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.

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.

Active Fault Tolerant Control of a Class of Nonlinear Time-Delay Processes

Based on a nonlinear state predictor (NSP) and a strong tracking filter (STF), a sensor fault tolerant generic model control (FTGMC) approach for a class of nonlinear time-delay processes is proposed. First, the NSP is introduced, and it is used to extend the conventional generic model control (GMC) to nonlinear processes with large input time-delay. Then the STF is adopted to estimate process states and sensor biaz, the estimated sensor bias is used to drive a fault detection logic. When a sensor fault is detected, the estimated process states by the STF will be used to construct the process output to form a 'soft sensor', which is then used by the NSP (instead of the real outputs) to provide state predictors. These procedures constitute an active fault tolerant control scheme. Finally, simulation results of a three-tank-system demonstrate the effectiveness of the proposed approach.

High-Accuracy Pneumatic Position Control by Applying Nonlinear Control and Arranging Transient Process

By applying a nonlinear control and arranging a transient process, the initiative error of the pneumatic servo positioning system is reduced largely, and a larger gain of the controller is used to improve the responding speed of the system at the same damping ratio. Therefore, a compromise is made among the responding speed, overshoot, robustness, adaptability and stability. In addition, a dynamic output feedback controller, including position velocity and acceleration （PVA） feedback, is designed to improve the performance of the system. And a nonlinear controller is reconstructed based on the linear output feedback controller to decrease noises and disturbances. The dynamic responses of the system are simulated and tested. Results show that the error is kept within 0.02 mm under different mass loads and the positioning transient process is smooth, without overshoot and speedy.

Recent Advances in Iterative Learning Control

In this paper we review the recent advances in three sub-areas of iterative learning control (ILC): 1) linear ILC for linear processes, 2) linear ILC for nonlinear processes which are global Lipschitz continuous (GLC), and 3) nonlinear ILC for general nonlinear processes. For linear processes, we focus on several basic configurations of linear ILC. For nonlinear processes with linear ILC, we concentrate on the design and transient analysis which were overlooked and missing for a long period. For general classes of nonlinear processes, we demonstrate nonlinear ILC methods based on Lyapunov theory, which is evolving into a new control paradigm.

Gender differences in nonlinear motor performance following concussion

Purpose:To quantify differences in nonlinear aspects of performance on a seated visual-motor tracking task between clinically asymptomatic males and females with and without a self-reported mild traumatic brain injury history.Methods:Seventy-three individuals with a self-reported concussion history(age:21.40±2.25 years,mean±SD)and 75 without completed the visual-motor tracking task(age:21.50±2.00 years).Participants pressed an index finger against a force sensor,tracing a line across a computer screen(visual-motor tracking).The produced signal's root-mean-square error(RMSE),sample entropy(SampEn,a measure of regularity),and average power(AvP)between 0 and 12 Hz were calculated.Results:Males with a history of 0 or 1 concussion had greater RMSE(worse performance)than females with 0(p<0.0001)and 1 concussion(p=0.052).Additionally,females with 2+concussions exhibited lower SampEn than females with no history(p=0.001)or a history of 1 concussion(p=0.026).Finally,females with 2+concussions had lower 8-12 Hz AvP than males with 2+concussions(p=0.031).Few differences were observed in the male participants.Conclusion:Females with a self-reported history of multiple concussions exhibited lower SampEn in the visual-motor tracking-task force output structure as compared to those with no reported history of concussion and their male counterparts.Lower SampEn and lower power between 8 and12 Hz indicated persistent impairment in visual processing and feed-forward or predictive motor control systems.

基于神经网络相对增益阵的非线性系统常规控制回路配对

化工过程一般为非线性系统,对其进行常规控制系统结构设计一般采用基于线性化模型的相对增益阵方法,但是当非线性特性对控制性能影响较大时,应采用基于非线性模型的相对增益阵方法。笔者考虑非线性静态环节与线性动态环节连接的Hammerstein模型,采用能量增益将线性动态环节转换为静态增益阵,与非线性静态环节串联为多变量非线性静态增益阵,利用神经网络分别对组合后的非线性静态增益阵的正向和逆向的映射关系进行拟合,以训练好的正/逆神经网络求取非线性相对增益阵进行常规控制回路配对。最后通过线性系统的实例分析验证了所提方法的正确性,并对酸碱中和反应和精馏塔进行了常规控制回路配对。

五轴联动数控刀具切削加工误差非线性插补方法研究

在五轴联动数控刀具切削加工过程中,机床摆心运动轨迹和刀具运动轨迹之间的误差导致加工精度难以保持较高水平,为此提出五轴联动数控刀具切削加工误差非线性插补方法。分析刀具切削加工误差产生的原因,获得理想刀轴矢量,通过插值处理两旋转轴的运动角度,获得实际刀轴矢量误差。通过计算刀轴控制点的距离偏移值,调整刀位和刀闸位置,并使用线性补偿位移调整刀具的位移。同时,根据角度变化计算误差插补量,进一步优化刀具运行轨迹,以确保刀具轨迹始终与理想轨迹保持一致,从而实现五轴联动数控刀具运行轨迹的位移补偿。经实验验证,采用所提方法开展误差插补后,五轴联动数控刀具运行误差小,且运行轨迹贴近理想轨迹。

Markov切换拓扑下二阶非线性多智能体编队容错控制

多智能体系统编队控制过程中存在固定拓扑下智能体之间通信信号易丢失和控制器故障频发的问题。对此,提出一种具有随机切换通信拓扑的二阶非线性多智能体系统领导-跟随编队容错控制协议。为解决二阶非线性多智能体系统编队中控制信号丢失的问题,利用马尔可夫(Markov)随机过程描述切换通信拓扑编队模型,提出一种基于Markov随机切换通信拓扑的多智能体编队控制律,通过建立领导者和追随者之间的状态误差模型,构建具有二重积分的Lyapunov-Krasovskii函数证明多智能体系统在均方意义下的指数收敛稳定。为解决多智能体系统编队控制器故障问题,建立具有随机通信拓扑切换的多智能体编队容错控制模型,设计一种具有Markov随机切换通信拓扑的多智能体编队容错控制算法,利用李雅普诺夫稳定性理论分析所提出算法的稳定性和收敛性。仿真结果体现了多智能体系统随机切换通信拓扑编队控制器超调量小、响应时间快、调整时间短的特点,同时对比实验结果验证了系统在发生故障和无故障情况下的稳定性与有效性。

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.

非线性Hammerstein模型预测控制策略及其在pH中和过程中的应用

所有实际工业过程都包含一定程度的非线性,如pH中和过程由于其本身的强非线性是工业过程控制中具有挑战性的难题,但至今为止仍缺乏有效的非线性控制方法。将基于差分方程模型的模型预测控制策略(model predictive control,MPC)推广到包含一个静态非线性多项式函数和一个线性差分方程动态环节的非线性Hammerstein系统,详细描述了基于静态非线性多项式函数的最优控制作用求解方法,提出了一套新的非线性Hammerstein MPC控制策略(nonlinear Hammerstein predictive control,NLHPC)。pH中和过程控制仿真和控制实验表明,NLHPC的控制结果好于工业上常用的非线性PID(nonlinear PID,NL-PID)控制器。

核学习自适应预测控制器的在线更新方法

针对非线性过程控制器的设计问题,将基于稀疏核学习的一种具有解析形式的自适应预测控制算法与选择性递推核学习相结合.该在线核学习模型可以通过递推算法进行节点增长和删减的有效更新.因此,所提出的控制器复杂度可控,且能学习过程的时变等特性,从而获得更好的性能.通过一非线性时变过程的仿真研究,验证了所提出的核学习控制器较传统的PID和无在线更新的核学习控制器等具有更好的自适应能力和鲁棒性.

一般模型控制(GMC):一种简单实用的非线性过程控制新方法

一般模型控制（ＧｅｎｅｒｉｃＭｏｄｅｌＣｏｎｔｒｏｌ－ＧＭＣ）是１９８８年以来发展起来的一种非线性过程控制方法，此方法具有简单实用，鲁棒性强的特点，而其控制效果却与微分几合方法相类似，因此在过去的九年里此方法已得到广泛的应用．本文综述了此方法的发展状况，并指出了尚待解决的若干问题．

预测控制的研究现状与展望

近年来,预测控制在工业过程中不仅得到广泛的应用,而且其理论研究也取得了很大进展。对当前各种预测控制方法的现状及其工业应用进行了较深入的分析,并对其存在的问题和今后可能的发展趋势作了进一步探讨。

耗散结构理论及其在火成岩岩石学中的意义

本文简要介绍了耗散结构理论及其研究方法,以及它在火成岩中的应用,其中包括以下几个方面:(1)基性杂岩体的层状构造;)2)花岗岩中的球状构造;(3)斜长石的环带;(4)花岗岩体的成分分带。并讨论了这四种耗散结构的形成机理。这些耗散结构都是在远离平衡的条件下,通过内部不可逆的非线性过程,由于外界的扰动和内部的涨落造成定态的失稳而形成的。其形成的根本原因是其内部反馈机制作用的结果。斜长石的环带是时间耗散结构——极限环。利用耗散结构理论简要地解释了辽宁二道沟花岗闪长岩的成因。

模糊非线性内模控制算法及其在pH值控制中的应用

pH值控制过程具有较强的非线性,历来是过程控制研究的一大热点,本文针对pH值控制系统提出了一种基于模糊推理网的非线性内模控制算法（FNIMC）。模糊推理网用于辨识对象的模糊模型;FNIMC由一个逆模控制器和具有一个可调参数的鲁棒滤波器组成。仿真结果表明该算法优于非线性PID调节器,且计算效率高。

一类新型超混沌系统的非线性反馈同步研究

一类由快变和慢变吸引子构成的新型超混沌系统,具有强的抗噪声能力,但是采用传统同步算法时,同步收敛速度较慢,同步性能对响应系统参数敏感。针对该问题,该文提出非线性反馈同步算法,根据Hurwitz稳定原理,设计非线性控制变量,使得误差方程雅可比矩阵的特征值实部均小于零,并使得特征值的绝对值较大。该算法比传统同步算法收敛速度快,并且具有对系统参数不敏感的优点。仿真结果验证了上述算法的有效性。

基于神经网络的非线性预测控制综述

基于神经网络的非线性预测控制是智能控制中的重要前沿课题 ,在工业过程控制领域有着非常大的应用前景。从预测方式、控制律求解方法和典型应用等几个角度对基于神经网络的非线性预测控制做了综述。对其中的关键技术做了深入阐述。

基于微分几何的水电站过渡过程非线性控制

通过建立基于刚性水击的水电站小波动及水力干扰过渡过程非线性数学模型,从非线性微分几何控制理论出发,提出了构造调节输出函数的设计原则,使得非线性非最小相位系统能完全精确线性化,从而避免了判别零动态稳定性问题。运用该设计原则,推导出水电站小波动及水力干扰过渡过程非线性控制律,同时进行了数值仿真试验。结果表明:本文所提出的非线性控制律计算结果合理,且明显优于常规控制律。