间歇过程的批内自优化控制

针对间歇过程的实时优化问题, 提出了一种基于自优化控制的批内优化方法. 以测量变量的线性组合为被控变量, 在单批次内跟踪控制被控变量实现间歇过程的实时优化. 根据是否在间歇过程的不同阶段切换被控变量, 给出了两种自优化控制策略, 对每种策略又分别提出两种设定轨线选取方案. 为求解这些情形下的最优被控变量(组合矩阵), 以最小化平均经济损失为目标, 推导了组合矩阵和经济损失之间的函数关系, 分别将其描述为相应的非线性规划问题. 在此基础上, 进一步引入了扩张组合矩阵, 将这些非线性规划问题归纳为求解扩张组合矩阵的一致形式(扩张组合矩阵具有不同的结构约束), 并推导得到了其中一种方案的解析解计算方法. 以一个间歇反应器为研究对象, 验证了方法的有效性.     

基于分段线性化法的改进自主优化控制

现有自主优化控制方法(Self-optimizing control, SOC)均基于系统名义工作点的线性化模型,过程的非线性较强时, 由线性化误差导致的损失将显著影响控制系统的自主优化性能.提出了一种基于分段线性化模型的改进自主优化控制方法, 获取不同扰动工况下的分段线性化模型,分别应用线性化SOC方法并构造总的被控变量. 新方法能够在更大范围的操作空间内最小化平均损失,具有更加优异的自主优化效果. 对一个数值算例和一个放热反应过程的研究验证了提出方法的有效性.     

自适应扰动估计的相关积分优化方法及应用

针对传统相关积分优化方法,当系统扰动与调优变量相关时,在迭代优化的过程中,目标函数难以收敛到最优值的问题,提出了一种改进的相关积分优化方法用于稳态操作调优.基于数据驱动稳态模型,构造了自适应扰动估计器用来估计扰动均值,对最小二乘方法计算得到的调优变量梯度均值进行补偿,并修正调优变量,确保目标函数在调优的过程中收敛于最优值.仿真对比及工业应用结果证实了所提方法的可行性和有效性.     

基于互信息操作变量曲线参数化的间歇过程批内修正优化

针对间歇过程批次内扰动影响最终优化效果的问题,提出一种基于互信息操作变量曲线参数化的间歇过程批内修正优化方法.首先根据操作变量与指标变量间互信息和相关系数划分出操作变量曲线上对指标变量作用近似的时段;然后,结合操作变量曲线的形态特征选择有代表性参数建立优化模型,以降低优化模型求解的复杂度.考虑到生产过程噪声干扰影响最终优化效果,在批次内设置决策点,并根据当前工况信息对决策点后未实施的操作变量曲线进行调整,以减弱批次内扰动对最终优化效果的影响.最后,将所提出方法用于某一化工厂双酚A结晶过程的优化研究,通过仿真结果验证了该方法的有效性.     

PS转炉造渣过程的动态优化控制

建立了Pierce-Smith(PS)转炉造渣过程的非线性状态空问模型,并提出了基于生产质量指标反馈校正的铜锍造渣过程的动态优化控制方案.该方案首先基于最优模型求得最优控制律.为消除吹炼过程中的扰动以及其他不确定因素所带来的影响,再引入基于生产质量指标的反馈调整机制.其中反馈信息由软测量模犁根据进出转炉的物料计算得到,智能控制单元根据反馈的质量信息和期望的质量目标间的偏差对最优控制律进行补偿修正.并在该动态优化控制方案基础上,设计并开发了铜锍吹炼过程的优化控制指导决策系统.实际运行结果表明该系统优化了产品的质量,同时也实现了铜锍生产过程的节能.     

非线性过程自优化控制的快速算法

针对非线性过程的自优化控制问题,提出了一种求取被控变量的快速算法.不同于线性过程的自优化控制,本文方法基于系统的非线性模型,并且最小化全局平均损失.为快速求解得到的非凸非线性规划问题,作者对其进行了简化.讨论了被控变量解空间的相关特性,阐述了引入的正交酉约束的合理性,并进一步提出了求解次优被控变量的解析法.对一个数值算例和蒸发过程的研究结果表明,提出的快速算法是便捷的、有效的.     

多变量预测控制工程应用的控制模型前馈解耦策略

针对多变量预测控制计算量大、控制效果对扰动和模型失配敏感等特点,提出一种适用于预测控制工程应用的控制模型前馈解耦策略.基于结构分析,保留重要的被控变量与操作变量配对关系,将不重要的被控变量与操作变量配对作为前馈引入进行补偿,简化了系统结构,降低了系统耦合程度,减弱了预测控制器对扰动和模型失配的敏感程度,极端情况下形成的单入单出或小规模多入多出系统有效减小了在线计算量;基于分布式预测控制思想,给出控制模型前馈解耦策略的分散优化策略,进一步减小了系统规模和在线计算量.最后,通过仿真验证了所提策略的可行性与有效性.     

基于MMTMV方法的多变量时变扰动系统性能评估

工业控制系统性能评估关系到工业生产安全与企业绩效. 工业过程扰动频繁且存在时变特性,多变量时变扰动系统缺少统一有效的性能评估方法. 本文提出了基于多模型混合时变最小方差(Multi-model mixing time-variant minimum variance, MMTMV)的多变量系统性能评估方法. 首先,根据扰动作用起止时间设定混合权重,基于多模型混合的思想利用混合权重与每一时变扰动特性设计多变量MMTMV控制器. 然后,利用多变量MMTMV控制器得出各被控变量输出方差,将多变量MMTMV控制器下被控变量的平均方差作为性能评估的基准. 最后,本文通过在裂解过程和精馏过程中的控制性能评估应用,验证了多变量MMTMV评估方法的有效性.     

云计算环境下的模糊解耦能效优化算法研究

在保证云计算环境的高计算性能和较优服务质量的前提下,系统能效优化成为推广云计算所要重点解决的问题。为了适应多负载和多任务的云计算任务环境,设计了一种模糊解耦能效优化方案。首先进行输入输出及中间变量参数的设定;然后建立模糊神经网络(Fuzzy Neural Network,FNN)模型及解耦规则,对影响能效指标的关键参数进行提取和优化,该方法能快速找到影响能效的关键因素并对其进行评估,从而实现稳定可控的能效优化;最后加入模糊解耦的参数扰动自调整设计,对解耦运算遇到的参数扰动进行自适应调整,提高系统的鲁棒性。     

基于改进卡尔曼滤波器的扰动抑制无模型自适应控制方案

针对无模型自适应控制方法在测量扰动作用下控制效果不佳的问题, 本文提出了一种新的扰动抑制无模 型自适应控制方案. 首先基于受控系统的动态线性化数据模型及测量扰动的统计特性, 在最小方差估计准则下推导 了基于系统输入输出数据的改进卡尔曼滤波器. 然后基于此滤波器给出了一种新的扰动抑制无模型自适应控制方 案. 该方案仅需用到受控系统的输入输出数据, 即可实现在强测量扰动作用下系统的无模型自适应控制. 仿真结果 显示, 相比现有的扰动抑制无模型自适应控制方案, 该方案在系统跟踪常值参考信号、时变参考信号时均能有效地 抑制测量扰动, 适用性更好的同时可以获得更小的跟踪误差及更大的数据信噪比.     

Self-optimizing control – A survey

Self-optimizing control is a strategy for selecting controlled variables. It is distinguished by the fact that an economic objective function is adopted as a selection criterion. The aim is to systematically select the controlled variables such that by controlling them at constant setpoints, the impact of uncertain and varying disturbances on the economic optimality is minimized. If a selection leads to an acceptable economic loss compared to perfectly optimal operation then the chosen control structure is referred to as “self-optimizing”. In this comprehensive survey on methods for finding self-optimizing controlled variables we summarize the progress made during the last fifteen years. In particular, we present brute-force methods, local methods based on linearization, data and regression based methods, and methods for finding nonlinear controlled variables for polynomial systems. We also discuss important related topics such as handling changing active constraints. Finally, we point out open problems and directions for future research.     

NCO tracking and self-optimizing control in the context of real-time optimization

This paper reviews the role of self-optimizing control (SOC) and necessary conditions of optimality tracking (NCO tracking) as presented by [1]. We show that self-optimizing control is not an alternative to NCO tracking for steady state optimization, but is to be seen as complementary. In self-optimizing control, offline calculations are used to determine controlled variables (CVs), which by use of a lower layer feedback controller indirectly keep the process close to the optimum when a disturbance enters the process. Preferably, the setpoints are kept constant, but they may be adjusted by some optimization layer. Good CVs reduce the need for frequent setpoint changes. When selecting self-optimizing CVs, a set of disturbances must be assumed, as unexpected disturbances are not rejected in SOC. On the other hand, the presented NCO tracking procedure adapts the inputs at given sample times without a model or any assumptions on the set of disturbances. However, disturbances with high frequencies or those which do not lead to a steady state are not rejected. By using NCO tracking in the optimization layer and SOC in the lower control layer, we demonstrate that the methods complement each other, with SOC giving fast optimal correction for expected disturbances, while other disturbances are compensated by the model free NCO tracking procedure on a slower time scale.     

Multiparameter self-optimizing systems using correlation techniques

A class of self-optimizing systems which continually alter their parameters to reduce a mean-square performance criterion is described. The change in each parameter is determined from an error gradient in parameter space computed by cross-correlation methods which are independent of signal spectra and require no test signal or parameter perturbation. Applications of this technique to both open-loop and closed-loop systems are included and it is shown that a combination of such self-optimizing systems is a possible solution to the adaptive control problem. Computer simulation results are included to demonstrate the practicality of the proposed systems.     

不同目标函数对用GA寻优Fuzzy控制规则的影响

本文首先简单介绍了遗传算法和模糊控制自寻优,然后给出了用遗传算法对模糊控制规则进行了自寻优的方法,并通过仿真比较不了同目标函数对用遗传算法寻优模糊控制规则的影响,得出了一些结论。     

Multidimensional Taylor network adaptive control for MIMO time‐varying uncertain nonlinear systems with noises

In this paper, an adaptive control approach based on the multidimensional Taylor network (MTN) is proposed here for the real‐time tracking control of multiple‐input–multiple‐output (MIMO) time‐varying uncertain nonlinear systems with noises. Two MTNs are used to formulate the optimum control and adaptive filtering approaches. The feed‐forward MTN controller (MTNC) is developed to realize the precise tracking control. The closed‐loop errors between the filtered outputs and expected values are directly chosen as the MTNC's inputs. A valid initial value selection scheme for the weights of the MTNC, which can ensure the initial stability of adaptive process, is introduced. The proposed MTNC can update its weights online according to errors caused by system's uncertain factors, based on stable learning rate. The resilient backpropagation algorithm and the adaptive variable step size algorithm via linear reinforcement are utilized to update the MTNC's weights. The MTN filter (MTNF) is developed to eliminate measurement noises and other stochastic factors. The proposed adaptive MTN filtering system possesses the distinctive properties of the Lyapunov theory–based adaptive filtering system and MTN. Lyapunov function of the filtering errors between the measured values and MTNF's outputs is defined. By properly choosing the weights update law in the Lyapunov sense, the MTNF's outputs can asymptotically converge to the desired signals. The design is independent of the stochastic properties of the input disturbances. Simulation of the MTN‐based control is conducted to test the effectiveness of the presented results.     

烧结点火模糊控制

针对烧结点火控制，综合考虑强度、温度对点火质量的影响，提出一种强度控制、温度辅控方案。为进一步提高控制质量，燃气、空气回路采用Fuzzy－PID复合控制，空／燃比可在线变步长寻优。     

网络化多轴运动控制系统的容侵同步控制

针对网络攻击下的不确定网络化多轴运动控制系统,提出一种基于分布式中间观测器的容侵同步控制方法.首先将不确定性分解为匹配分量和不匹配分量,继而通过分布式中间观测器估计由执行器攻击、领航者的非零输入以及匹配不确定性分量构成的组合未知输入信号,进而设计基于估计值的容侵同步控制协议对匹配未知输入进行有效补偿,同时通过调节特定参数充分抑制不匹配不确定性效应,最终得到满意的容侵同步控制性能.