化工过程强非线性系统的变模型自适应预测控制

提出一种变模型自适应预测控制算法 ;基于非线性状态空间模型 ,通过每步在当前工作点 (非平衡点 )线性化获得线性化子模型 ,以此进行状态反馈预测控制 ,线性化子模型随工作点变化 ,且不限于平衡点。通过pH值控制的对比仿真实验 ,证明其对强非线性过程的控制效果优于传统的多模型预测控制。最后分析讨论了该控制算法存在的几个重要问题 ,并指出与之相关的未来研究方向     

基于改进回声状态网络的游离氧化钙预测控制

在非线性时延水泥烧成系统中,针对传统预测控制方法调节时间长、控制精度不高的问题,提出一种改进的在线型回声状态网络预测控制模型。首先将带有L1范数约束项的递归最小二乘法与回声状态网络相结合构建在线型预测模型,解决传统预测控制模型辨识精度较低、无法进行实时预测的问题;然后基于改进的回声状态网络预测模型,构建预测控制模型结构,并采用具有全局优化能力的粒子群算法进行滚动优化,保证实际输出量快速、准确、平稳地跟随被控量的设定值;最后利用改进的预测控制模型对水泥烧成系统中的游离氧化钙含量进行预测控制仿真实验,结果表明改进的预测控制模型具有良好的性能和应用前景。     

基于改进回声状态网络的游离氧化钙预测控制

在非线性时延水泥烧成系统中,针对传统预测控制方法调节时间长、控制精度不高的问题,提出一种改进的在线型回声状态网络预测控制模型。首先将带有L1范数约束项的递归最小二乘法与回声状态网络相结合构建在线型预测模型,解决传统预测控制模型辨识精度较低、无法进行实时预测的问题;然后基于改进的回声状态网络预测模型,构建预测控制模型结构,并采用具有全局优化能力的粒子群算法进行滚动优化,保证实际输出量快速、准确、平稳地跟随被控量的设定值;最后利用改进的预测控制模型对水泥烧成系统中的游离氧化钙含量进行预测控制仿真实验,结果表明改进的预测控制模型具有良好的性能和应用前景。     

普遍化的基于LJ流体的自扩散系数预测模型

引　言扩散系数由于在实验测定上存在一定的困难 ,其预测就显得非常重要 .近年来 ,随着计算机技术的发展 ,分子动力学 (ＭＤ)模拟成为一种新的有效的扩散系数预测工具[1] .目前在预测有机物扩散系数的ＭＤ研究中主要采用的势能模型为Ｈａｒｄ -Ｓｐｈｅｒｅ(ＨＳ)模型和Ｌｅｎｎａｒｄ -Ｊｏｎｅｓ(ＬＪ)模型 .然而 ,由于ＭＤ模拟需要较长的计算时间 ,从工程应用的角度出发 ,半经验的扩散系数预测模型还是显得比较方便 .对于自扩散系数的研究 ,以前主要有刘洪勤等[2 ] 、于养信等[3] 采用ＬＪ模型进行了探讨 .然而 ,他们都是对每一种物质的…     

基于LPV模型的燃料电池空气进气系统控制

质子交换膜燃料电池是一种通过氢气和氧气的电化学反应将化学能直接转化为电能的装置。提出一种改进的四阶燃料电池进气系统模型,分析了系统的约束性。针对系统模型所具有的非线性特性,提出建立线性变参数（LPV）模型用于对系统的控制。针对状态变量不可测的问题引入卡尔曼滤波器,同时通过可观性分析得出系统所需测量的最佳变量。在符合约束条件下设计基于线性变参数模型的状态空间模型预测控制器,控制空压机的工作电压保证氢气燃料的充分反应。仿真结果表明,基于LPV模型的模型预测控制器能够对空气进气系统进行有效的控制,且满足空压机喘振和阻塞边界等约束条件,与单模型预测控制相比具有更好的控制效果。     

基于模型预测控制及智能寻优的水泥粉磨优化控制

本文提出一种基于运行状态软测量和成本软约束的多变量模型预测控制(MPC)。MPC控制与传统的专家经验控制和模糊控制相比,通过模型对系统工艺参数的预测,不断地学习更新模型,更符合水泥粉磨大时延、多工况的特性。应用中通过对水泥粉磨装置的阶跃响应实验,建立多变量预测控制模型,解决水泥粉磨系统生产过程的不确定性。在此基础上,通过增量学习和机器学习找到最优运行参数,使水泥粉磨的MPC控制一直保持在最优工况。     

快速增量约束预测控制及在GLCC液位控制中的应用

针对气-液柱状旋流式(GLCC)多相流量计的液位控制问题,提出一种增量多变量模型预测控制(MPC)算法。采用控制增量状态空间模型和阶梯式控制策略,建立约束多变量MPC优化控制问题。为在线计算约束优化问题,引入坐标轮换法和黄金分割法,在线计算控制变量增量值,进而得到预测控制量。最后,以GLCC多相流量计的两输入单输出液位控制模型为例,仿真验证本文算法的有效性。     

涂层老化数值模拟与数据处理的研究进展

[目的]涂层老化的数值模拟研究取得了丰硕的成果。[方法]综述了研究涂层老化失效机理、寿命预测、在线监测的模拟分析与数据处理方法。[结果]对于涂层老化失效机理,可以就光氧老化、热氧老化、机械应力老化等微观或宏观特征进行模拟。对于涂层寿命预测,可以建立基于老化特征的模型或基于不确定性数学的模型。对于涂层老化的在线监测,电化学监测和老化特征监测是两个主要途径,所得数据可用于腐蚀数据库的建立及基于大数据的模型开发。[结论]引入人工智能是涂层老化数值模拟与数据处理的发展趋势。     

基于状态校正的积分对象预测控制算法及其应用

针对具有大滞后、强动态干扰的积分特性对象,提出了基于一阶状态校正的预测控制算法。算法考虑预测状态与其对应的实际测量值偏差的积分作用,在一阶维度上施加状态校正,从而大幅提高控制器的预测准确性与应对模型失配的能力。通过仿真对比验证了状态校正算法的有效性。本算法已实际应用于某炼厂闪蒸罐的液位控制,取得了较好的控制效果。     

基于神经网络的pH中和过程非线性预测控制

针对pH中和过程这一化工过程系统中的典型非线性对象特点,应用神经网络建模思想和模型预测控制方法,并结合Hammerstein模型特点,研究pH中和过程非线性系统的两种新型模型预测控制手段,分别建立基于神经网络的非线性预测控制系统整体求解策略和基于Hammerstein模型的两步法预测控制策略,并用MATLAB对其进行仿真。控制仿真结果表明,建立的神经网络预测控制策略和非线性Hammerstein模型预测控制均优于传统PID控制方法,具有良好的设定值跟踪效果和抗干扰控制响应,说明这两种控制策略是非线性过程的有效控制方法。     

基于FGPC的单元机组协调控制系统研究

针对单元机组的大迟延、强耦合、参数时变且不确定性的特点,将T—s模糊模型引入预测控制中,作为预测模型。首先,用改进的模糊c一均值聚类算法和随机牛顿法辨识得到非线性系统的T-s模型;然后基于线性化后的系统模型设计模糊广义预测控制器,并对非线性对象进行在线控制。仿真结果表明：FGPC对于时变的非线性系统具有很好的控制效果。     

复杂非线性系统的子系统分解方法

利用社区发现算法研究了一种复杂非线性化工系统的子系统分解方法,并进行了分布式模型预测控制设计。引入信息图论的节点表示系统的状态、输入和输出变量,构建非线性过程系统的加权有向图,节点通过加权边连接,加权反映了节点间连接的强度,因而能够同时反映系统内部的连通性和连接强度。利用社区结构发现算法将所有变量分成子系统的群组,使得每个组内的关联比不同组间的相互作用强,从而得到复杂化工过程系统的子系统分解。针对连续搅拌反应釜过程,实施子系统分解,并设计分布式模型预测控制算法,结果表明,所提出的子系统分解方法更能考虑子系统之间的连接权重,得到更有利于分布式模型预测控制的子系统划分,提升系统控制的性能。     

Nonlinear predictive control using multi-rate sampling

A nonlinear predictive control (NLPC) strategy based on a nonlinear, lumped parameter model of the process is developed in this paper. A constrained optimization approach is used to estimate unmeasured state variables and load disturbances. Additional model/process mismatch is handled by using an additive output term which is equivalent to the Internal Model Control approach. Similar to linear predictive control methods, an optimal sequence of future control moves is determined in order to minimize an objective function based on a desired output trajectory, subject to manipulated variable constraints (absolute and velocity). Deadtime is explicitly included in the model formulation, giving NLPC the same deadtime compensation feature of linear model-predictive techniques. The multi-rate sampling nature of most chemical processes is also used to improve estimates of process disturbances. Infrequent composition measurements in conjunction with frequent temperature measurements are used to improve the “inferential” control of the composition in a continuous flow stirred tank reactor (CSTR).     

ADAPTIVE FUZZY MODEL BASED PREDICTIVE CONTROL OF AN EXOTHERMIC BATCH CHEMICAL REACTOR*

An adaptive fuzzy model based predictive control (AFMBPC) approach is presented to track the desired temperature trajectories in an exothermic batch chemical reactor. The AFMBPC incorporates an adaptive fuzzy modeling framework into a model based predictive control scheme to derive analytical controller output. This approach has the flexibility to cope with different fuzzy model structures whose choice also lead to improve the controller performance. In this approach, adaptation of fuzzy models using dynamic process information is carried out to build a predictive controller, thus eliminating the determination of a predefined fixed fuzzy model based on various sets of known input-output relations. The performance of the AFMBPC is evaluated by comparing to a fixed fuzzy model based predictive controller (FFMBPC) and a conventional PID controller. The results show the better suitability of AFMBPC for the control of highly nonlinear and time varying batch chemical reactors.     

基于控制器切换的模糊不确定网络化系统的稳定性

基于T-S模糊模型建立模糊网络化控制系统模型,研究了一类存在不确定参数的网络时滞系统在单包传输且没有丢包情况下的稳定性.为了增强其诱导时延的动态性能,假设存在有限个备选的控制增益己知的模糊状态反馈控制器,在每个备选的控制器均不能镇定系统的情况下,使用控制器切换技术及Lyapunov函数方法,设计切换律,得到了系统渐进稳定的一个充分条件,并且此条件可转化为求解线性矩阵不等式(LMIS)问题.最后仿真结果表明所设计策略的可行性和有效性.     

Bounded robust control of constrained multivariable nonlinear processes

This work focuses on control of multi-input multi-output (MIMO) nonlinear processes with uncertain dynamics and actuator constraints. A Lyapunov-based nonlinear controller design approach that accounts explicitly and simultaneously for process nonlinearities, plant-model mismatch, and input constraints, is proposed. Under the assumption that all process states are accessible for measurement, the approach leads to the explicit synthesis of bounded robust multivariable nonlinear state feedback controllers with well-characterized stability and performance properties. The controllers enforce stability and robust asymptotic reference-input tracking in the constrained uncertain closed-loop system and provide, at the same time, an explicit characterization of the region of guaranteed closed-loop stability. When full state measurements are not available, a combination of the state feedback controllers with high-gain state observes and appropriate saturation filters, is employed to synthesize bounded robust multivariable output feedback controllers that require only measurements of the outputs for practical implementation. The resulting output feedback design is shown to inherit the same closed-loop stability and performance properties of the state feedback controllers and, in addition, recover the closed-loop stability region obtained under state feedback, provided that the observer gain is sufficiently large. The developed state and output feedback controllers are applied successfully to non-isothermal chemical reactor examples with uncertainty, input constraints, and incomplete state measurements. Finally, we conclude the paper with a discussion that attempts to put in perspective the proposed Lyapunov-based control approach with respect to the nonlinear model predictive control (MPC) approach and discuss the implications of our results for the practical implementation of MPC, in control of uncertain nonlinear processes with input constraints.     

Distributed output‐feedback fault detection and isolation of cascade process networks

Distributed output‐feedback fault detection and isolation (FDI) of nonlinear cascade process networks that can be divided into subsystems is considered. Based on the assumption that an exponentially convergent estimator exists for each subsystem, a distributed state estimation system is developed. In the distributed state estimation system, a compensator is designed for each subsystem to compensate for subsystem interaction and the estimators for subsystems communicate to exchange information. It is shown that when there is no fault, the estimation error of the distributed estimation system converges to zero in the absence of system disturbances and measurement noise. For each subsystem, a state predictor is also designed to provide subsystem state predictions. A residual generator is designed for each subsystem based on subsystem state estimates given by the distributed state estimation system and subsystem state predictions given by the predictor. A subsystem residual generator generates two residual sequences, which act as references for FDI. A distributed FDI mechanism is proposed based on residuals. The proposed approach is able to handle both actuator faults and sensor faults by evaluating the residual signals. A chemical process example is introduced to demonstrate the effectiveness of the distributed FDI mechanism. © 2017 American Institute of Chemical Engineers AIChE J, 63: 4329–4342, 2017     

APPLICATION OF FUZZY ADAPTIVE CONTROLLER IN NONLINEAR PROCESS CONTROL

In general, physical processes are usually nonlinear and control system design based on the linearization technique cannot control the process well for a wide range of operation. Use of the variable transformation method may not always solve the problem. In this paper, a fuzzy adaptive controller is proposed to control the nonlinear process. The CSTR control problem has also been considered. The results are compared with the method of nonlinear model predictive control (NMPC) with constrained and unconstrained control variables. A fuzzy model-following control system scheme is also proposed. The results show that the proposed controller is a feasible control structure for a nonlinear or parameter-variations process control.     

基于T-S模糊模型的间歇过程的迭代学习容错控制

间歇过程不仅具有强非线性,同时还会受到诸如执行器等故障影响,研究非线性间歇过程在具有故障的情况下依然稳定运行至关重要。针对执行器增益故障及系统所具有的强非线性,提出一种新的基于间歇过程的T-S模糊模型的复合迭代学习容错控制方法。首先根据间歇过程的非线性模型,利用扇区非线性方法建立其T-S模糊故障模型,再利用间歇过程的二维特性与重复特性,在2D系统理论框架内,设计2D复合ILC容错控制器,进而构建此T-S模糊模型的等价二维Rosser模型,接着利用Lyapunov方法给出系统稳定充分条件并求解控制器增益。针对强非线性的连续搅拌釜进行仿真,结果表明所提出方法具有可行性与有效性。