实时优化与分布式控制集成算法

预测控制作为一种以预测模型为基础的先进控制算法,分布式预测控制广泛应用于复杂高维的复杂大系统控制中。提出了针对大规模过程的实时优化与分布式预测控制集成算法,包含稳态目标计算层和动态分布式控制层。在稳态目标计算层根据当前系统的运行状况进行集中优化,在系统运行的每一时刻计算出相对的全局最优值,并将其传递到下层进行控制。在动态控制层将复杂的大系统分为若干个相对独立的子系统,并且充分考虑各个子系统之间的关联和耦合,用分布式预测控制算法对上层计算得到的相对的全局最优值进行跟踪,提高系统在动态情况下的控制性能。仿真应用表明,此方法的优点在于保证全局最优的同时,降低了计算的复杂度,并且实现了经济目标。     

双层结构预测控制研究进展

现代工业大系统的优化控制采用递阶结构,其中以预测控制为代表的先进过程控制已经成为重要的一级.目前,主流的工业预测控制技术均采用双层结构,即包含稳态优化层和动态控制层.双层结构预测控制技术可以有效解决复杂工业过程常见的多目标优化、多变量控制的难点问题.本文简要总结了双层结构预测控制的算法,并从控制输入与被控输出稳态关系入手分析了多变量预测控制稳态解的相容性和唯一性,说明了稳态优化的重要性.针对双层结构预测控制与区间预测控制的性能比较、稳态模型的奇异性以及闭环系统动态特性等提出了一些见解,并指出了需要重点研究的主题.     

双层结构模型预测控制算法性能分析

双层结构模型预测控制是在常规模型预测控制的基础上集成了稳态目标计算层,所增加稳态目标计算层通过对给定外部目标进行调节或执行独立经济优化为底层动态控制提供设定值.本文着重于分析增加稳态目标计算层对常规模型预测控制策略在控制性能、经济性能、及鲁棒性方面的影响.阐明稳态目标计算是在动态不确定性下的最优决策过程,可实时考虑当前工况环境给出最佳设定值,使动态层控制量的计算更加合理,并可保证在动态控制层实现无静差控制;稳态目标计算层可利用包含目标跟踪和经济优化的性能指标,在保证跟踪控制要求的同时利用操作自由度进行经济性优化,有助于提高系统经济性能,此外由于操作自由度增加使得系统对于特定方向的扰动和通道失配具有更强的鲁棒性.最后通过实验仿真验证了所做分析的正确性.     

状态空间模型的双层结构预测控制算法

双层结构预测控制是指先进行设定值优化、再进行设定值跟踪的预测控制.在已有的双层结构动态矩阵控制的基础上,本文给出基于状态空间模型的双层结构预测控制算法.该算法基于干扰模型和新定义的开环预测值,给出了新的开环预测模块.该开环预测模块采用Kalman滤波方法得到操作变量、被控变量的开环动、稳态预测值.基于这些开环预测值,稳态目标计算模块的基本原理同双层结构动态矩阵控制,但是具体细节上遵循状态空间方法.动态控制模块基于稳态目标计算提供的操作变量、被控变量的稳态目标(设定值),采用二次规划算法计算控制作用.仿真算例证实了该算法的有效性.     

离线优化在线查表的双层结构预测控制

双层结构预测控制包括稳态优化和动态控制,计算复杂度较大,难以在实时性要求较高或者是主控制器计算能力较弱的场合应用.本文阐述了一种离线计算在线查表的稳态优化方法和详细的技术实施方案.首先采用枚举方式离线计算稳态优化值,然后通过在线查表得到实时优化值.当在线查表不可行时,通过查找距离该查表点最近的多个可查表点,以距离作为加权系数,近似计算最优稳态目标值.最后,通过性能分析和仿真实验证明了本文提出的该方法有效的降低了计算复杂度,减少了计算时间.     

多变量预测控制结构分解的图论方法

预测控制算法的计算复杂度主要由变量个数和控制时域决定, 而大型复杂系统中变量个数较多将导致计 算量大的问题, 尤其在有约束预测控制的优化求解中增加较重的计算负担. 本文针对此问题利用邻接矩阵、可达矩 阵和关联矩阵梳理系统传递函数模型中变量之间的关联, 将有关联的控制变量划分为一个子系统, 进而将一个大系 统分解成若干独立子系统, 即可将一个高维度的优化求解问题分解成多个维度较低的子优化问题, 降低计算复杂度 以达到减少计算量的目的. 最后将其应用在多变量有约束的双层结构预测控制算法中, 通过仿真进行验证.     

结合LM算法的PID神经网络预测控制系统

针对工业过程中非线性系统的复杂控制和实时性问题,提出1种基于LM算法的PID神经网络预测控制器的设计方法.该方法在原有的神经网络预测控制基础上,引入了1种新的神经网络结构与学习算法来建立预测模型,并针对该模型采用神经网络逆动态控制方法进行滚动优化以实现下一步控制量的优化.仿真结果表明,该方法有结构简单、计算量小、响应速度快等特点.在一定程度上满足复杂工业过程控制中的实时性要求.     

分布式预测控制在空气分馏塔中的应用

在大型空分控制系统中,集中式控制由于在线计算量大,对计算机的要求较高。采用分布式预测控制,把复杂系统的控制过程分散到各个子系统中去,降低了计算的复杂度,同时提高了系统的可靠性。建立了分馏塔的数学模型,并拆分成若干个子系统,利用纳什优化原理,获得了全局最优解。仿真结果表明,该算法取得了较好的控制效果。     

一种开环输出反馈预测控制

考虑状态不可测且具有多胞不确定性的系统.本文通过定义参数依赖的顶点控制作用,提出一种基于多胞描述模型的启发式开环输出反馈预测控制方案,其中顶点控制作用依赖于多胞描述模型参数,由多胞顶点值构成.在工业预测控制的框架下,方案包含开环预测、稳态目标计算、动态控制3个模块.上层稳态目标计算模块采用稳态模型优化得到稳态目标值,下层动态控制跟踪由稳态目标值计算得到的多胞顶点稳态目标值.考虑到工业预测控制中稳态目标计算模块常采用线性模型,本文通过在多胞描述模型中加入人工干扰进而改进控制方案,使得上层稳态目标计算模块可直接采用改进的稳态多胞描述模型,下层基于改进的动态预测方程跟踪顶点稳态目标值.通过仿真分别验证了两种方法的可行性和有效性.     

有界扰动多变量Hammerstein系统输入到 状态稳定模型预测控制

考虑具有状态和控制约束的有界未知扰动多变量Hammerstein系统,提出一种具有输入到状态稳定和有限L_2增益性能的鲁棒非线性模型预测控制策略.基于多变量线性子系统H_∞控制律,滚动预测非线性代数方程的解算误差,继而在线优化计算满足系统约束条件的预测控制量.利用输入到状态稳定性概念和L_2增益思想,建立闭环系统关于该扰动信号具有鲁棒稳定性和L_2增益的充分条件,使闭环系统不仅满足系统约束,而且对不确定扰动输入和解算误差具有鲁棒性.最后以工业聚丙烯多牌号切换过程控制为例,仿真验证本文算法的有效性.     

基于协调的变风量空调系统分布式预测控制

在实验和系统动力学行为分析的基础上,建立了变风量空调实验系统的内部模型,并分解为7个子系统。各个子系统分别采用模型预测控制（MPC）进行局部优化控制。在保证各个子系统之间网络连通和信息共享的基础上,将各个MPC的局部目标组合成系统级目标,从而把大规模的变风量空调控制系统在线优化问题转化为各子系统小规模的分布式优化问题。通过仿真和实验研究,验证了系统控制的效果。     

多目标优化策略在精馏过程中的应用研究

针对精馏塔自由度多,能耗大,扰动复杂多变,控制和优化要求高等特点,提出了一种基于动态矩阵控制(DMC)的二层多目标优化控制策略。在实现精馏过程控制目标的前提下,设计了二层优化指标,可在有多余自由度的情况下,进行二层控制和优化,实现精馏过程质量控制目标和经济运行优化。通过对Shell多侧线精馏过程的仿真研究,验证了二层多目标优化控制策略的有效性。与DMC控制相比,二层多目标优化控制策略可以明显改善精馏塔运行的经济性,实现增加收益的目标。     

分散式MPC经济性能评估

近年来,学术界对集中式模型预测控制 (Model predictive control, MPC) 性能评估进行了广泛的研究. 对于大规模化工过程, 工业现场通常采用分散式MPC的控制结构. 由于各子系统间存在复杂的耦合关系, 针对集中式MPC 的性能评估方法不能客观反映分散式MPC的性能. 本文基于线性矩阵不等式(Linear matrix inequality, LMI)的方法对分散式MPC进行经济性能评估. 首先提出了一种迭代方法求解分散式线性二次型调节器(Linear quadratic regulator, LQR)问题, 该方法显著降低了已有求解方法的保守性. 再利用LQR基准建立了一组随机优化命题对MPC进行经济性 能评估, 评估方法对集中式MPC与分散式MPC均适用, 评估结果可以指导MPC参数调整, 也可以为集中式与分散式MPC结构选择提供重要参考. 通过对重油分馏塔控制问题的仿真验证了本文方法的有效性与应用价值.     

提高分布式多预测控制器协调及稳定性的鲁棒区域控制

大型复杂化工程过程控制中,常规的集中式控制方式不利于实时性、灵活性和容错控制。而采用多预测控制器协调的分布式控制是解决这一问题的有效方法:。针对子系统间的动态耦合行为严重影响多预测控制器协调以及稳定性的问题,提出一种鲁棒区域控制策略。即在每个子系统的目标函数中加入松弛因子增加控制器间协调时的余量来达到分布式预测控制的稳定性。通过以反应器-存储器分馏器组成的过程为事例,仿真结果:表明该方法:的可行性和有效性。     

Distributed Model Predictive Control for Networked Plant-wide Systems With Neighborhood Cooperation

For large-scale networked plant-wide systems composed by physically (or geographically) divided subsystems, only limited information is available for local controllers on account of region and communication restrictions. Concerning the optimal control problem of such subsystems, a neighbor-based distributed model predictive control (NDMPC) strategy is presented to improve the global system performance. In this scheme, the performance index of local subsystems and that of its neighbors are minimized together in the determination of the optimal control input, which makes the local control decision also beneficial to its neighboring subsystems and further contributes to improving the convergence and control performance of overall system. The stability of the closed-loop system is proved. Moreover, the parameter designing method for distributed synthesis is provided. Finally, the simulation results illustrate the main characteristics and effectiveness of the proposed control scheme.      

Distributed model predictive control for polytopic uncertain systems subject to actuator saturation

In this paper, we present a distributed model predictive control (MPC) algorithm for polytopic uncertain systems subject to actuator saturation. The global system is decomposed into several subsystems. A set invariance condition for polytopic uncertain system with input saturation is identified and a min–max distributed MPC strategy is proposed. The distributed MPC controller is designed by solving a linear matrix inequalities (LMIs) optimization problem. An iterative algorithm is developed for making coordination among subsystems. Case studies are carried out to illustrate the effectiveness of the proposed algorithm.     

Price-driven coordination method for solving plant-wide MPC problems

In large-scale model predictive control (MPC) applications, such as plant-wide control, two possible approaches to MPC implementation are centralized and decentralized MPC schemes. These represent the two extremes in the “trade-off” among the desired characteristics of an industrial MPC system, namely accuracy, reliability and maintainability. To achieve optimal plant operations, coordination of decentralized MPC controllers has been identified as both an opportunity and a challenge. Typically, plant-wide MPC problem can be formulated as a large-scale quadratic program (QP) with linking equality constraints. Such problems can be decomposed and solved with the price-driven coordination method and on-line solutions of these structured large-scale optimization problems require an efficient price-adjustment strategy to find an “equilibrium price”. This work develops an efficient price-adjustment algorithm based on Newton’s method, in which sensitivity analysis and active set change identification techniques are employed. With the off-diagonal element abstraction technique and the enhanced priced driven coordination algorithm, a coordinated, decentralized MPC framework is proposed. Several case studies show that the proposed coordination-based decentralized MPC scheme is an effective approach to plant-wide MPC applications, which provides a high degree of reliability and accuracy at a reasonable computational load.     

Distributed Model Predictive Control Method for Optimal Coordination of Signal Splits in Urban Traffic Networks

Coordination and control approaches based on model predictive control (MPC) have been widely investigated for traffic signal control in urban traffic networks. However, due to the complex non‐linear characters of traffic flows and the large scale of traffic networks, a basic challenge faced by these approaches is the high online computational complexity. In this paper, to reduce the computational complexity and improve the applicability of traffic signal control approaches based on MPC in practice, we propose a distributed MPC approach (DCA‐MPC) to coordinate and optimize the signal splits. Instead of describing the dynamics of traffic flow within each link of the traffic network with a simplified linear model, we present an improved nonlinear traffic model. Based on the nonlinear model, an MPC optimization framework for the signal splits control is developed, whereby the interactions between subsystems are accurately modeled by employing two interconnecting constraints. In addition, by designing a novel dual decomposition strategy, a distributed coordination algorithm is proposed. Finally, with a benchmark traffic network, experimental results are given to illustrate the effectiveness of the proposed method.     

Nash-based robust distributed model predictive control for large-scale systems

In this paper, a new robust distributed model predictive control (RDMPC) is proposed for large-scale systems with polytopic uncertainties. The time-varying system is first decomposed into several interconnected subsystems. Interactions between subsystems are obtained by a distributed Kalman filter, in which unknown parameters of the system are estimated using local measurements and measurements of neighboring subsystems that are available via a network. Quadratic boundedness is used to guarantee the stability of the closed-loop system. In the MPC algorithm, an output feedback-interaction feedforward control input is computed by an LMI-based optimization problem that minimizes an upper bound on the worst case value of an infinite-horizon objective function. Then, an iterative Nash-based algorithm is presented to achieve the overall optimal solution of the whole system in partially distributed fashion. Finally, the proposed distributed MPC approach is applied to a load frequency control (LFC) problem of a multi-area power network to study the efficiency and applicability of the algorithm in comparison with the centralized, distributed and decentralized MPC schemes.