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.     

Theoretical analysis of unconstrained nonlinear model predictive control

Model predictive control (MPC) is a well-established controller design strategy for linear process models. Because many chemical and biological processes exhibit significant nonlinear behaviour, several MPC techniques based on nonlinear process models have recently been proposed. The most significant difference between these techniques is the computational approach used to solve the nonlinear model predictive control (NMPC) optimization problem. Consequently, analysis of NMPC techniques is often connected to the computational approach employed. In this paper, a theoretical analysis of unconstrained NMPC is presented that is independent of the computational approach. A nonlinear discrete-time, state-space model is used to predict the effects of future inputs on future process outputs. It is shown that model inverse, pole-placement, and steady-state controllers can be obtained by suitable selection of the control and prediction horizons. Moreover, the NMPC optimization problem can be modified to yield nonlinear internal model control (NIMC). The computational requirements of NIMC are considerably less than NMPC, but the NIMC approach is currently restricted to nonlinear models with well-defined and stable inverses. The NIMC controller is shown to provide superior servo and regulatory performance to a linear IMC controller for a continuous stirred tank reactor.     

基于混合逻辑的非线性系统多模型预测控制

针对已有的多模型预测控制算法在模型预测过程中采用局部线性模型进行预测而产生的预测误差较大这一问题, 本文将非线性过程的多模型描述与输出预测之间的因果关系以约束条件的形式引入到模型预测控制的设计中, 将非线性过程描述成为一个混合逻辑动态系统模型, 模型切换规则以先验知识的形式引入到多模型预测过程中, 该模型可以全局地表征非线性过程的特性, 从而解决了多模型约束非线性预测控制的模型预测与模型切换问题.     

一类非线性时滞过程的自适应控制

通过结合非线性过程的一般模型控制(GMC)、强跟踪预测器(STP)和强跟踪滤波器(SIF),提出了一类具有输入时滞非线性时变过程的自适应一般模型控制(AGMC)方法.基于强跟踪预测器对未来状态的预测,传统的一般模型控制被扩展到一类具有输入时滞的非线性过程.通过强跟踪滤波器估计非线性过程的时变参数,对STP和GMC进行在线参数修正.对三容水箱系统DTS200进行计算机仿真,仿真结果表明,该自适应控制策略是令人满意的,其状态跟踪能力强,对于模型失配也具有较强的鲁棒性.     

基于最优保留遗传算法的非线性模糊预测控制

针对传统的模型预测控制不能很好解决具有严重非线性、不确定性的对象或过程的控制问题。提出将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测器，采用一种收敛精度高、速度快的具有最优保留特性遗传算法(EGA)依据模型预测输出在线滚动求解控制律的非线性预测控制算法。仿真结果表明该算法对一类非线性系统具有较快的响应速度和较强的抗干扰能力。     

基于最优保留遗传算法的非线性模糊预测控制

针对传统的模型预测控制不能很好解决具有严重非线性、不确定性的对象或过程的控制问题，提出将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作为模型输入，从而构成了模糊多步预测器。采用一种收敛精度高、速度快的具有最优保留特性遗传算法(EGA)，依据模型预测输出在线滚动求解控制律的非线性预测控制算法。仿真结果表明该算法对一类非线性系统具有较快的响应速度和较强的抗干扰能力。     

Adaptive neural network predictive control for nonlinear pure feedback systems with input delay

This paper presents an adaptive neural control design for nonlinear pure-feedback systems with an input time-delay. Novel state variables and the corresponding transform are introduced, such that the state-feedback control of a pure-feedback system can be viewed as the output-feedback control of a canonical system. An adaptive predictor incorporated with a high-order neural network (HONN) observer is proposed to obtain the future system states predictions, which are used in the control design to circumvent the input delay and nonlinearities. The proposed predictor, observer and controller are all online implemented without iterative predictive calculations, and the closed-loop system stability is guaranteed. The conventional backstepping design and analysis for pure-feedback systems are avoided, which renders the developed scheme simpler in its synthesis and application. Practical guidelines on the control implementation and the parameter design are provided. Simulation on a continuous stirred tank reactor (CSTR) and practical experiments on a three-tank liquid level process control system are included to verify the reliability and effectiveness.     

Multivariable predictive control of a pressurized tank using neural networks

The behavior of a multivariable predictive control scheme based on neural networks applied to a model of a nonlinear multivariable real process, consisting of a pressurized tank is investigated in this paper. The neural scheme consists of three neural networks; the first is meant for the identification of plant parameters (identifier), the second one is for the prediction of future control errors (predictor) and the third one, based on the two previous, compute the control input to be applied to the plant (controller). The weights of the neural networks are updated on-line, using standard and dynamic backpropagation. The model of the nonlinear process is driven to an operation point and it is then controlled with the proposed neural control scheme, analyzing the maximum range over the neural control works properly.     

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.     

一类非线性时滞过程的传感器主动容错控制

通过结合非线性过程的一般模型控制(GMC)、强跟踪预测器(STP)和强跟踪滤波器(STF),本文提出了一类具有输入时滞非线性时变过程的传感器主动容错控制方法.基于强跟踪预测器对未来状态的预测,传统的一般模型控制被扩展到一类具有输入时滞的非线性过程.然后采用强跟踪滤波器估计过程状态及传感器偏差,传感器偏差估计用于驱动一个故障检测逻辑.当某一传感器故障被检测出来时,STF的状态估计值将用于重构过程输出(代替真实输出),此重构输出被STP用于继续进行状态预测,从而确保系统性能.最后,三容水箱系统仿真结果证明该方法的有效性.     

Nonlinear system identification for predictive control using continuous time recurrent neural networks and automatic differentiation

In this paper, a continuous time recurrent neural network (CTRNN) is developed to be used in nonlinear model predictive control (NMPC) context. The neural network represented in a general nonlinear state-space form is used to predict the future dynamic behavior of the nonlinear process in real time. An efficient training algorithm for the proposed network is developed using automatic differentiation (AD) techniques. By automatically generating Taylor coefficients, the algorithm not only solves the differentiation equations of the network but also produces the sensitivity for the training problem. The same approach is also used to solve the online optimization problem in the predictive controller. The proposed neural network and the nonlinear predictive controller were tested on an evaporation case study. A good model fitting for the nonlinear plant is obtained using the new method. A comparison with other approaches shows that the new algorithm can considerably reduce network training time and improve solution accuracy. The CTRNN trained is used as an internal model in a predictive controller and results in good performance under different operating conditions.     

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模型的预测控制及应用

由于工业实践的需要,非线性预测控制近年来受到广泛地关注.Volterra模型是一类特殊的非线性模型,非常适合描述工业过程中的无记忆非线性对象.传统的基于Volterra模型的控制器合成法及迭代计算预测控制器法计算量大,且不便于处理控制约束.非线性模型预测控制求解是典型的非线性规划问题,序列二次规划(sequential quadratic program,SQP)算法是求解非线性规划问题常用方法之一.针对Volterra非线性模型预测控制求解问题,本文将滤子法与一种信赖域SQP算法相结合,提出一种改进SQP算法用于基于非线性Volterra模型的带控制约束的多步预测控制求解,并分析了所提方法的收敛性.工业实例仿真结果证实了所提方法的可行性与有效性.     

Economic model predictive control of nonlinear singularly perturbed systems

We focus on the development of a Lyapunov-based economic model predictive control (LEMPC) method for nonlinear singularly perturbed systems in standard form arising naturally in the modeling of two-time-scale chemical processes. A composite control structure is proposed in which, a “fast” Lyapunov-based model predictive controller (LMPC) using a quadratic cost function which penalizes the deviation of the fast states from their equilibrium slow manifold and the corresponding manipulated inputs, is used to stabilize the fast dynamics while a two-mode “slow” LEMPC design is used on the slow subsystem that addresses economic considerations as well as desired closed-loop stability properties by utilizing an economic (typically non-quadratic) cost function in its formulation and possibly dictating a time-varying process operation. Through a multirate measurement sampling scheme, fast sampling of the fast state variables is used in the fast LMPC while slow-sampling of the slow state variables is used in the slow LEMPC. Appropriate stabilizability assumptions are made and suitable constraints are imposed on the proposed control scheme to guarantee the closed-loop stability and singular perturbation theory is used to analyze the closed-loop system. The proposed control method is demonstrated through a nonlinear chemical process example.     

时变大纯滞后系统的单神经元自适应控制

阐述一种新型单神经元自适应控制器,对时变大纯滞后系统实现快速有效的实时控制。该单神经元采用一种新学习算法,并与Smith补偿、在线辨识相结合,在保留单神经元器适应性强优点的同时。改善了单神经元器的动态性能,减轻了大滞后对象控制结果不能及时反馈的不足。应用该控制策略对大滞后一阶仿真研究表明,对大滞后时变系统具有较强的适应性和鲁棒性,各种控制性能优于常规单神经元PID和常规PID．     

利用BP算法的一种自适应模糊预测控制器

提出一种由模糊预测器和模糊预测控制器组成的自适应模糊预测控制方案,采用ＢＰ算法训练模糊预测器和模糊预测控制器,并给出这种模糊预测控制器的训练算法。控制系统对于具有纯时延的非线性被控过程有良好的控制性能。     

Time-varying feedforward and output feedback controllers for nonlinear time-delay processes

The problem of output regulation of nonlinear time-delay processes with time-varying disturbances is considered. We present a new disturbance-dependent coordinate transformation for the linearization of nonlinear time-varying processes. In our proposed controller configuration, the modified Smith-type predictor plays the central role. Using this approach, the modified Smith-type predictor is composed of a nonlinear process model and a linear internal model. The presented feedforward and dead-time compensation can eliminate the effect of delayed disturbances on the output. With the aid of the incorporating linear internal model it aims to achieve both the asymptotic output regulation and the dead time compensation. A reduced-order controller structure for nonlinear time-delay processes can also asymptotically track the desired trajectory. Finally, the synthesis controllers are successfully implemented for chemical reactor systems.     

基于自适应递归模糊神经网络的污水处理控制

针对污水处理过程中具有的非线性、大时变等特征,提出了一种基于自适应递归模糊神经网络(recurrent fuzzy neural network,RFNN)的污水处理控制方法.该方法利用自适应RFNN识别器建立污水处理过程的非线性动态模型,建立的模型可以为RFNN控制器提供污水处理过程中的状态变量信息,保证了控制器根据系统响应调整操作变量的精确性;并且RFNN辨识器及RFNN控制器基于自适应学习率进行学习,确保了递归模糊神经网络的收敛精度和速度,并通过构造李雅普诺夫函数证明了此算法的收敛性;最后,基于基准仿真模型(benchmark simulation model 1,BSM1)平台进行仿真实验.结果表明,与PID、模型预测控制及前馈神经网络相比,该方法对污水处理中溶解氧浓度和硝态氮浓度的跟踪控制精度具有明显的提升.     

一种新的模糊内模预估控制器及应用

对不确定时滞系统, 提出了一种新的模糊内模预估控制方法. 它的最大特点是采用智能化的模糊模型预估器作为被控过程的内部模型, 对实际输出起预测作用, 从而克服时滞对系统带来的不利影响. 同时, 根据预测误差建立一模糊内模控制器, 在线修正、补偿被控过程的模型失配. 数字仿真和电阻炉温度实时控制结果表明, 这种控制方法优于常规控制方法; 有一定的实用前景.     

Developing a robust model predictive control architecture through regional knowledge analysis of artificial neural networks

Chemical processes are nonlinear. Model based control schemes such as model predictive control are highly related to the accuracy of the process model. For a highly nonlinear chemical system, it is clear to implement a nonlinear empirical model, such as artificial neural network model, should be superior to a linear model such as dynamic matrix model. However, unlike linear systems, the accuracy of a nonlinear empirical model strongly depends on its original data or training data based on how the model is built up. A regional-knowledge index is proposed in this study and applied in the analysis of dynamic artificial neural network models in process control. New input patterns that imply extrapolations and thus unreliable prediction by an artificial neural network model can be recognized from a significant decrease in the regional-knowledge index. To tackle the extrapolation problem and assure stability of the control system, we propose to run a neural adaptive controller in parallel with a model predictive control. A coordinator weights the outputs of these two controllers to make the final control decision. The present state of the controlled process and the model fitness to the present input pattern determine the weightings of the controller's output. The proposed analysis method and the modified model predictive control architecture have been applied to a neutralization process and excellent control performance is observed in this highly nonlinear system.