基于参量预报的磁选管回收率智能优化控制

竖炉焙烧过程的关键工艺指标磁选管回收率难以实时测量,因而实现优化控制很困难.将优化设定、参量预报与回路控制技术相结合,提出一种磁选管回收率的智能优化控制方法.基于案例推理的优化设定模型根据工况的变化和磁选管同收率的实时预报值给出基础控制回路的设定值,并通过先进的控制方法实现回路的稳定控制.该方法应用于竖炉焙烧过程的生产实际,使磁选管回收率的实际值保持在其目标值范围内,取得显著应用成效.     

对角CARIMA模型多变量自适应约束广义预测控制

为了简化约束存在时多变量广义预测控制算法的设计与实现,依据对角CARIMA模型的结构特点,将多输入多输出对象的参数辨识和模型预报问题转化为一系列多输入单输出子对象的参数辨识和模型预报问题.推导了输入输出的约束形式及优化求解过程.简化了多变量对象的参数辨识、模型预报、目标函数和约束条件系数矩阵的计算.在由DCS控制的非线性液位装置上的对比实验结果表明了该方法的有效性.     

基于粒子群优化算法的熔融指数预报

针对丙烯聚合生产控制中聚丙烯熔融指数在线测量的控制要求,以及过程变量间相关性高的特点,提出一种实用高精度的软测量方法,以弥补传统的实验室分析严重滞后所导致的生产控制瓶颈问题。采用主元分析,提取少量主元反映多个变量的综合信息,以降低预报模型的复杂度。并在此基础上建立基于径向基函数神经网络的统计预报模型,提出利用粒子群优化算法优化神经网络的结构与参数,以减少人为因素对建模的影响,得到最优预报结果。通过对工厂实际生产过程进行详细的预报检测,并进一步与国内外相关研究报道进行比较,结果表明,所提出的预报方法具有更强的可靠性和更高的准确性。     

基于区间数的黄金湿法冶炼过程建模与优化研究

针对黄金湿法冶炼生产过程中某些关键变量不能准确在线测量，导致局部工序无法定量建模、难以基于定量模型实现过程优化控制的问题，提出一种基于区间数的过程分层优化方法.在对黄金湿法冶炼生产过程特点进行分析的基础上，提出了基于区间数的过程分层优化框架.基于专家知识和现场操作人员经验等信息，建立了调浆过程的模糊定性模型.结合氰化浸出和置换等工序的定量模型及调浆过程的定性模型，建立了以综合经济效益最大为优化目标的黄金湿法冶炼生产过程优化模型.针对模糊定性模型的每一输出模态，利用区间数代替无法检测关键变量，提出了基于区间优化和分层优化思想相结合的优化方法，实现了黄金湿法冶炼过程的优化.与传统全流程优化方法的仿真对比实验表明，所提方法在具有不确定性的流程工业生产过程优化中具有一定的应用价值.     

磁选管回收率智能混合预报方法

针对衡量竖炉焙烧过程焙烧矿质量好坏的关键工艺指标磁选管回收率难以在线测量、化验结果滞后的难题，采用神经网络、案例推理和专家系统技术，提出了由神经网络预报模型、案例推理预报模型、自校正模型组成的磁选管回收率智能混合预报模型，讨论了模型的结构、主要功能和实现算法，并成功应用于赤铁矿选矿厂竖炉焙烧过程．应用效果表明，在工况正常与异常两种情况下，所提出的方法均能准确预报磁选管回收率．将磁选管回收率预报模型应用于竖炉焙烧过程的优化控制，使磁选管回收率保持在最优工艺指标范围之内，取得了明显的成效．     

生物发酵过程中SSVIP-NNI软测量方法

生物发酵过程中关键生化变量难以直接检测,提出了一种利用基于敏感性与特异性的变量投影重要性(SSVIP)方法优化神经网络逆系统(NNI)的软测量方法。根据逆系统理论建立软测量模型,采用VIP变量优选方法,对软测量模型中的辅助变量进行优化。为了进一步提高VIP方法优选变量的性能,利用模型敏感性与特异性的概念,重新定义了VIP筛选阈值,确定最优的发酵过程软测量模型的变量。构造神经网络近似最优逆系统软测量模型,实现对发酵过程中关键过程变量的估计。通过Pensim仿真平台进行实验研究,结果表明经过辅助变量优选的神经网络逆系统软测量模型具有更高的估计精度和泛化能力。     

水泥生料预分解过程智能优化设定控制

在生料预分解过程中,由于生料边界条件频繁变化,致使产品的质量指标生料分解率过低或过高,从而增加了回转窑的负荷或导致最低一级旋风筒下料管堵塞.为了解决上述问题,本文提出了一个智能优化设定方法,由回路预设定模块、分解率预报模块、前馈补偿模块、反馈补偿模块组成.这个方法能够根据生料边界条件的变化在线调整控制回路的设定值.所提出的方法已经成功应用于酒钢宏达水泥生料预分解过程,取得了显著的应用效果.工业应用表明所提出的智能优化设定方法能够将生料分解率稳定在工艺范围内.     

智能优化算法及其在打浆优化中的应用

提出了一种遗传算法与神经网络技术相结合的智能优化算法,实现打浆过程的优化控制;首先针对打浆过程中系统的非线性、工艺参数间关系的不确定性,对打浆过程采用神经网络建立输入与输出之间的非线性模型,再利用遗传算法对控制参数寻求决策变量优化求解;通过改进遗传算法的交叉、变异算子等,使算法在优化过程中能有效地保持种群的多样性,防止种群过早收敛、局部收敛的现象,以实现打浆全局最优控制;实践表明,该智能优化算法,在满足打浆前后性能指标的同时明显降低了打浆能耗,是解决过程控制优化问题的可行之路。     

具有多优先级的多变量实时在线决策支持系统

生产过程中,操作人员的操作经验影响生产效益和安全,为此本文结合双层预测控制中稳态目标计算思想,提出了一种具有优先级的实时在线决策支持系统.针对实际生产过程无法确定准确的代价系数的问题,引入操作优先级的思想,结合稳态目标计算层被控变量的优先级优化方法,计算最优操作目标(被控变量和操作变量),解决了模块多变量操作指导中每层模块无法区分变量重要性的问题,并说明了二者在结构上的相似性.最后给出应用本文提及的方法进行决策支持的一个例子,验证该方法的有效性.     

基于COM的发酵过程调度优化软件开发

在生物制药多罐并行发酵过程中，关键变量在线检测困难，发酵过程的优化调度决策难度高。本文采用了基于COM的组件化程序设计方法，设计了多罐并行发酵过程的调度优化软件系统。利用该软件，可以实现发酵过程关键变量的超前预报和罐批的在线分类。并给出了以经济效益最优为目标的停罐调度策略。     

复杂高阶对象的预测PID控制

针对复杂高阶对象提出了一种基于数值最优模型降阶方法,并基于这种降阶模型设计了预测PID控制器,将此控制器应用于原始模型能够得到很好地控制效果。数值最优模型降阶算法使高阶对象能近似为一阶加时滞对象或二阶加时滞对象,通过模型阶跃响应和Bode图对比,降阶模型曲线很好地逼近原始模型曲线。预测PID对大时滞对象有着很好地控制效果,模型降阶使得预测PID很好地控制复杂高阶对象,且其结构简单,可调参数少的特点。     

低计算复杂度的永磁同步电机多步预测电流控制

传统的永磁同步电机模型预测电流控制策略仅在一个采样周期内寻优,难以避免陷入局部最优问题,而多步预测会增加预测次数,计算复杂度成倍增长.为此,提出一种低复杂度的永磁同步电机三步电流预测控制策略.首先,在延时补偿的基础上,两步预测结合三矢量电压控制和最优占空比电压控制,三步预测保持与两步预测相同的电压矢量,然后由代价函数选出控制电压矢量;最后,设计电感dq轴分量双闭环的鲁棒控制.仿真结果表明,相比其他控制策略,所提策略具有良好的动静态性能,寻优代码执行时间降低了约51%;在不影响输出电能质量的前提下,开关频率降低了约17%;并对电感失配造成的性能恶化具有抑制性.     

A reliable multi-objective control strategy for batch processes based on bootstrap aggregated neural network models

This paper presents a reliable multi-objective optimal control method for batch processes based on bootstrap aggregated neural networks. In order to overcome the difficulty in developing detailed mechanistic models, bootstrap aggregated neural networks are used to model batch processes. Apart from being able to offer enhanced model prediction accuracy, bootstrap aggregated neural networks can also provide prediction confidence bounds indicating the reliability of the corresponding model predictions. In addition to the process operation objectives, the reliability of model prediction is incorporated in multi-objective optimisation in order to improve the reliability of the obtained optimal control policy. The standard error of the individual neural network predictions is taken as the indication of model prediction reliability. The additional objective of enhancing model prediction reliability forces the calculated optimal control policies to be within the regions where the model predictions are reliable. By such a means, the resulting control policies are reliable. The proposed method is demonstrated on a simulated fed-batch reactor and a simulated batch polymerisation process. It is shown that by incorporating model prediction reliability in the optimisation criteria, reliable control policy is obtained.     

Optimal Control of Fed-Batch Processes Based on Multiple Neural Networks

The performance of empirical model based fed-batch process optimal control is strongly affected by the model prediction reliability at the end-point of a batch. An optimal control profile calculated from an empirical model may not give the best performance when applied to the actual process due to model-plant mismatches. To tackle this issue, a new method for improving the reliability of fed-batch process optimal control by incorporating model prediction confidence bounds is proposed. Multiple neural networks (MNN) are used to build an empirical model of fed-batch process based on process operation data. Model prediction confidence bounds are calculated based on predictions of all component networks in an MNN model and the model prediction confidence bound at the end-point of a batch is incorporated into the optimization objective function. The modified objective function penalizes wide prediction confidence bounds in order to obtain a reliable optimal control profile. The non-linear optimization problem based on MNN with augmented objective function is solved by iterative dynamic programming. The proposed control strategy is illustrated on a simulated fed-batch ethanol fermentation process. The results demonstrate that the optimal control profile calculated from the proposed approach is reliable in the sense that its performance degradation is limited when applied to the actual process.     

基于收缩约束模型预测控制的无人车辆路径跟踪

针对存在有界扰动的非线性无人驾驶车辆避障过程中最优路径规划跟踪问题,提出一种基于预测时域内系统输入输出收缩约束(PIOCC)的模型预测控制(MPC)方法.首先在构建目标函数时,为扩大可行性解的范围引入软约束思想,将最优规划路径的跟随问题转化为对模型预测控制优化问题的求解;其次为避免短预测时域造成闭环系统发散而导致在约束条件限定下出现无可行性解的情况,采用预测时域内系统输入输出收缩约束的方法,设计模型预测控制器;再次基于Lyapunov稳定性理论证明所设计的模型预测闭环控制系统是渐近稳定的;最后通过仿真实例验证了所提出基于PIOCC的控制策略在解决扩大可行解范围和避免闭环系统发散问题时的有效性,实现了无人驾驶车辆在路径跟踪时具有良好的快速性和稳定性.     

基于COBP模型的城市短期需水量预测研究

针对城市需水量预测中时间序列的非线性特性及传统BP网络预测收敛速度慢易陷入局部极小值等问题,将Chaos理论和BP神经网络理论相结合,提出了一种基于Chaos-BP理论的城市短期需水量COBP(ChaosBackPropagtion)预测模型。利用重构相空间的嵌入维数确定COBP网络的结构,通过混沌优化搜索,找到BP神经网络权值的全局最优值,并对其输出的“尖点”预测值进行混沌参数控制,实现城市短期需水量的预测。仿真分析表明,与传统预测模型相比,COBP预测模型所需训练数据样本少,收敛速度快、易达到全局最小值,预测结果整体误差的指标良好,呈现良好的综合预测性能。     

Identification of k-step-ahead prediction error model and MPC control

This work studies k-step-ahead prediction error model identification and its relationship to MPC control. The use of error criteria in parameter estimation will be discussed, where the identified model is used in model predictive control (MPC). Assume that the model error is dominated by the variance part, it can be shown that a k-step-ahead prediction error model is not optimal for k-step-ahead prediction. A normal one-step-ahead prediction error criterion will be optimal for k-step-ahead prediction. Then it is argued that even when some bias exists, the result could still hold true. Therefore, for MPC identification of linear processes, one-step-ahead prediction error models fever k-step-ahead prediction models. Simulations and industrial testing data will be used to illustrate the idea.     

基于模糊T-S模型的批过程建模与最优控制

基于模糊T-S模型,提出一种具有自学习能力的模糊方法用于批过程建模和最优控制.通过引入与均方误差相关的动态误差传递因子,使用改进的梯度下降法,本方法能够辨识模糊T-S预测模型.对于批过程的受限非线性最优控制,基于所辨识的预测模型,运用庞特里亚金最小值原理和平行分布补偿算法,本方法能够把一个复杂非线性系统最优控制设计问题转化为一些基于复杂T-S预测模型的局部线性系统的最优问题,从而给出一种有效和简单的模糊最优控制策略.所提方法用于一个半连续式反应器的建模和最优控制,仿真结果表明新方法是有效和准确的.     

Optimal tracking design for stochastic fuzzy systems

In general, fuzzy control design for stochastic nonlinear systems is still difficult since the fuzzy bases are stochastic so as to increase the difficulty and complexity of the fuzzy tracking control design. In this study, a fuzzy stochastic moving-average model with control input (fuzzy ARMAX model) is introduced to describe nonlinear stochastic systems. From the fuzzy ARMAX model, a fuzzy one-step ahead prediction model is developed. Based on a fuzzy one-step ahead prediction stochastic model, optimal design algorithms are proposed to achieve the optimal tracking of nonlinear stochastic systems. In this study, the minimum variance tracking control, generalized minimum variance tracking control, and the optimal model reference tracking control are developed for stochastic fuzzy systems. We construct some basic stability conditions for general stochastic fuzzy systems and use these conditions to verify the stability of the fuzzy tracking control systems. Finally, two simulation examples are given to indicate the performance of the proposed methods.     

Identification and predictive control of a melter unit used in the sugar industry

This paper presents the real identification and non-linear predictive control of a melter unit; the unit is used in a sugar factory placed in Benavente (Spain). The proposed approach uses a specific recurrent neural network that allows us to identify a non-linear model of the process, providing a mathematical representation in the state space form. Output and state variables can be obtained from the inputs and measured disturbances acting on the system. The neural based predictive control is carried out through the optimization of a cost function that takes into account the output prediction errors from a reference trajectory and the future control efforts, by using the identified model as a prediction model for the system outputs. The solution to this problem provides the optimal set of future control actions, but only the first one is applied to the real process, and the optimization problem is solved again at time t + 1.The results show the good performance of neural predictive control and its suitability for applications in real systems, particularly in the process industry.