基于动态补偿逆的非线性RBF内模控制及其应用

针对非线性不确定系统和传统的非线性内模控制在控制上存在的不足,提出一种基于动态补偿逆的非线性不确定系统RBF内模控制,在引入RBF建立逆模型的同时,将无模型自适应控制方法作为附加控制器,用于模型偏离被控对象时在线修正逆模型。仿真结果表明,本文提出的方法不仅对系统的常量摄动具有较好的鲁棒性,对时变不确定性仍然保持较好的跟踪效果,具有较好的实时性、鲁棒性和在线校正功能。     

基于三角区间软约束的模型预测控制算法

针对设定值控制多入多出(MIMO)系统在外界干扰下系统自由度低和鲁棒性差的问题, 提出了兼顾定值控制与鲁棒性的基于三角区间软约束的模型预测控制算法。文中算法在设定值控制的基础上增加了三角区间软约束, 使得控制目标分阶段达成, 以减小干扰对系统的影响, 提高系统自由度及鲁棒性。最后, 对算法的鲁棒性进行了分析, 并采用Shell公司的典型重油分馏塔进行仿真实验, 通过与设定值控制结果的对比, 证明了文中算法有更好的鲁棒性及更优良的控制品质。     

工业串联系统的多约束广义预测控制

针对由一系列环节串联而成且存在多种约束的复杂工业对象,利用过程的中间变量进行预测和反馈,在充分考虑控制量幅值约束、控制增量幅值约束、中间变量幅值约束、输出误差约束以及最终输出幅值约束共同构成的多约束情况下,提出了工业串联系统的多约束广义预测控制算法。仿真结果验证了该算法对多约束串联系统的有效性和较强的鲁棒性。     

碳三加氢装置鲁棒最小协方差约束控制及应用

碳三加氢反应器操作条件多变，并且存在级联反应，传统的手动操作配氢容易过加氢或漏炔，导致出口甲基乙炔(MA)和丙二烯(PD)浓度波动较大，还会影响催化剂的选择性和转化率。由于碳三加氢过程具有非线性、操作区域多变的特点，传统的基于线性模型的模型预测控制器在工业过程控制中存在较大的局限性。Hammerstein模型是一种将非线性稳态环节和线性动态环节串联的非线性模型，非常适合描述非线性工业对象且方便控制器设计。提出了一种基于Hammerstein模型的鲁棒最小协方差约束控制方法，Hammerstein模型的非线性稳态部分采用WaveARX神经网络逼近，并根据多操作点稳态输入输出数据进行辨识，通过机理分析辨识得到的操作点附近线性化模型作为线性动态部分，将非线性鲁棒控制问题转化为线性模型鲁棒约束控制和非线性环节求逆问题，基于Hammerstein模型的建模偏差，对模型输出偏差协方差上限进行约束并最小化，采用线性矩阵不等式求解出次优状态反馈控制律，再根据非线性部分的逆模型得到系统的控制输入，仿真及工业应用结果证实了所提方法的可行性和有效性。     

基于动态RBF网络的预测控制在中速磨煤机优化控制的应用

针对大型火电厂直吹式制粉系统的时滞和非线性特点,将基于动态RBF网络模型的预测控制应用于MPS中速磨煤机的优化控制中。通过对M-RAN算法的改进,加快了对非线性系统的辨识速度。建模仿真证明了改进M-RAN算法的有效性和实时性,并结合预测控制解决了系统的时滞问题;应用结果也表明该方法具有良好的动态响应和较强的鲁棒性。     

基于自适应逆的温度控制系统

介绍了自适应逆控制方案,采用变步长LMS自适应滤波算法,用该算法建立对象的模型、对象逆模型,并设计了自适应逆控制器。将这种控制方案应用到含有大惯性、纯滞后的系统。仿真结果表明,此种方案鲁棒性及动态性能较好。     

一种基于速率线性化的非线性预测控制算法

提出了一种基于速率线性化方法的非线性预测控制算法。该算法采用速率线性化方法得到与原系统非线性模型相对应的线性变参数模型,这类变参数模型在结构上是线性的,而模型参数将随工作条件的变化而变化,在系统的整个工作区间内都能很好地逼近原非线性模型。在此模型的基础上设计了预测控制器,并利用基于置信域的Levenberg-Marquardt算法在线求得预测控制率。最后对连续搅拌反应釜进行了仿真研究,仿真结果表明了该算法的可行性和有效性。     

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。     

采用最小二乘支持向量机的PVC汽提温度逆补偿控制方法

基于最小二乘支持向量机(LSSVM)构建聚氯乙烯树脂(PVC)汽提过程的温度预测逆模型,将逆模型作为前馈控制器置于PVC汽提过程的前馈通道,以补偿汽提过程的非线性问题。在逆补偿控制的基础上加入PID控制器作为反馈控制器,对PVC汽提过程进行复合控制。通过汽提塔温度进行逆建模和仿真实验,仿真结果表明所提控制方法的控制效果更好。     

基于人工免疫PSO算法的球杆系统仿真研究

针对球杆系统非线性不稳定的特性,利用Matlab中的Simulink模块和True Time工具箱设计了人工免疫PSO算法网络控制系统。当网络控制系统存在一定的时延和滞后时,对人工免疫PSO算法的网络控制系统进行仿真分析。结果表明:采用基于人工免疫PSO算法的控制器的系统输出曲线超调量更小,调节时间更短,鲁棒性也优于一般PID控制器。     

Economic model predictive control of stochastic nonlinear systems

This work focuses on the design of stochastic Lyapunov‐based economic model predictive control (SLEMPC) systems for a broad class of stochastic nonlinear systems with input constraints. Under the assumption of stabilizability of the origin of the stochastic nonlinear system via a stochastic Lyapunov‐based control law, an economic model predictive controller is proposed that utilizes suitable constraints based on the stochastic Lyapunov‐based controller to ensure economic optimality, feasibility and stability in probability in a well‐characterized region of the state‐space surrounding the origin. A chemical process example is used to illustrate the application of the approach and demonstrate its economic benefits with respect to an EMPC scheme that treats the disturbances in a deterministic, bounded manner. © 2018 American Institute of Chemical Engineers AIChE J, 64: 3312–3322, 2018     

基于同址训练递推最小二乘支持向量机的分批补料生物反应器中溶氧浓度的非线性广义预测控制(英文)

In this study, Saccharomyces cerevisiae (baker’s yeast) was produced in a fed-batch bioreactor at the optimal dissolved oxygen concentration (DOC) and growth medium temperature. However, it is very difficult to control the DOC using conventional controllers because of the poorly understood and constantly changing dynamics of the bioprocess. A generalized predictive controller (GPC) based on a nonlinear autoregressive integrated moving average exogenous (NARIMAX) model is presented to stabilize the DOC by manipulation of air flow rate. The NARIMAX model is built by an improved recursive least-squares support vector machine, which is trained by an in-place computation scheme and avoids the computation of the inverse of a large matrix and memory reallocation. The proposed nonlinear GPC algorithm requires little preliminary knowledge of the fermentation process, and directly obtains the nonlinear model in matrix form by using iterative multiple modeling instead of linearization at each sampling period. By application of an on-line bioreactor control, experimental results demonstrate the robustness, effectiveness and advantages of the new controller.     

Multi-objective nonlinear model predictive control through switching cost functions and its applications to chemical processes

This paper proposes a switching multi-objective model predictive control (MOMPC) algorithm for constrained nonlinear continuous-time process systems. Different cost functions to be minimized inMPC are switched to satisfy different performance criteria imposed at different sampling times. In order to ensure recursive feasibility of the switching MOMPC and stability of the resulted closed-loop system, the dual-mode control method is used to design the switching MOMPC controller. In this method, a local control law with some free-parameters is constructed using the control Lyapunov function technique to enlarge the terminal state set of MOMPC. The correction termis computed if the states are out of the terminal set and the free-parameters of the local control laware computed if the states are in the terminal set. The recursive feasibility of the MOMPC and stability of the resulted closed-loop system are established in the presence of constraints and arbitrary switches between cost functions. Finally, implementation of the switching MOMPC controller is demonstrated with a chemical process example for the continuous stirred tank reactor.     

基于MLD模型的CSTR建模和控制

A novel control strategy for a continuous stirred tank reactor （CSTR） system, which has the typical characteristic of strongly pronounced nonlinearity, multiple operating points, and a wide operating range, is initiated from the point of hybrid systems. The proposed scheme makes full use of the modeling power of mixed logical dynamical （MLD） systems to describe the highly nonlinear dynamics and multiple operating points in a unified framework as a hybrid system, and takes advantage of the good control quality of model predictive control （MPC） to design a controller. Thus, this approach avoids oscillation during switching between sub-systems, helps to relieve shaking in transition, and augments the stability robustness of the whole system, and finally achieves optimal （i.e. fast and smooth） transition between operating points. The simulation results demonstrate that the presented approach has a satisfactory performance.     

一种新型的浊度控制算法

针对城市供水过程中,待滤水浊度控制过程的大惯性、大滞后、模型参数时变和干扰多等特点,采用一种新型的控制算法来实现对该过程的控制,其具体思想是:分别从对设定值的跟踪性要求和对扰动的抑制性要求出发,采用双控制器分别实现各自的控制,且两个独立的控制器分别采用常规PI和带非线性补偿的PI算法。并将该种新型控制算法和传统PID反馈控制算法、Smith预估控制算法和双PI控制算法进行了仿真比较。最后,还将该种算法实际应用到某水厂的中试实验基地。仿真和实验结果表明:所采用算法能够及时克服扰动,并对模型参数变化不敏感,具有很强的鲁棒性,对设定值具有良好的跟踪能力,确保了供水质量的稳定性。     

Neural‐Networks‐Based Feedback Linearization versus Model Predictive Control of Continuous Alcoholic Fermentation Process

In this work advanced nonlinear neural networks based control system design algorithms are adopted to control a mechanistic model for an ethanol fermentation process. The process model equations for such systems are highly nonlinear. A neural network strategy has been implemented in this work for capturing the dynamics of the mechanistic model for the fermentation process. The neural network achieved has been validated against the mechanistic model. Two neural network based nonlinear control strategies have also been adopted using the model identified. The performance of the feedback linearization technique was compared to neural network model predictive control in terms of stability and set point tracking capabilities. Under servo conditions, the feedback linearization algorithm gave comparable tracking and stability. The feedback linearization controller achieved the control target faster than the model predictive one but with vigorous and sudden controller moves.     

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.     

基于复合神经网络的离散非线性系统建模与控制

An adaptive inverse controller for nonliear discrete-time system is proposed in this paper. A compound neural network is constructed to identify the nonlinear system, which includes a linear part to approximate the nonlinear system and a recurrent neural network to minimize the difference between the linear model and the real nonlinear system. Because the current control input is not included in the input vector of recurrent neural network (RNN), the inverse control law can be calculated directly. This scheme can be used in real-time nonlinear single-input single-output (SISO) and multi-input multi-output (MIMO) system control with less computation work. Simulation studies have shown that this scheme is simple and affects good control accuracy and robustness.     

A delay‐dependent robust fuzzy MPC approach for nonlinear CSTR

Based on Takagi–Sugeno (T–S) fuzzy models, a robust fuzzy model predictive control (MPC) algorithm is presented for a class of nonlinear time‐delay systems with input constraints. Delay‐dependent sufficient conditions for the robust stability of the closed‐loop system are derived, and the condition for the existence of the fuzzy model predictive controller is formulated in terms of nonlinear matrix inequality via the parallel distributed compensation (PDC) approach. By using a novel matrix transform technique, a receding optimization problem with linear matrix inequality (LMIs) constraints is constructed to design the desired controllers with an on‐line optimal receding horizon guaranteed cost. Finally, an example of continuous stirred tank reactors (CSTR) is given to demonstrate the effectiveness of the proposed results.