Hammerstein模型非线性预测控制的研究

对于pH中和过程的控制,由于其本身具有严重的非线性特性,因而对于此过程的控制十分困难.鉴于此,本文提出了Hammerstein系统非线性预测控制方法,并应用仿真和试验的方法分别实现了对pH中和非线性过程的控制.研究结果表明,Hammerstein模型非线性预测控制具有良好的控制效果和很强的稳定性,且优于常用的PID控制方法.     

具有Hammerstein形式的非线性系统广义预测控制

本文提出了具有Ｈａｍｍｅｒｓｔｅｉｎ形式的非线性系统广义预测控制方法，分析了当控制水平等于１时闭环系统的稳定性，同时还提出了使用线性估计器的非线性自适应广义预测控制算法。仿真结果表明了算法的有效性。     

基于Hammerstei模型的非线性预测控制

本文提出了一种针对 Hammerstein模型的预测控制策略.该策略将Hammerstein模型中的无记忆非线性静态增益环节,改进成易于由中间变量求取控制量的环节,避免了求解高阶方程根的困难,又对线性环节采用线性系统的广义预测控制.由于引入了广义预测控制中多步预测的思想,抗噪声的能力显著提高.仿真结果验证了该策略的有效性.     

采用Hammerstein模型的非线性预测控制

对于象ｐＨ中和，高纯度分离以及化学反等过程的控制，由于其过程本身的严重非线性而变得十分困难。本文提出了一种采用Ｈａｍｍｅｒｓｔｅｉｎ模型的预测控制方法来控制诸如上述的非线性过程，Ｈａｍｍｅｒｓｔｅｉｎ模型用两种方法进行辨识：联立辨识法与序贯识法，特别地，本文提示了一种改进型Ｈａｍｍｅｒｓｔｅｉｎ模型用于克服常规Ｈａｍｍｅｒｓｔｅｉｎ模型在控制器设计时的不足之处，对－ｐＨ中和过程的仿真结果表明，基于     

基于Volterra 模型的非线性系统预测控制

基于系统的正、负和双阶跃响应,提出一种新的非线性预测控制模型的建立方法,同时给出了相应的非线性控制算法,并证明了控制算法解的存在性和唯一性,针对 化工过程蒸馏塔控制系统,通过仿真计算验证了该方法的有效性。     

基于Hammerstei模型的非线性预测控制

本文提出了一种针对Hammerstein模型的预测控制策略。该策略将Hammerstein模型中的无记忆非线性静态增益环节,改进成易于由中间变量求取控制量的环节,避免了求解高阶方程根的困难,又对线性环节采用线性系统的广义预测控制。由于引入了广义预测控制中多步预测的思想,抗噪声的能力显著提高。仿真结果验证了该策略的有效性。     

基于一类特殊非线性模型的广义预测控制

针对Hammerstein模型和Wiener模型,分别选择PH中和滴定过程和控制阀门作为仿真对象,研究了基于两步法的非线性预测控制方法。计算机仿真结果表明,该控制算法比PID控制具有更好的控制品质。     

非线性不对称系统的模型预测控制

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.采用输入输出约束策略保证模型准确,并通过可行性分析确定最终的控制作用. pH 值控制的仿真实验表明,其对不对称非线性系统的控制效果明显优于传统的基于单一线性模型的预测控制及正反方向分别采用 PID 控制的控制效果.     

基于神经网络的非线性系统多步预测控制

针对离散非线性系统,利用非线性激励函数的局部线性表示,提出一种可用于非线性过程的神经网络多步预测控制方法,并给出了控制律的收敛性分析．该方法将非线性系统处理成简单的线性和非线性两部分,对复杂的非线性多步预测方程给出了直观而有效的线性形式,并用线性预测控制方法求得控制律,避免了复杂的非线性优化求解．仿真结果表明了该算法的有效性．     

基于神经网络的非线性系统预测函数控制

针对离散非线性系统,利用神经网络非线性激励函数的局部线性表示,提出一种可用于非线性过程的神经网络预测函数控制方法并给出了控制律的收敛性分析.该方法将复杂的神经网络非线性预测方程转化成直观而有效的线性形式,同时利用线性预测函数方法求得解析的控制律,避免了复杂的非线性优化求解,仿真结果表明了算法的有效性.     

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.     

基于DE算法的非线性预测控制及其应用

针对非线性预测控制系统中需要实时求解非线性规划问题,基于差异演化算法(DE)提出了一种非线性预测控制的新方法。差异演化算法是进化算法的一种,具有全局最优、稳健性强、收敛速度快、及参数调整简单等优点,用于求解非线性预测控制问题中的非线性规划问题具有明显优势。该算法应用于化工过程和化学反应的pH值控制仿真中,仿真结果说明了该方法的有效性。     

基于T-S模糊模型的非线性预测控制策略

提出了一种新的基于T-S模糊模型的非线性预测控制策略. T-S模糊模型用于描述对象的非线性动态特性, 通过将模糊模型的输出反馈回来作为模型输入, 从而构成了模糊多步预报器. 由于T-S模糊模型每条规则的结论部分是一个线性模型, 因此整个模糊模型可以看作一个线性时变系统, 从而将模糊预测控制器中的非线性优化问题转化为一个线性二次寻优问题, 以方便求解. pH中和过程的仿真结果表明其性能优于传统的动态矩阵控制器.     

基于改进DE-NMPC的酸碱中和反应pH值控制

将一种改进的基于差分进化算法的非线性预测控制应用到酸碱中和反应pH值控制系统. 算法充分利用滴定曲线模型, 指导优化过程搜索的初始化空间. 同时, 在变异和选择等操作中改进了差分进化算法, 解决了一类有边界约束的非线性优化问题. 在发酵罐实验装置中进行了测试实验, 取得了较好的效果.     

基于图像特征的锑浮选矿浆pH值预测控制

适宜的矿浆pH值是泡沫浮选高效生产的关键。针对浮选矿浆pH值无法在线检测和控制滞后的问题,提取pH关联泡沫表面敏感特征,建立了基于仿射传播聚类(AP)的多模型最小二乘支持向量机(LSSVM)软测量模型;提出一种基于差分进化(DE)的在线支持向量回归(OSVR)pH值预测控制方法,离线建立和在线校正pH值预测模型,采用DE优化方法求解预测控制决策变量实现pH值实时控制。金锑浮选工业数据表明所提出的控制策略稳定了矿浆pH值,减少了药剂消耗。     

A two-tier control architecture for nonlinear process systems with continuous/asynchronous feedback

In this work, we introduce a two-tier control architecture for nonlinear process systems with both continuous and asynchronous sensing and actuation. This class of systems arises naturally in the context of process control systems based on hybrid communication networks (i.e. point-to-point wired links integrated with networked wired or wireless communication) and utilising multiple heterogeneous measurements (e.g. temperature and concentration). Assuming that there exists a lower-tier control system which relies on point-to-point communication and continuous measurements to stabilise the closed-loop system, we propose to use Lyapunov-based model predictive control to design an upper-tier networked control system to profit from both the continuous and the asynchronous measurements as well as from additional networked control actuators. The proposed two-tier control system architecture preserves the stability properties of the lower-tier controller while improving the closed-loop performance. The theoretical results are demonstrated using two different chemical process examples.     

Adaptive control Lyapunov function based model predictive control for continuous nonlinear systems

A design of adaptive model predictive control (MPC) based on adaptive control Lyapunov function (aCLF) is proposed in this article for nonlinear continuous systems with part of its dynamics being unknown at the starting time. Specifically, to guarantee the convergence of the closed-loop system with online predictive model updating, a stability constraint is designed. It limits the aCLF of the system under the MPC to be less than that under an online updated auxiliary adaptive control. The auxiliary adaptive control which implements in a sampling-hold fashion can guarantee the convergence of the controlled system. The sufficient conditions that guarantee the states to be steered to a small region near the equilibrium by the proposed MPC are provided. The calculation of the proposed algorithm does not depend on the model mismatch at the starting time. And it does not require the Lyapunov function of the state of the real system always to be reduced at each time. These provide the potential to improve the performance of the closed-loop system. The effectiveness of the proposed method is illustrated through a chemical process example.     

Nonlinear predictive control of smooth nonlinear systems based on Volterra models. Application to a pilot plant

There is a large demand to apply nonlinear algorithms to control nonlinear systems. With algorithms considering the process nonlinearities, better control performance is expected in the whole operating range than with linear control algorithms. Three predictive control algorithms based on a Volterra model are considered. The iterative predictive control algorithm to solve the complete nonlinear problem uses the non‐autoregressive Volterra model calculated from the identified autoregressive Volterra model. Two algorithms for a reduced nonlinear optimization problem are considered for the unconstrained case, where an analytic control expression can be given. The performance of the three algorithms is analyzed and compared for reference signal tracking and disturbance rejection. The algorithms are applied and compared in simulation to control a Wiener model, and are used for real‐time control of a chemical pilot plant. Copyright © 2009 John Wiley & Sons, Ltd.