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。     

非线性时变系统的小波预测控制

从小波分析原理出发,给出了一种正交小波网络辨识非线性动态系统的方法,并以此网络作为预测模型,同时通过递推最小二乘法在线修正模型;采用遗传算法优化控制器输出,通过对遗传算子和优化指标的修改,减少了在线寻优计算量.仿真实验表明该算法对非线性时变系统有良好的控制效果.     

基于最小二乘支持向量机的非线性补偿器

针对工业过程中普遍存在的非线性被控对象,通过最小二乘支持向量机对系统的模型偏差建模,并在此基础上构造非线性补偿器.首先,采用具有RBF核函数的LS-SVM离线建立系统偏差模型,并在系统运行时不断对偏差模型进行在线修正;然后基于此模型在DMC预测控制的基础之上构建补偿器;最后成功应用于智能工厂实验室的多变量液位控制实验装置.     

基于快速正交搜索算法的非线性预测控制

针对一些非线性系统的模型辨识和预测控制计算繁琐的问题,提出基于快速正交搜索算法的预测控制方法,可准确地对非线性系统模型进行辨识。该方法优化了计算方法,省去了繁琐的迭代过程,并减少了每次迭代过程中引入的误差。利用辨识模型建立预测模型,对非线性系统进行直接预测控制。仿真实验结果证实了该方法的有效性和可行性。     

一类非线性不确定时滞系统鲁棒预测控制

针对一类非线性不确定时滞系统,结合Lyapunov稳定性定理和H∞理论,得到系统渐进稳定和状态反馈H∞控制器存在的充分条件,并且给出了此类非线性不确定时滞系统的鲁棒H∞状态反馈控制律设计方案.最后通过具体数值仿真说明了设计方案的有效性.     

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。     

内部重整固体氧化物燃料电池发电系统的非线性预测控制

固体氧化物燃料电池（SOFC）发电系统运行除了电堆本体外还需要包含诸多其他辅助组件以期获得系统输出的最大效率,为了使SOFC电堆能够对纯氢以外的燃料具有更好的适用性,加入了燃料内部重整装置和燃烧室两个重要辅助组件。文中在对系统展开建模的基础上提出了采用非线性模型预测控制策略,能够更有效地使输出燃料气体的组分、温度、压力、浓度和流率满足燃料电池堆正常运行的需要,通过仿真分别论证了线性模型预测控制和非线性模型预测控制两种不同控制方案的有效性和适用性。     

基于控制性能比较的非线性不对称系统预测控制

生产过程某些非线性系统常常表现出不对称动态特性,相对于其在工业工程中经常出现的理论研究特别是控制方法研究则十分有限。本文针对基于正反方向上的两个线性模型分别设计PID控制器的缺陷,提出根据正反方向上的线性模型分别设计相应的状态反馈预测控制器。在每一步的控制率计算中,正反方向的控制器分别计算控制作用,并通过比较正反控制器的控制性能指标来确定最终采用的控制作用。通过pH值控制的仿真实验证明其对非线性不对称系统的控制效果明显优于传统的在正反方向分别采用PID控制的控制效果。     

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。     

RBF网络模型在预测函数控制中的应用

对三容液位系统的非线性复杂特点,利用RBF网络对系统建立预测模型,着重分析了RBF网络结构的选取、模型参数辨识以及网络优化的问题.通过预测函数控制验证了RBF网络模型在非线性系统建模中的优越性.     

基于多核支持向量机的非线性模型预测控制

Multi-kernel-based support vector machine （SVM） model structure of nonlinear systems and its specific identification method is proposed, which is composed of a SVM with linear kernel function followed in series by a SVM with spline kernel function. With the help of this model, nonlinear model predictive control can be transformed to linear model predictive control, and consequently a unified analytical solution of optimal input of multi-step-ahead predictive control is possible to derive. This algorithm does not require online iterative optimization in order to be suitable for real-time control with less calculation. The simulation results of pH neutralization process and CSTR reactor show the effectiveness and advantages of the presented algorithm.     

A Composite Model Predictive Control Strategy for Furnaces

Tube furnaces are essential and primary energy intensive facilities in petrochemical plants. Operational optimization of furnaces could not only help to improve product quality but also benefit to reduce energy consumption and exhaust emission. Inspired by this idea, this paper presents a composite model predictive control (CMPC) strategy, which, taking advantage of distributed model predictive control architectures, combines tracking nonlinear model predictive control and economic nonlinear model predictive control metrics to keep process running smoothly and optimize operational conditions. The controllers connected with two kinds of communication networks are easy to organize and maintain, and stable to process interferences. A fast solution algorithm combining interior point solvers and Newton's method is accommodated to the CMPC realization, with reasonable CPU computing time and suitable online applications. Simulation for industrial case demonstrates that the proposed approach can ensure stable operations of furnaces, improve heat efficiency, and reduce the emission effectively.     

Nonlinear model predictive control for dividing wall columns

Dividing wall columns (DWCs) are practical, effective, and promising among distillation process intensification technologies. Nonlinear model predictive control (NMPC) schemes are developed in this study to control the three-product DWCs. As these systems are intensely interactive and highly nonlinear, NMPC may be more suitable than the traditional PI control. The model is established based on Python and Pyomo platforms. As the original mathematical model of the column section is ill-posed, index reduction is used to avoid a high-index differential-algebraic equation (DAE) system. The well-posed index-1 system after index reduction is employed for the steady-state simulation and dynamic control in this study. Case studies with three DWC configurations to separate the mixture of ethanol (A), n-propanol (B), and n-butanol (C) show that the NMPC performs very well with small maximum deviations and short settling times. This demonstrates that the NMPC is a feasible and very effective scheme to control three-product DWCs.     

Machine-learning-based construction of barrier functions and models for safe model predictive control

In this paper, we propose a control Lyapunov-barrier function-based model predictive control method utilizing a feed-forward neural network specified control barrier function (CBF) and a recurrent neural network (RNN) predictive model to stabilize nonlinear processes with input constraints, and to guarantee that safety requirements are met for all times. The nonlinear system is first modeled using RNN techniques, and a CBF is characterized by constructing a feed-forward neural network (FNN) model with unique structures and properties. The FNN model for the CBF is trained based on data samples collected from safe and unsafe operating regions, and the resulting FNN model is verified to demonstrate that the safety properties of the CBF are satisfied. Given sufficiently small bounded modeling errors for both the FNN and the RNN models, the proposed control system is able to guarantee closed-loop stability while preventing the closed-loop states from entering unsafe regions in state-space under sample-and-hold control action implementation. We provide the theoretical analysis for bounded unsafe sets in state-space, and demonstrate the effectiveness of the proposed control strategy using a nonlinear chemical process example with a bounded unsafe region.     

基于2次核SVM的单步非线性模型预测控制

A support vector machine (SVM) with quadratic polynomial kernel function based nonlinear model one-step-ahead predictive controller is presented. The SVM based predictive model is established with black-box identification method. By solving a cubic equation in the feature space, an explicit predictive control law is obtained through the predictive control mechanism. The effect of controller is demonstrated on a recognized benchmark problem and on the control of continuous-stirred tank reactor (CSTR). Simulation results show that SVM with quadratic polynomial kernel function based predictive controller can be well applied to nonlinear systems, with good performance in following reference trajectory as well as in disturbance-rejection.     

Observer based nonlinear quadratic dynamic matrix control for state space and input/output models

An observer based nonlinear Quadratic Dynamic Matrix Control (QDMC) algorithm is developed for use with nonlinear input-output (I/O) and state space models. It generalizes and extends previously published nonlinear QDMC algorithms. The extension to I/O models is particularly important due to the increased use of neural networks and other types of nonlinear black box models in the chemical industry. Disturbance rejection and offset free tracking is addressed in a general setting utilizing concepts from filtering theory. Various kinds of disturbance models can be incorporated in the formulation. Even though nonlinear models are utilized for model prediction, the on-line optimization is formulated as a single Quadratic Program, thus preserving the computational advantages of nonlinear QDMC as compared to Model Predictive Control algorithms based on nonlinear programming techniques. The examples illustrate parameter tuning for open-loop unstable and stable processes and point out both benefits and shortcomings of the algorithm.     

基于T-S模糊模型与粒子群优化的非线性预测控制

引言模型预测控制属于一种基于模型的多变量的控制算法,发展至今已在化工过程控制方面得到了广泛的应用[1-5]。状态反馈预测控制[6-8]是模型预测控制技术的一种,基于状态空间模型,采用实测状态