了该控制系统中智能数据传输单元及工程设计中的注意事项,并对比了该系统与分散控制系统在建设过程中费用控制、进度控制、质量控制方面的优势,分析了利用该系统代替其他控制系统的可行性。该系统采用现场智能IO技术,用光信号代替电信号,通过分析表明:该系统在经济性、信号稳定性、施工、设计、进度控制上都具有优势。

工业过程指标的平滑交替辨识预报算法

针对复杂工业过程的指标预报问题,本文提出一种基于数据的非线性系统平滑交替辨识算法.交替辨识算法将系统的输入输出模型在工作点附近展开为线性模型和高阶非线性模型,然后交替更新线性模型参数和非线性模型参数,其中对于线性模型采用最小二乘辨识方法,对于高阶非线性模型采用长短期记忆网络进行建模.所提方法的创新之处在于,对于实际系统中的噪声易导致线性部分辨识参数震荡的问题,引入平滑因子来抑制震荡,提高预测模型的稳定性能;在非线性部分则引入压缩因子来调节在辨识过程中非线性部分的权重,总体上提高了预报的准确性.通过数值仿真验证了所提算法的性能,并与其他方法进行了对比实验,结果表明所提算法能够有效抑制辨识过程中的参数震荡,并且取得更好的辨识精度.

基于储层预测数据分布式处理的质控平台研发

随着我国企业信息化建设的不断发展与石油勘探工作的持续深入,石油石化企业在生产管理智能化和勘测场景多样化等方面面临着更为严峻的挑战。为应对这些问题,塔里木油田企业将储层预测工作划分成岩石物理、正演模拟、特殊处理及属性分析、叠后反演、叠前反演5个工序,通过工作流程的分布式优化推动全局最优,在降低解释工作复杂度的同时,缩短项目的处理周期。在此基础上,针对各质控点制定详细的处理工艺和精度要求,结合GeoEast-iEco数据解释系统,研发一套智能质控平台,实现储层预测的高效率和高质量质控。

基于工业物联网的造纸企业全流程废水智能控制系统研究

造纸企业废水的智能化控制始终是造纸产业绿色转型升级工作中的重点。以工业互联网等先进网络技术为基础构建了一种针对造纸企业生产线的全流程废水智能控制系统。该系统由管理模块、水质数据收集模块、水质数据分析模块等组成,通过工业物联网等技术实现了造纸企业全流程废水智能检测、预警、控制等功能的搭建。对该系统的工作情况进行测试。结果表明:该系统可以精准地采集造纸企业全流程形成的废水信息并对废水的处理情况进行智能控制。

基于MPC与非线性控制算法的智能移动平台轨迹跟踪控制对比研究

随着经济和科技的持续进步,智能移动平台已被广泛应用于工业、农业和军事等领域。常见的轨迹跟踪控制算法包括模型预测控制算法和基于模型的非线性控制算法两种,两者各有优势,均取得了较好的控制效果。以具有阿克曼转向模型的智能移动平台为研究对象,分别设计了模型预测控制器及非线性控制器,并从圆形轨迹跟踪和移动避障效果两个方面进行仿真对比,给出两种常见算法的适用条件,以期对后续的相关工作提供思路和指导。

基于智能控制的工业过程控制自动化研究

随着工业4.0概念的普及,智能控制在自动化工业过程中的应用变得尤为关键,改善着生产流程,极大地促进了产品质量的提升及生产成本的降低。探究基于智能控制的工业过程控制自动化及其在工业实际应用中的效果,对理解工业过程控制自动化对现代制造业的影响及推动产业技术创新发展具有重要意义。

LIMS在钢铁实验室智能化系统中的应用

在钢铁行业中,质量控制是确保产品性能和市场竞争力的关键。实验室信息管理系统(LIMS)作为一种先进的实验室管理工具,对于提高样品管理、流程控制和数据分析的效率具有重要作用。针对钢铁行业的特定需求,探讨了LIMS在智能化实验室的开发与应用,分析了其在样品流程控制和仪器数据采集后处理与分析中的实际效果,并提出了未来的发展方向。

复杂工业过程运行的混合智能优化控制方法

工业过程运行的优化控制的目标是将反应产品在加工过程中的质量、效率、消耗的工艺指标控制在目标值范围内.由于复杂工业过程的工艺指标难于在线测量且与控制回路输出之间的动态特性具有强非线性、强耦合、难以用精确模型描述、随生产边界条件变化而变化的综合复杂性,因此,难以采用已有优化控制方法,运行控制只能采用人工设定的控制方式.由于人工控制不能及时准确地随运行工况调整设定值,难以将工艺指标控制在目标值范围内,甚至造成故障工况.本文提出了根据运行工况实时调整控制回路设定值,通过控制系统跟踪调整后的设定值,将工艺指标控制在目标值范围内的过程优化运行的混合智能控制方法.该方法由控制回路预设定模型、前馈补偿与反馈补偿器、工艺指标预报模型、故障工况诊断和容错控制器组成.在某选矿厂22台竖炉组成的焙烧过程的应用案例,证明了所提出方法的有效性.