变风量空调系统的预测控制应用研究

变风量空调具有非线性和大时滞等特点,通过建立静压点压力与流量变化的关系确立模型,结合预测控制优化参数的控制策略,系统在流量变化的冲击下达到压力恒定和控制的快速响应,从而改善空调系统的性能和达到节能的目的。系统利用相关的参数进行了仿真研究,结果表明该控制策略有助于解决静压点压力在流量变化较大时能相对保持恒定。     

基于状态观测器的约束鲁棒模型预测控制

模型不确定情况下的鲁棒问题是模型预测控制的一个根本问题。本文采用线性矩阵不等式(LMI)，研究多模型不确定性描述情况下的鲁棒模型预测控制问题。在输入输出约束条件下，最小化最坏情况下的无穷时域目标函数，获得保证系统稳定的基于状态观测器的状态反馈增益并且给出观测器增益的设计方法。实例说明算法可行且保证闭环系统渐近稳定。     

饱和约束系统的鲁棒模型预测控制

针对饱和约束系统提出了一种鲁棒模型预测控制算法,分别考虑了多面体不确定性和结构反馈不确定性.考虑无穷时域的最坏二次性能指标,通过采用带有饱和特性的反馈控制结构,将控制律的求解转化为一个在线的线性矩阵不等式优化问题.初始时刻优化问题的可行性保证了闭环控制系统的鲁棒稳定性.最后的仿真结果说明了算法的优越性.     

基于LMI的多模型鲁棒预测控制

用线性矩阵不等式 (LMI)方法研究多模型鲁棒预测控制, 提出了状态反馈的综合方法, 并分析了闭环系统的可行性, 同时证明闭环系统渐近稳定. 在此基础上, 研究了带终端零状态的有限优化时域预测控制和无穷优化时域预测控制的性能, 证明了两者在性能上的一致性.     

变风量空调系统的分布式预测控制

基于预测控制策略的多输入多输出（MIMO）控制器可以解决单通道变风量系统的控制问题,但在输入输出变量较多时,计算就变得非常复杂。在分析系统工作机理和系统动力学行为的基础上,建立了内部模型,在此基础上采用分布式预测控制技术将系统分解成四个子系统,通过设定参考轨迹、输入输出约束及加权矩阵,解决了系统凸二次型的全局优化和纳什问题。通过仿真研究验证了系统控制的效果。     

互联电力系统鲁棒分布式模型预测负荷频率控制

负荷频率控制是现代互联电力系统运行的重要保障.本文针对含有不确定因素和负荷扰动的多区域互联电力系统提出了一种基于线性矩阵不等式参数可调节的鲁棒分布式预测控制算法.设计各个区域控制器目标函数引入相邻区域的状态变量和输入变量,同时考虑发电机变化速率约束和阀门位置约束,将求解一组凸优化问题转化成线性矩阵不等式求解,得到各个区域的控制律,在线性矩阵不等式中引入一组可调参数,将优化一个上限值转化成优化吸引区,降低算法的保守性.仿真结果验证了该算法在负荷扰动、系统参数不确定和结构不确定性情况下具有鲁棒性.     

区间时滞相关离散非线性系统的鲁棒模型预测控制

针对一类带有扰动、输入约束和凸多面体不确定性的区间时滞离散非线性系统, 提出一种鲁棒模型预测控制方法. 一方面, 利用min-max 模型预测控制求解鲁棒模型预测控制器, 以研究鲁棒预测控制在范数有界意义下的扰动抑制问题; 另一方面, 充分利用时滞的上下界信息构造Lyapunov 函数以得到控制器存在的充分条件. 最后给出了闭环系统鲁棒稳定性证明.     

一类不确定模糊模型的输出反馈鲁棒预测控制

针对一类满足扇形界条件的不确定模糊模型描述的非线性系统,提出一种输出反馈鲁棒预测控制方法．该方法将鲁棒预测控制的Min-Max优化问题转化为具有LMI约束的线性目标最小化问题,并且不需系统状态完全可测,仅仅利用系统测量输出和不可测状态的界限值来确定保证闭环系统鲁棒稳定的输出反馈控制器．仿真实验证明了该方法的有效性．     

基于多椭圆集序列的鲁棒模型预测优化控制

在工业过程的模型预测控制中,离线算法和在线算法是基于线性矩阵不等式的鲁棒模型预测算法的两个部分,离线得到的椭圆集序列是在线算法的基础.为了得到合适的控制规律,使系统的响应快速稳定,离线时根据状态变量的每个一维子空间得到相应的多个椭圆集序列.在线时,每个采样周期根据当前测量的状态变量值,在多个椭圆集序列中选择一个合适椭圆集序列,确定状态变量位于其中的两个椭圆集之间,并用优化的方式精确定位状态变量的位置,并得到系统控制量,使在线优化得到了证明.通过和传统算法的仿真比较,验证了所提出算法对系统的响应更迅速.     

Robust variable horizon model predictive control for vehicle maneuvering

This paper provides a new method of robust maneuver control with guaranteed finite‐time arrival and satisfaction of constraints, despite the action of an unknown but bounded disturbance. The new method extends the constraint tightening approach to robust model predictive control of constrained linear systems by combining it with a variable horizon. This relaxes the requirement for the target to be an invariant set, which is assumed by many stabilizing MPC formulations but can be restrictive in vehicle maneuvering applications. The target sets for vehicle maneuvers are typically determined by the mission requirements and are not generally invariant sets. The new controller guarantees finite‐time arrival within an arbitrary target set, i.e. not necessarily invariant, and is therefore applicable when the target is predetermined by other factors. Several simulation examples are presented including spacecraft rendezvous control with sensor visibility constraints and UAV guidance through obstacle fields. Copyright © 2006 John Wiley & Sons, Ltd.     

变风量空调的自适应解耦控制研究

变风量空调是一个多输入多输出的相互耦合的系统,由于实际系统受多种因素的影响,系统模型参数也会发生变化。针对系统的耦合性和时变性,提出一种基于在线辨识的自适应解耦控制策略并将其应用在变风量空调系统中。该控制策略采用具有遗忘因子的递推方法和基于零频率模型匹配的联合参数辨识算法,在控制系统闭环运行条件下,对系统的过程参数进行在线辨识,以此模型来设计控制器和解耦器,对相关控制回路进行解耦控制。对实际系统中两个耦合的回路进行仿真分析和实验验证,结果表明该方法有效改善了系统的耦合作用,提高了控制系统的稳定性和鲁棒性。     

基于风机模型不确定性的鲁棒控制

通过对变风量空调系统中的定静压控制、变静压控制和总风量控制方法的分析,结合空调末端的特点,选择定静压控制方法控制风管静压.由于设备老化、非线性和外部因素,风机模型具有不确定性,对静压的稳定控制造成不利影响.对静压控制采用基于线性矩阵不等式的PID控制和预测控制算法,并与普通PID控制作比较.仿真表明,采用这2种算法的定静压控制稳定性好,具有较强的鲁棒性.     

Distributed control for a multi-evaporator air conditioning system

An autonomous hierarchical distributed control (AHDC) strategy is proposed for a building multi-evaporator air conditioning (ME A/C) system in this paper. The objectives are to minimize peak demand and energy costs, and to reduce communication resources, computational complexity and conservativeness while maintaining both thermal comfort and indoor air quality (IAQ) in acceptable ranges. The building consists of multiple connected rooms and zones. The proposed control strategy consists of two layers. The upper layer is an open loop optimizer, which only collects local measurement information and solves a distributed steady state resource allocation problem to autonomously and adaptively generate reference points, for low layer controllers. This is achieved by optimizing the demand and energy costs of a multi-zone building ME A/C system under a time-of-use (TOU) rate structure, while meeting the requirements of each zone’s thermal comfort and IAQ within comfortable ranges. The lower layer also uses local information to track the trajectory references, which are calculated by the upper layer, via a distributed model predictive control (DMPC) algorithm. The control strategy is distributed at both layers because they use only local information from the working zone and its neighbors. Simulation results are provided to illustrate the advantages of the designed control schemes.     

Efficient algorithms for online tracking of set points in robust model predictive control

This paper presents some computationally efficient algorithms for online tracking of set points in robust model predictive control context subject to state and input constraints. The nonlinear systems are represented by a linear model along with an additive nonlinear term which is locally Lipschitz. As an unstructured uncertainty, this term is replaced in the robust stability constraint by its Lipschitz coefficient. A scheduled control technique is employed to transfer the system to desired set points, given online, by designing local robust model predictive controllers. This scheme includes estimating the regions of feasibility and stability of the related equilibriums and online switching among the local controllers. The proposed optimisation problems for calculating the regions of feasibility and stability are defined as linear matrix inequalities that can be solved in polynomial time. The effectiveness of the proposed algorithms is illustrated by an example.     

基于状态观测器的多面体不确定随机广义系统鲁棒预测控制

针对It?o型多面体不确定随机广义系统,提出一种离线观测器型鲁棒预测控制器的综合方法.通过构造带有误差项的增广随机Lyapunov函数,运用多维It?o公式和LMI方法,将“min-max”随机规划问题等价转化为一组线性矩阵不等式的求解问题;给出了控制器存在的充分条件和参数表达式,证明了初始时刻的可行解可以保证闭环广义系统的随机容许性.仿真算例验证了该方法的有效性.     

Latent variable iterative learning model predictive control for multivariable control of batch processes

A latent variable iterative learning model predictive control (LV-ILMPC) method is presented for trajectory tracking in batch processes. Different from the iterative learning model predictive control (ILMPC) model built from the original variable space, LV-ILMPC develops a latent variable model based on dynamic partial least squares (DyPLS) to capture the dominant features of each batch. In each latent variable space, we use a state–space model to describe the dynamic characteristics of the internal model, and an LV-ILMPC controller is designed. Each LV-ILMPC controller tracks the set points of the current batch projection in the corresponding latent variable space, and the optimal control law is determined and the persistent process disturbances is rejected along both time and batch horizons. The proposed LV-ILMPC formulation is based on general LV-MPC and incorporates an iterative learning function into LV-MPC. In addition, the real physical input that drives the process can be reconstructed from the latent variable space. Therefore, this algorithm is particularly suitable for multiple-input, multiple-output (MIMO) systems with strong coupling and serious collinearity. Three studies are used to illustrate the effectiveness of the proposed LV-ILMPC .     

基于解析法的变风量空调解耦优化控制

为了解决变风量空调中变量众多、耦合严重、延迟时间大、易出现不确定摄动等问题,提出了一种基于解析法的PID解耦控制策略。通过建立送风温度与室内温度两支耦合回路模型,并构建合适的期望闭环传函阵,推导出解耦控制器矩阵的理想表述形式。利用劳斯降阶与一阶帕德近似将其转换成PID形式,通过改变唯一的调节变量并根据谱半径幅频曲线来满足稳定性与鲁棒稳定性等要求,从而获得各参数的具体数值。结果表明,相比于前馈补偿PID法,基于解析法的解耦策略不仅能够完全消除两支回路间的耦合效应,还具有更平稳的跟踪性能,受不确定摄动的影响更小。