新型增益调度变结构控制器的性能比较研究

为了提高变结构控制系统的控制效率,从节省控制能耗、消除颤振、快速进入滑动模态、获取稳定滑动模态等基本要求出发,将变结构控制器设计成具有增益调度控制器参数的增益调度变结构控制器,并提出了增益调度变结构控制器的设计方法.通过选择不同的调度变量、调度变量函数以及调度变量的变化范围,给出6种不同的增益调度变结构控制器,并进行了仿真研究,以比较其控制性能.     

新型增益调度变结构控制器的性能比较研究

为了提高变结构控制系统的控制效率,从节省控制能耗,消除颤振,快速进入滑动模态,获取稳定滑动模态等基本要求出发,将变结构控制器设计成具有增益调度控制器参数的增益调度变结构控制器,并提出了增益调度变结构控制器的设计方法.通过选择不同的调度变量,调度变量函数以及调度变量的变化范围,给出6种不同的增益调度变结构控制器,并进行了仿真研究,以比较其控制性能.

     

磁悬浮储能飞轮线性变参数鲁棒增益调度控制

针对强陀螺效应的磁悬浮储能飞轮转速快变引起的模型变化而带来的控制问题,设计了线性变参数增益调度鲁棒控制器.根据飞轮的线性变参数模型,设计的鲁棒增益调度控制器,能够保证其全转速范围内的鲁棒稳定性和性能.为降低控制器设计的保守性,在设计控制器时,可缩小转速区间,使控制性能得到提高.与按照非时变模型设计的鲁棒控制器相比,线性变参数鲁棒增益调度控制器可以实现以转速为参数的自适应调节,在全转速范围内,其鲁棒稳定性和性能均具有显著优势.仿真结果验证了此控制器的有效性和先进性.     

风力机的线性变参数主动容错控制

针对风力机具有非线性和参数的不确定性的特征,提出了基于线性变参数(linear parameter varying,LPV)增益调度的风力机主动容错控制方法,降低故障对机组动态特性的影响.基于LPV凸分解方法,将风力机的非线性模型转化为具有凸多面体结构LPV模型,利用线性矩阵不等式(linear matrix inequalities,LMIs)技术对凸多面体各个顶点分别设计满足性能要求的控制器,再利用各顶点设计的反馈控制器得到具有凸多面体结构LPV容错控制器.仿真结果表明,LPV增益调度技术可以成功地应用于风力机系统的容错控制.     

基于回路成形的鲁棒增益调度控制器设计

针对目前基于线性变参数系统的增益调度控制设计中存在的控制结构复杂性问题，提出一种基于回路成形的简单且易实现的增益调度控制结构.在此基础上，提出一个鲁棒增益调度控制设计方法.设计过程首先采用补偿器函数使得被控对象奇异值具有期望的形状，以保证被控对象的性能要求，然后利用小增益定理设计一个鲁棒控制器，得到具有良好性能的、结构简单的鲁棒增益调度控制器.最后针对一个化工过程，说明此方法的有效性.     

电磁轴承增益调度多目标控制器设计

为了减弱转子质量不平衡带来的干扰,提出一种多目标(H2/H∞控制和极点配置)性能要求的增益调度电磁轴承控制器.电磁轴承这类线性参数变化系统(LPV)可认为是参数多面体,在多面体各顶点处采用线性矩阵不等式组(LMIs)和凸优化技术,设计满足闭环极点配置和H2/H∞性能的控制器参数,然后将这些控制器参数综合为一个增益调度控制器.仿真结果表明,利用该方法设计的控制器可使电磁轴承随转速变化始终具有良好的控制性能.     

基于国际评价基准的溶解氧控制方法研究

针对活性污泥污水处理过程溶解氧浓度(DO)控制的非线性特性，以DO为控制对象、国际评价基准（benchmark）为平台，将PID控制、增益调度控制和一般模型控制(GMC)方法应用于DO控制中，并进行了控制器性能的比较．仿真结果表明，所提一般模型非线性控制方法的性能优于其他两种控制方法，体现出该方法的有效性和优越性．     

一种新型的滑模变结构增益调度控制器设计方法

研究影响一般滑动模态变结构控制性能的因素,并给出了根据切换函数选择滑动模态系数、边界层厚度以及控制器增益系数的一般要求．针对一类含参数不确定性的非线性系统,采用新型增益调度变结构控制策略进行控制,以切换函数作为调度变量对滑动模态系数、边界层厚度以及控制器增益系数进行调度,以提高滑动模态变结构控制系统的控制性能,抑制颤振,降低控制能耗．仿真算例验证了所提出控制策略的有效性．     

中立时滞LPV系统基于观测器的控制器设计

研究一类中立时滞线性参数变化系统的基于观测器的控制问题．呆用Lyapunov方法，提出了系统的时滞相关稳定性条件，设计了增益调度控制器和状态观测器．利用参数线性矩阵不等式，将控制器存在的充分条件转化为凸优化问题．最后通过数值仿真验证了所提出方法的可行性．     

直接多模型自适应控制器设计——LMI方法

本文将线性矩阵不等式(LMI)方法引入直接多模型自适应控制, 将直接多模型控制器的 设计过程转化为求解线性矩阵不等式的可行解问题,同时给出在不同不确定参数范围内的多 个状态反馈控制器,并由此构成直接多模型自适应控制器．同时将直接多模型自适应控制推 广到多输入多输出被控对象的设定值跟踪问题,并给出稳定性分析结果．     

基于干扰观测器的污水处理过程控制研究

溶解氧浓度控制是污水处理过程的核心环节，恰当的溶解氧控制能改进过程性能，而且可以节约能源。但是由于入水水质水量的时变性、非线性和延迟等动态特性，使得基于常规PID的控制效果很不理想，常引起出水水质波动大、能耗高的问题。针对入水水质水量的扰动不确定性设计了基于干扰观测器的PID控制方法，仿真结果表明，此种控制方案鲁棒性强，能有效克服入水干扰的不确定性，具有实际应用前景。     

On stability and regulation performance for flexible-joint robots with input/output communication delays

Networked control of robotic systems is widely recognized as a potentially transformative technological enabler for several applications. However, the issues of time delays in communication and recovery from data losses have emerged as the pivotal issues that have stymied practical deployment. The study for control of robotic system with input/output communication delays has attracted many researchers’ attention, but the existing results have been primarily developed for rigid-joint robots. Since joint flexibility is largely unavoidable in practical manipulators, in this paper the set-point control problem for flexible-joint robots with input/output communication delays is studied. It is demonstrated that the scattering variables address the stability problem for unknown constant delays, however, in contrast to the rigid-robot case, they cannot guarantee set-point regulation. In addition, we compute the explicit dependence of the regulation errors on the communication delays, control gains, and the desired set-point configuration. Without exact knowledge of time delays, a scattering variable based controller with position feedback is subsequently studied in this paper to guarantee stability with improved regulation performance. The control architecture is further extended to the case with time-varying delays. Simulation results are presented to validate the efficacy of the proposed control algorithms.     

Online set-point optimisation cooperating with predictive control of a yeast fermentation process: A neural network approach

Online set-point optimisation which cooperates with model predictive control (MPC) and its application to a yeast fermentation process are described. A computationally efficient multilayer control system structure with adaptive steady-state target optimisation (ASSTO) and a suboptimal MPC algorithm are presented in which two neural models of the process are used. For set-point optimisation, a steady-state neural model is linearised online and the set-point is calculated from a linear programming problem. For MPC, a dynamic neural model is linearised online and the control policy is calculated from a quadratic programming problem. In consequence of linearisation of neural models, the necessity of online nonlinear optimisation is eliminated. Results obtained in the proposed structure are comparable with those achieved in a computationally demanding structure with nonlinear optimisation used for set-point optimisation and MPC.     

Estimation and control of an α-olefin polymerization reactor

The research addressed in this paper is focused on the estimation and control of a nonlinear industrial polymerization process. The majority of the available estimation techniques are not easy to implement on-line due to the intensive computations involved. Therefore, there is a need for an estimation technique that is simple, yet capable of handling multi-rate sampled data with variable measurement dead times. For the polymerization system under study, one such estimator is presented. Estimation is implemented within a nonlinear model predictive control algorithm (NLMPC). The combined estimator-controller is shown to display good set-point tracking and disturbance rejection properties via simulations involving some practical control problems.     

污水处理过程溶解氧浓度的自抗扰控制

溶解氧是活性污泥法处理污水的一个关键变量,它关系到污水中有机物的生物降解、微生物生长和出水水质.多数污水处理过程选择溶解氧为被控量.然而,溶解氧浓度受进水流量,进水组分、浓度波动等诸多因素影响,较难控制.本文根据曝气量变化确定溶解氧浓度设定值,以污水进水变化率为控制量,设计线性自抗扰控制实现对溶解氧浓度的跟踪,进而获得对污水出水底物浓度的间接控制.设计两组仿真实验,分别模拟进水底物浓度固定和变化时,线性自抗扰控制对溶解氧浓度和出水底物浓度的控制;同时,设计仿真实验验证线性自抗扰控制对总扰动的估计和补偿效果.仿真结果表明,线性自抗扰控制可获得良好的溶解氧浓度跟踪和出水底物浓度控制效果;在进水水质波动时,线性自抗扰控制亦具有很强的干扰补偿能力,可保证出水水质.     

Survey of gain-scheduling analysis and design

The gain-scheduling approach is perhaps one of the most popular non-linear control design approaches which has been widely and successfully applied in fields ranging from aerospace to process control. Despite the wide application of gainscheduling controllers and a diverse academic literature relating to gain-scheduling extending back nearly thirty years, there is a notable lack of a formal review of the literature. Moreover, whilst much of the classical gain-scheduling theory originates from the 1960s, there has recently been a considerable increase in interest in gain-scheduling in the literature with many new results obtained. An extended review of the gain-scheduling literature therefore seems both timely and appropriate. The scope of this paper includes the main theoretical results and design procedures relating to continuous gain-scheduling (in the sense of decomposition of non-linear design into linear sub-problems) control with the aim of providing both a critical overview and a useful entry point into the relevant literature.     

Advanced control and on-line process optimization in multilayer structures

The paper demonstrates the place, role and mutual interaction of advanced control algorithms and on-line set-point optimization in process control structures. First, a multilayer control structure resulting from a functional decomposition is briefly presented. The role and selected realizations of advanced control algorithms, in particular mostly applied now model predictive control (MPC) ones, at direct control and supervisory constraint control layers is discussed. Then possible solutions to on-line set-point optimization, depending of disturbance dynamics, are presented: dynamic set-point optimization including involved structures based on temporal decomposition, and steady-state set-point optimization for cases with disturbance dynamics both much slower than and comparable with the process dynamics. For the last case, important in industrial practice, different structures of interaction and even integration of MPC and steady-state optimization are discussed. The topics are illustrated by briefly presented examples, selected from given references.