工业过程多速率分层运行优化控制

工业过程运行优化控制通常采用基础回路层和运行层两层结构,涉及不同时间尺度特性的被控对象,且由于检测装置采样周期不同难以统一控制与采样周期;此外,运行层动态往往机理复杂难以建模.因此针对这一多层次、多时间尺度且部分模型未知的复杂多速率控制问题,本文提出一种工业过程多速率分层运行优化控制方法.该方法在使用提升技术解决分层多速率问题的基础上,采用一种基于Q-!学习的数据驱动运行层设定值优化方法,更新基础回路层的设定值;并针对提升后的系统采用模型预测控制（Model predictive control,MPC）方法设计基础回路层控制器以跟踪设定值,从而实现运行指标的优化控制.对典型工业闭路磨矿过程进行了仿真实验,验证了本文所提方法的有效性.     

大规模工业过程非凸稳态优化控制的两级协调法

针对具有输出关联不等式的束和非凸目标函数的大规模工业过程稳态优化控制问题，提出一种全新的两级协调求解算法。该算法由三层构成，上层和中层是两个协调器，下层是各局部决策单元。在较弱的条件下，获得了算法的全局收敛性定理。数字仿真结果表明了该算法的有效性和实用性。     

工业大系统多目标优化问题的分解协调方法

本文针对工业大系统多目标优化问题的特点,提出"一致单调性"概念、"目标诱导性决策"方法以及与分解协调方法有关的两个定理,使分解协调问题得以简化.     

复杂工业过程运行的混合智能优化控制方法

工业过程运行的优化控制的目标是将反应产品在加工过程中的质量、效率、消耗的工艺指标控制在目标值范围内. 由于复杂工业过程的工艺指标难于在线测量且与控制回路输出之间的动态特性具有强非线性、强耦合、难以用精确模型描述、随生产边界条件变化而变化的综合复杂性,因此, 难以采用已有优化控制方法, 运行控制只能采用人工设定的控制方式. 由于人工控制不能及时准确地随运行工况调整设定值, 难以将工艺指标控制在目标值范围内, 甚至造成故障工况.本文提出了根据运行工况实时调整控制回路设定值, 通过控制系统跟踪调整后的设定值, 将工艺指标控制在目标值范围内的过程优化运行的混合智能控制方法. 该方法由控制回路预设定模型、前馈补偿与反馈补偿器、工艺指标预报模型、故障工况诊断和容错控制器组成. 在某选矿厂22台竖炉组成的焙烧过程的应用案例, 证明了所提出方法的有效性.     

复杂工业过程的满意控制

本文从分析了工业过程优化控制的环境出发，针对复杂工业过程的特点与用户要求提出了满意控制的概念，给出了满意控制的基本框架，分析了它对复杂工业环境的适应性。     

复杂工业过程非串级双速率组合分散运行优化控制

复杂工业过程具有模型维数高、多时间尺度耦合、动态不确定性等特点, 其运行优化控制(Operational optimal control, OOC)一直是控制领域的研究难点与热点. 本文聚焦一类由多个快变且互联的设备单元与慢变且模型未知的运行过程串联组成的工业过程, 提出一种数据和模型混合驱动的非串级双速率组合分散运行优化控制方法. 该方法通过奇异摄动理论, 将非串级双速率运行优化问题描述为异步采样的慢子系统最优设定值跟踪和快子系统最优调节控制. 利用工业运行数据, 采用不依赖系统动态的Q-学习算法设计慢子系统最优跟踪策略, 克服运行过程模型难以建立的情形; 针对快子系统, 设计基于模型的分散次优控制策略, 并给出收敛因子的下界, 解决设备层互联项对系统稳定性的影响. 通过浮选过程仿真实验验证了所提控制方法的有效性.     

基于神经网络的复杂工业过程稳态优化控制策略及发展

本文结合神经网络的基本概念和理论,介绍了神经网络在复杂系统稳态优化控制领域的某些应用成果,并对其今后的发展方向进行了探讨。     

预测控制滚动优化的时间分解方法

基于大系统分解协调思想,针对预测控制系统,提出了一种带有并行结构的时间分解算法,以提高滚动优化在线计算效率.仿真结果表明了该算法的有效性.     

A simulation model of a coordinated decentralized supply chain

This paper presents a simulation‐based study of a coordinated, decentralized linear supply chain (SC) system. In the proposed model, any supply tier considers its successors as part of its inventory system and generates replenishment orders on the basis of its partners’ operational information. We show that the proposed coordinated decision‐making process creates a reduction in information distortion along an SC compared with a traditional, noncoordinated strategy. A novel result is that we show how a coordinated SC can avoid the detrimental consequences of demand amplification in terms of penalty costs due to the stockout phenomenon in upstream stages.     

动态交通流建模及协调控制与优化策略

根据快速路交通流特性,建立主、辅路的动态交通流模型.根据不同的优化性能指标,确立相应的主、辅路协调优化策略,以便进一步开发实时控制算法进行优化控制.     

Sequential stability and optimization of large scale decentralized systems

The notion of sequential stability in the synthesis of decentralized control for large scale systems is introduced in this paper. This notion is concerned with the property of a synthesis technique which allows the decentralized controllers of a large scale system to be connected to the systems one at a time (in a sequential way) such that the controlled system remains stable at all times. The motivation for introducing this constraint is that in practical terms, it is generally impossible to connect all decentralized controllers to a system simultaneously (due to the difficulties of communication etc.). A practical design procedure for the synthesis of a decentralized robust regulator for the servomechanism problem, based on a sequential approach to system design, is then given. The design procedure proceeds in two stages: (1) decentralized controllers are initially connected to the system in a sequential way to guarantee stability; (2) the parameters of the decentralized controllers are then sequentially adjusted, in a way to guarantee stability, so as to optimize a given performance index for the system. Applications of the above procedure are then made to the synthesis of centralized multivariable controllers and to the decentralized robust control of unknown systems.A simple example is given to illustrate the design synthesis.     

LMI optimization approach to robust decentralized controller design

A new approach for design of robust decentralized controllers for continuous linear time‐invariant systems is proposed using linear matrix inequalities (LMIs). The proposed method is based on closed‐loop diagonal dominance. Sufficient conditions for closed‐loop stability and closed‐loop block‐diagonal dominance are obtained. Satisfying the obtained conditions is formulated as an optimization problem with a system of LMI constraints. By adding an extra LMI constraint to the system of LMI constraints in the optimization problem, the robust control is addressed as well. Accordingly, the decentralized robust control problem for a multivariable system is reduced to an optimization problem for a system of LMI constraints to be feasible. An example is given to show the effectiveness of the proposed method. Copyright © 2010 John Wiley & Sons, Ltd.     

Adaptive decentralized control under non-uniqueness of the optimal control

We study the problem of decentralization of flow control in packet-switching networks under the isarithmic scheme. An incoming packet enters the network only if there are permits available at the entry port when it arrives. The actions of the controllers refer to the routing of permits in the network and the control variables are the corresponding probabilities. We study the behavior of adaptive algorithms implemented at the controllers to update these probabilities and seek optimal performance. This problem can be stated as a routing problem in a closed queueing network. The centralized version of a learning automation is a general framework presented along with the proof of asymptotic optimality. Decentralization of the controller gives rise to non-uniqueness of the optimal control parameters. Non-uniqueness can be exploited to construct asymptotically optimal learning algorithms that exhibit different behavior. We implement two different algorithms for the parallel operation and discuss their differences. Convergence is established using the weak convergence methodology. In addition to our theoretical results, we illustrate the main results using the flow control problem as a model example and verify the predicted behavior of the two proposed algorithms through computer simulations, including an example of tracking.The work of this author was partially supported by a grant from the Canadian Institute for Telecommunications Research under the NCE program of the Government of Canada, and partially supported by NSERC grant WFA 0139015 and FCAR-Québec grant 95-NC-1375.The work of this author was supported by a grant from the CITR under the NCE program of the Government of Canada.     

Plant-wide hierarchical optimization and control of an industrial hydrocracking process

Hydrocracking is a crucial refinery process in which heavy hydrocarbons are converted to more valuable, low-molecular weight products. Hydrocracking plants operate with large throughputs and varying feedstocks. In addition the product specifications change due to varying economic and market conditions. In such a dynamic operating environment, the potential gains of real-time optimization (RTO) and control are quite high. At the same time, real-time optimization of hydrocracking plants is a challenging task. A complex network of reactions, which are difficult to characterize, takes place in the hydrocracker. The reactor effluent affects the operation of the fractionator downstream and the properties of the final products. In this paper, a lumped first-principles reactor model and an empirical fractionation model are used to predict the product distribution and properties on-line. Both models have been built and validated using industrial data. A cascaded model predictive control (MPC) structure is developed in order to operate both the reactor and fractionation column at maximum profit. In this cascade structure, reactor and fractionation units are controlled by local decentralized MPC controllers whose set-points are manipulated by a supervisory MPC controller. The coordinating action of the supervisory MPC controller accomplishes the transition between different optimum operating conditions and helps to reject disturbances without violating any constraints. Simulations illustrate the applicability of the proposed method on the industrial process.     

基于扩张状态观测器的分散模型预测控制

针对复杂关联系统中分散控制方法无法有效解决子系统间的耦合和干扰问题, 提出一种基于扩张状态观测器的分散模型预测控制算法. 首先将复杂关联系统分解为多个状态维数较低、控制变量较少的子系统, 并为每个子系统设计本地预测控制器; 然后, 采用扩张状态观测器对子系统的耦合项以及干扰项进行估计, 进而利用估计值对子系统进行前馈补偿, 从而降低复杂关联系统的计算复杂度, 提高系统的稳定性和抗干扰能力; 最后, 利用液位控制系统验证了所提出算法的有效性.

     

On decentralized negotiation of optimal consensus

A consensus problem consists of finding a distributed control strategy that brings the state or output of a group of agents to a common value, a consensus point. In this paper, we propose a negotiation algorithm that computes an optimal consensus point for agents modeled as linear control systems subject to convex input constraints and linear state constraints. By primal decomposition and incremental subgradient methods, it is shown that the algorithm can be implemented such that each agent exchanges only a small amount of information per iteration with its neighbors.     

铅锌烧结过程质量产量的智能集成优化控制

针对铅锌烧结过程具有大滞后、多约束的特点,建立烧结块质量产量神经网络预测模型和优化控制模型,提出一种融合聚类搜索粗优化和混沌遗传细优化的智能集成优化控制方法.首先采用模糊聚类算法进行优化样本查询,所得结果作为问题的次优解;然后采用最优保存对简单遗传混沌算法进行二次优化,求取问题的最优解;最后对智能集成方法进行实际验证,系统运行结果表明.该方法较好地实现了高产、优质的生产目标.并且具有全局收敛性和工业有效性,为解决复杂工业过程的优化控制问题提供了一种有效、实用的新思路.     

多时标分散预测控制算法

针对一类各通道动态响应时间呈现较大差异的复杂大系统的控制问题, 提出了一种基于不同时标和分散通信模式下的预测控制算法. 该算法考虑了各个子系统的快慢特性和耦合关系, 采用纳什最优的思想, 并利用多时标信息预估方法实现整个系统的优化控制. 仿真实例验证了该算法的有效性.