Stochastic simulation-based genetic algorithm for chance constraint programming problems with continuous random variables

In this article, we present a stochastic simulation-based genetic algorithm for solving chance constraint programming problems, where the random variables involved in the parameters follow any continuous distribution. Generally, deriving the deterministic equivalent of a chance constraint is very difficult due to complicated multivariate integration and is only possible if the random variables involved in the chance constraint follow some specific distribution such as normal, uniform, exponential and lognormal distribution. In the proposed method, the stochastic model is directly used. The feasibility of the chance constraints are checked using stochastic simulation, and the genetic algorithm is used to obtain the optimal solution. A numerical example is presented to prove the efficiency of the proposed method.     

基于多层概率集的随机预测控制算法设计

考虑具有乘型不确定性的离散随机系统约束控制问题, 设计了一种基于多层概率集的随机预测控制算法. 多层概率集描述了状态在多步反馈控制律下的一系列不同概率的分布区域, 因此能够同时保证多个不同概率要求的软约束. 通过动态优化多步反馈律, 算法具有较大的可行范围. 之后设计的简化算法在降低计算负担的同时保证了算法的可行范围.     

非线性模型预测控制的现状与问题

模型预测控制的主要优点是能显式并优化处理控制量和状态量的约束。为此,主要围绕非线性预测控制的算法、稳定性鲁棒性、对偶问题和流动时域估计的最新研究成果进行综述,并阐述了理论与应用方面有待进一步研究的几个主要问题。     

Model predictive control for systems with stochastic multiplicative uncertainty and probabilistic constraints

Robust predictive control handles constrained systems that are subject to stochastic uncertainty but propagating the effects of uncertainty over a prediction horizon can be computationally expensive and conservative. This paper overcomes these issues through an augmented autonomous prediction formulation, and provides a method of handling probabilistic constraints and ensuring closed loop stability through the use of an extension of the concept of invariance, namely invariance with probability p.     

约束非线性系统稳定经济模型预测控制

考虑约束非线性系统,提出一种新的具有稳定性保证的经济模型预测控制（Economic model predictive control,EMPC）策略.由于经济性能指标的非凸性和非正定性,引入关于经济最优平衡点的正定辅助函数.利用辅助函数的最优值函数定义原始EMPC优化问题的稳定性约束.应用终端约束集、终端代价函数和局部控制器三要素,建立闭环系统关于经济最优平衡点的渐近稳定性和渐近平均性能.进一步,结合多目标理想点概念,将提出的控制策略用于多个经济性能指标的优化控制,得到稳定多目标EMPC策略.最后,以连续搅拌反应器为例,比较仿真结果验证本文策略的有效性.     

An integrated perturbation analysis and Sequential Quadratic Programming approach for Model Predictive Control

Computationally efficient algorithms are critical in making Model Predictive Control (MPC) applicable to broader classes of systems with fast dynamics and limited computational resources. In this paper, we propose an integrated formulation of Perturbation Analysis and Sequential Quadratic Programming (InPA-SQP) to address the constrained optimal control problems. The proposed algorithm combines the complementary features of perturbation analysis and SQP in a single unified framework, thereby leading to improved computational efficiency and convergence property. A numerical example is reported to illustrate the proposed method and its computational effectiveness.     

MIMO系统的多模型预测控制

针对非线性多变量系统提出一种多模型预测控制(MMPC)策略.首先给出一种多模型辨识方法,利用模糊满意聚类算法将复杂非线性系统划分为若干子系统,并获得多个线性模型,通过模型变换得出全局系统模型,接着对全局MIMO系统设计MMPC,并进行了系统的性能分析,最后以pH中和过程为例,通过仿真研究验证了辨识和控制算法的有效性.     

Stable Model Predictive Control for Constrained Max-Plus-Linear Systems

Discrete-event systems with synchronization but no concurrency can be described by models that are “linear” in the max-plus algebra, and they are called max-plus-linear (MPL) systems. Examples of MPL systems often arise in the context of manufacturing systems, telecommunication networks, railway networks, parallel computing, etc. In this paper we provide a solution to a finite-horizon model predictive control (MPC) problem for MPL systems where it is required that the closed-loop input and state sequence satisfy a given set of linear inequality constraints. Although the controlled system is nonlinear, by employing results from max-plus theory, we give sufficient conditions such that the optimization problem that is performed at each step is a linear program and such that the MPC controller guarantees a priori stability and satisfaction of the constraints. We also show how one can use the results in this paper to compute a time-optimal controller for linearly constrained MPL systems.

A scenario planning approach for propositioning rescue centers for urban waterlog disasters

An urban waterlog disaster can produce severe results, such as residents’ property loss, environmental damages and pollution, and even casualties. This paper presents a system specification for urban waterlog disasters according to the analysis of urban waterlog disaster risks. Then, a two-stage stochastic mixed-integer programming model is formulated. The model minimizes the total logistics cost, and risk-induced penalties. Moreover, a deterministic counterpart of the stochastic model is proposed to study the expected value of perfect information. The multi-attribute utility theory is used to build assessment functions that assess the utility of the rescue system and the degree contributed to disaster relief for each rescue center. Finally, a real example of rescue logistics is examined for the urban waterlog disasters in Pudong District of Shanghai, China. Using the proposed model, two main results can be obtained. First, the expected value of perfect information experiment reveals that an additional ¥45,005 logistics cost and an additional ¥2417 risk-induced penalties can be incurred due to the presence of uncertainty. Second, as the weight of risk-induced penalty increases from 0.1 to 0.9, the logistics cost is increased by 41.21%, which thus contributes to a decrease of risk-induced penalty by 97.44%. Some managerial implications are discussed based on the numerical studies.     

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.      

终端滑模约束的时滞系统模型预测控制

终端约束区域和终端代价项在模型预测控制中起着关键的作用,针对输入受限的时滞系统,提出了终端滑模约束的模型预测控制．将满足输入约束的滑模面作为终端约束区域,使得终端约束区域扩大,有效缩短预测时域,减少计算量,有利于在线应用．最后通过仿真验证了所提方法的有效性．     

一种实用的磨矿多模型预测控制策略研究

房矿过程是选矿过程中的重要一环,属多输入多输出系统,并存在严重的扰动变量(诸如硬度等)和时变参数.介绍了一种磨矿控制系统中实用的多变量动态矩阵控制的多模型控制策略.该方法基于不同的矿石硬度对球磨过程建立不同的阶跃模型进行多模型预测控制.仿真证实了该方法的有效性.     

基于2维性能参考模型的2维模型预测迭代学习控制策略

将迭代学习控制(Iterative learning control, ILC)系统看作一类具有2维动态特性的控制系统,根据模型预测控制(Model predictive control, MPC)和性能参考模型控制思想, 提出了一种基于2维性能参考模型的2维模型预测迭代学习控制系统设计方案.在该控制系统设计方案中,可以通过选择适当的2 维性能参考模型来构造2 维动态变化的设定值信号和预测控制信号,从而引导迭代学习控制系统收敛到合理的控制性能,并有效避 免系统性能收敛过程中控制输入可能发生的剧烈波动.通过对控制系统的结构分析可知,所得的迭代学习控制器本质上是由沿时 间指标的参考模型预测控制器和沿周期指标的迭代学习控制器组成,闭环系统的收敛性等价于一个2维滤波系统的稳定性.数值仿 真结果证明了该设计方案的有效性和鲁棒性.     

动态输出反馈鲁棒模型预测控制离线算法

研究具有多包不确定性和有界噪声系统的动态输出反馈鲁棒模型预测控制(Robust model predictive control, RMPC)的离线方法. 先前的在线方法中, 在估计状态和估计误差集合已知的情况下, 在每一采样时刻通过近似最优算法求解控制器参数. 本文采用先前的方法计算离线控制器参数和吸引域. 首先, 选定一系列估计状态, 其中,每个估计状态对应同样一组嵌套的估计误差集合. 然后,针对每一估计状态和每一估计误差集合的组合,离线计算唯一的控制器参数和对应的吸引域. 这些控制器参数和对应的吸引域存储在表中. 如果离线确定的吸引域包含实时的扩展状态, 则该离线控制器参数是实时可行的. 在线时, 根据实时估计状态和选取实时估计误差集合, 在表中搜索包含实时扩展状态且优化性能指标最小的吸引域所对应的控制器参数. 通过连续搅拌釜式反应器控制系统验证了该方法的有效性.     

A Simultaneous Inventory Control and Facility Location Model with Stochastic Capacity Constraints

Traditionally, logistics analysts divide decisions levels into strategic, tactical and operational. Often these levels are considered separately for modeling purposes. The latter may conduce to make non-optimal decisions, since in reality there is interaction between the different levels. In this research, a cross-level model is derived to analyze decisions about inventory control and facility location, specially suited to urban settings, where the storage space is scarce and the vehicles’ capacity is usually restricted. Both conditions, on the one hand make the problem difficult to solve optimally but on the other hand make it more realistic and useful in practice. This paper presents a simultaneous nonlinear-mixed-integer model of inventory control and facility location decisions, which considers two novel capacity constraints. The first constraint states a maximum lot size for the incoming orders to each warehouse, and the second constraint is a stochastic bound to inventory capacity. This model is NP-Hard and presents nonlinear terms in the objective function and a nonlinear constraint. A heuristic solution approach is introduced, based on Lagrangian relaxation and the subgradient method. Numerical experiments were designed and applied. The solution procedure presented good performance in terms of the objective function. One of the key conclusions of the proposed modeling approach is the fact that a reduction of the inventory capacity does not necessarily imply an increase in the number of installed warehouses. In fact, reducing the order size allows the optimal allocation of customers (those with higher variances) into different warehouses, reducing the total system’s cost.     

Stochastic bounds for queueing systems with limited service schedules

Motivated by the study of cyclic service queue processor schedules and token ring local area networks, upper and lower stochastic bounds for a GI/G/1 vacation model with limited service are developed. The limited service vacation model is compared with the Bernoulli schedule vacation model. For the case of Poisson arrivals and infinitely divisible vacation durations simple, closed-form expressions are given for upper and lower bounds of the first two moments of the waiting time. Some upper and lower bounds are also derived for cyclic queues with limited service. The quality of the bounds is illustrated through numerical examples.     

基于椭圆型估计误差界的动态输出反馈鲁棒模型预测控制

研究具有多包不确定型参数和有界噪声系统的动态输出反馈鲁棒模型预测控制（Output feedback robust model predictive control,OFRMPC）的综合方法. 前期的研究表明,估计误差集合（Estimation error set,EES）的更新是输出反馈模型预测控制综合方法研究的一个关键技术. 在本文中,通过利用S-procedure,采用新的估计误差集合更新方法.通过适当地在线更新估计误差集合,可获得下一采样时刻更紧凑的估计误差集合. 通过数值仿真例子验证了该方法的有效性.     

Hybrid Fuzzy Modelling for Model Predictive Control

Model predictive control (MPC) has become an important area of research and is also an approach that has been successfully used in many industrial applications. In order to implement a MPC algorithm, a model of the process we are dealing with is needed. Due to the complex hybrid and nonlinear nature of many industrial processes, obtaining a suitable model is often a difficult task. In this paper a hybrid fuzzy modelling approach with a compact formulation is introduced. The hybrid system hierarchy is explained and the Takagi–Sugeno fuzzy formulation for the hybrid fuzzy modelling purposes is presented. An efficient method for identifying the hybrid fuzzy model is also proposed. A MPC algorithm suitable for systems with discrete inputs is treated. The benefits of the MPC algorithm employing the hybrid fuzzy model are verified on a batch-reactor simulation example: a comparison between the proposed modern intelligent (fuzzy) approach and a classic (linear) approach was made. It was established that the MPC algorithm employing the proposed hybrid fuzzy model clearly outperforms the approach where a hybrid linear model is used, which justifies the usability of the hybrid fuzzy model. The hybrid fuzzy formulation introduces a powerful model that can faithfully represent hybrid and nonlinear dynamics of systems met in industrial practice, therefore, this approach demonstrates a significant advantage for MPC resulting in a better control performance.     

Hybrid Dynamic Variables-Dependent Event-Triggered Fuzzy Model Predictive Control

This article focuses on dynamic event-triggered mechanism(DETM)-based model predictive control(MPC) for T-S fuzzy systems. A hybrid dynamic variables-dependent DETM is carefully devised, which includes a multiplicative dynamic variable and an additive dynamic variable. The addressed DETM-based fuzzy MPC issue is described as a “min-max” optimization problem(OP). To facilitate the co-design of the MPC controller and the weighting matrix of the DETM, an auxiliary OP is proposed based on a new Lyap...     

基于反馈线性化的永磁同步电机模型预测控制

提出一种基于反馈线性化和模型预测控制(MPC)策略的永磁同步电机(PMSM)控制方案.运用微分几何理论讨论了非线性PMSM模型可进行反馈线性化的充分必要条件,并将其转换为新坐标空间中的线性模型;分析了MPC原理和对系统约束条件的处理方法.针对获得的PMSM线性模型,分别采用MPC和状态反馈极点配置方法设计了控制器.在有...