基于概率隧道设计的随机预测控制研究

项目名称： 基于概率隧道设计的随机预测控制研究

项目编号： No.61304090

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 自动化技术、计算机技术

项目作者： 程奇峰

作者单位： 辽宁工程技术大学

项目金额： 26万元

中文摘要： 随机预测控制是近几年来模型预测控制在处理含有随机不确定度的系统时发展起来的理论方法，当前，它在智能楼宇温度控制、智能交通、金融工程以及网络控制系统、混杂系统等多领域都取得了广泛应用。但是，已有的随机预测控制方法远不能满足实际需求。项目针对含有乘法噪声或同时含有加法噪声的系统，首先提出三种概率隧道的设计框架，即"同形概率隧道"、"平行边界概率隧道"和"概率隧道分离构建法"，研究概率隧道演化规律，计算概率隧道大小，实现将概率约束转化成硬约束；设计与概率隧道相适应的终端约束集以确保闭环可行性和稳定性，获得在线计算量小、保守度低的控制策略。在输出反馈时，设计后验状态估计器，考虑状态估计和误差构成的增广系统，研究增广系统的概率隧道构建原理，分析求解隧道边界极值，设计出高效、稳定的控制算法。项目将为拓展随机预测控制在多领域中的应用提供理论支撑，也会推进预测控制理论在不确定线性系统和非线性系统中的发展。

中文关键词： 模型预测控制；概率约束；随机系统；交易决策优化；时间最优

英文摘要： Stochastic predictive control is a newly developed theory and strategy when model predictive control handles systems with stochastic uncertainty in the past few years. Currently, it has been applied into many fields, such as the building climate control, intelligent traffic control, financial engineering, networked control systems and hybrid systems. However, the existing theory and algorithms of stochastic predictive control cannot meet the requirements in practice. The project studies systems with multiplicative noise and/or additive noise. Firstly, it proposes the design framework of three kinds of probabilistic tubes, namely 'homothetic probabilistic tube', 'probabilistic tube with parallel edges' and 'probabilistic tube with separate construction stages'. The evolution laws of the tubes will then be analyzed and the tube scalings will be computed, thus the probabilistic constraints can be transferred into hard constraints. Terminal sets corresponding to those tubes will be designed to ensure the closed-loop feasibility and stability. Therefore, stochastic predictive control algorithms with small online computational burden and low conservatism are developed. Afterwards the output feedback case will be taken into account in an augmented form with the use of a posterior state estimator. Then the probabilistic

英文关键词： model predictive control；probabilistic constraints；stochastic systems；optimization of trade decision；time optimal