基于多面体方法的时变约束系统控制算法

基于多面体方法的约束系统控制算法能有效地处理存在状态量和控制量凸约束的定常系统的控制问题,采用多面体方法对时变约束系统的稳定控制方法进行了研究．针对时变约束系统的模型参数是变化的特点,提出了一种约束系统的动态控制算法．仿真结果表明,该算法能保证约束时变系统的稳定性．     

控制方向未知的全状态约束非线性系统的鲁棒自适应跟踪控制

针对一类控制方向未知的含有时变不确定参数和未知时变有界扰动的全状态约束非线性系统,本文提出了一种基于障碍Lyapunov函数的反步自适应控制方法.障碍Lyapunov函数保证了系统状态在运行过程中始终保持在约束区间内;Nussbaum型函数的引入解决了系统控制方向未知的问题;光滑投影算法确保了不确定时变参数的有界性.障碍Lyapunov函数、Nussbaum型函数及光滑投影算法与反步自适应方法的有效结合首次解决了控制方向未知的全状态约束非线性系统的跟踪控制问题.所设计的自适应鲁棒控制器能在满足状态约束的前提下确保闭环系统的所有信号有界.通过恰当地选取设计参数,系统的跟踪误差将收敛于0的任意小的邻域内.仿真结果表明了控制方案的可行性.     

一种基于H∞理论的鲁棒预测控制方法

融合H∞控制的鲁棒概念和预测控制的滚动优化原理,提出了一种全新的约束动态对策预测控制方法.对有状态和控制约束的不确定线性系统,证明了闭环系统的鲁棒稳定性并给出了鲁棒性条件.该方法同时具有H∞控制和预测控制的优点:鲁棒性和显式处理约束的能力.     

基于屏障控制函数的轮式机器人系统多目标分布式协同控制

受多目标优化理论的启发,针对非完整约束轮式机器人设计基于屏障控制函数的多目标协同控制算法.该方法可实现队形控制主目标、连通性次级目标以及避碰次级目标,其中将连通性保持和避碰问题建模为两个系统约束,屏障控制函数作为约束对应的惩罚函数,可解决系统有输入或状态约束的问题.通过获取的局部信息将系统状态约束转化为屏障控制函数,利用屏障控制函数的类李雅普诺夫特性对其导数引入约束,再通过保证约束集的正不变性,达到控制目标.所提出方法可有效地避免控制器在连通性约束和避碰约束边界处的频繁切换,减小机械疲劳,在理论上可进一步扩展次级目标的数目,实现多目标控制.另外,所提出的协同控制算法对编队队形没有特殊要求,适用于不同编队需求和通信拓扑情况.最后通过数值仿真验证了所提出算法在不同情况下的有效性.     

带输入饱和的欠驱动吊车非线性信息融合控制

针对欠驱动吊车系统的控制问题,提出了一种非线性信息融合控制方法. 通过融合二次型性能指标函数中包含的未来参考轨迹和控制能量的软约束信息,以及吊车系统状态方程和输出方程的硬约束信息,获得协状态和控制量的最优估计. 针对控制量输入饱和的问题,提出了一种控制能量软约束信息自适应调节算法,使求出的控制量满足限制要求. 信息融合控制方法基于被控对象的离散模型设计,具有易于实现的特点. 仿真结果表明了该方法的有效性.     

线性连续随机系统的容错约束方差控制设计

线性连续随机系统的容错约束方差控制设计王子栋,郭治（南京理工大学自动控制系南京２１００９４）关键词：线性连续随机系统,约束方差控制,容错控制,完整性１引言及问题描述在随机控制问题中,系统的性能指标常常直接表现为系统稳态状态方差的上界形式．起源于八十年...     

具有状态参数约束的鲁棒自适应最优控制设计

在传统Backstepping 鲁棒自适应设计中, 虚拟控制函数的设计参数缺乏约束, 对此, 采用新的设计方法得到了新坐标系下的系统模型和参数替换律, 结合直接反馈线性化和最优控制策略, 最终给出了具有参数约束的非线性鲁棒自适应最优控制的设计方法. 以具体励磁系统参数为例进行仿真实验研究, 所得结果表明, 该鲁棒自适应控制方法可以实现状态参数的最优约束, 并能有效控制状态参数的收敛速度.     

离散随机系统的容错约束方差控制设计

本文考虑线性离散随机系统的容错约束方差控制设计问题，即设计反馈控制器，使闭环系统在可能的传感器失效不仅保持渐近稳定，而且满足预先给定的稳态方差约束，文中导出期望了容约束方差控制器存在的充分条件，并进一步给出了其参数化代数表达式。     

基于模糊性能指标的广义预测控制器参数调整

针对广义预测控制算法在控制时域中求得的M个控制量，利用模糊模拟技术对系统的约束进行检验，不断修正目标函数中控制量的加权系数，充分利用系统预测控制量的信息，增强系统的鲁棒性，并满足系统的约束。     

具有状态参数约束的鲁棒自适应最优控制设计

在传统Backstepping鲁棒自适应设计中,虚拟控制函数的设计参数缺乏约束,对此,采用新的设计方法得到了新坐标系下的系统模型和参数替换律,结合直接反馈线性化和最优控制策略,最终给出了具有参数约束的非线性鲁棒自适应最优控制的设计方法.以具体励磁系统参数为例进行仿真实验研究,所得结果表明,该鲁棒自适应控制方法可以实现状态参数的最优约束,并能有效控制状态参数的收敛速度.     

Constrained robust sampled‐data control for nonlinear uncertain systems

In industrial process control, computer control, which makes the closed‐loop system a sampled‐data one containing both continuous‐ and discrete‐time signals, is widely used. In contrast with traditional approximation methods, sampled‐data synthesis, a direct digital controller design procedure without approximation, has received increasing attention during the past few years. However, many of the existing results cannot be applied to sampled‐data control design for the uncertain systems. In this paper, a result of robust asymptotic stability of sampled‐data systems with constraints on the state is presented based on a result on practical stability for these systems. Then the robust sampled‐data control for a class of uncertain nonlinear systems with constraints on the output is developed. The problem is formulated from vehicle steering control with constraint on the side slip angle of body. The result is described by some matrix inequalities which could be solved by an iterative algorithm based on the linear matrix inequality technique. Finally, a numerical example is presented to demonstrate the result. Copyright © 2002 John Wiley & Sons, Ltd.     

非线性系统的鲁棒采样控制

研究在汽车转向控制中遇到的、具有输出约束的一类非线性不确定系统的鲁棒采样控制和鲁棒采样最优控制问题,结果表示为一些矩阵不等式.最后基于线性矩阵不等式,给出了一个迭代算法和算例.数值计算实例证明了该算法的有效性.     

多采样率数字控制系统综述

本文给出了多采样率数字控制系统的发展和目前研究情况的综述．根据系统中各采样周期之间的关系，多采样率控制系统可以分为输入多采样率控制系统输出多采样率控制系统和广义多采样率控制系统等．它们又分别对应于采用广义保持器、广义采样器和周期时变控制器的数字控制系统．文中对这三类系统的特点和近期研究成果进行了系统的介绍与讨论．由于“因果条件”， 多采样率控制器具有结构约束．本文综述了几类满足这一结构约束的多采样率控制器的设计方法和各类多采样率控制系统．     

引用广义保持器的采样系统--结构性质与自适应鲁棒控制

研究一类线性时变不确定系统的采样控制.分析了采用的广义保持器和离散化系统的结构性质,提出了考虑微处理器主频和字长的自适应鲁棒采样控制方案,证明了闭环系统的稳定性.针对倒摆系统进行了计算机仿真研究.     

Co-states for sliding mode design in linear systems

A control design for linear systems with uncertainties is presented where a particular time-varying sliding mode manifold is specified utilizing co-states of the plant. We introduce design methods for both continuous time and sampled data systems. It is shown that robustness of the closed-loop systems with continuous time control degrades when an equivalent sampled data control is used.     

Robust event‐triggered model predictive control for cyber‐physical systems under denial‐of‐service attacks

This paper investigates the resilient control problem for constrained continuous‐time cyber‐physical systems subject to bounded disturbances and denial‐of‐service (DoS) attacks. A sampled‐data robust model predictive control law with a packet‐based transmission scheduling is taken advantage to compensate for the loss of the control data during the intermittent DoS intervals, and an event‐triggered control strategy is designed to save communication and computation resources. The robust constraint satisfaction and the stability of the closed‐loop system under DoS attacks are proved. In contrast to the existing studies that guarantee the system under DoS attacks is input‐to‐state stable, the predicted input error caused by the system constraints can be dealt with by the input‐to‐state practical stability framework. Finally, a simulation example is performed to verify the feasibility and efficiency of the proposed strategy.     

Stability condition for sampled data based control of linear continuous switched systems

Most practical systems are continuous in nature but with discrete (sampled) feedback when digital control is utilized. This paper investigates the stabilization problem of switched linear continuous-time systems with sampled data based control. For both known and unknown arbitrary switching processes, on the basis of Lyapunov stability theory, a sufficient global exponential stability condition related to Dwell time and sampling period is established. For the latter case, on-line one-step adaptive estimation algorithm is derived and integrated with sampled feedback for control design. Validation and verification of the established result are conducted through cruise control of train systems.     

不确定时滞线性系统的采样控制

采样控制作为一种数字控制的直接设计方法,近年来引起了广泛的重视,目前采样控制不易处理模型的不确定性和时滞,本语文给不确定时滞系统的采样控制算法,结果表示为一些矩阵不等式,并给中用ＬＭＩ求解的迭代算法。     

Power-constrained intermittent control

In this article, input power, as opposed to the usual input amplitude, constraints are introduced in the context of intermittent control. They are shown to result in a combination of quadratic optimisation and quadratic constraints. The main motivation for considering input power constraints is its similarity with semi-active control. Such methods are commonly used to provide damping in mechanical systems and structures. It is shown that semi-active control can be re-expressed and generalised as control with power constraints and can thus be implemented as power-constrained intermittent control. The method is illustrated using simulations of resonant mechanical systems and the constrained nature of the power flow is represented using power-phase-plane plots. We believe the approach we present will be useful for the control design of both semi-active and low-power vibration suppression systems.     

Global decentralized sampled‐data output feedback stabilization for a class of large‐scale nonlinear systems with sensor and actuator failures

In this paper, we investigate global decentralized sampled‐data output feedback stabilization problem for a class of large‐scale nonlinear systems with time‐varying sensor and actuator failures. The considered systems include unknown time‐varying control coefficients and inherently nonlinear terms. Firstly, coordinate transformations are introduced with suitable scaling gains. Next, a reduced‐order observer is designed to estimate unmeasured states. Then, a decentralized sampled‐data fault‐tolerant control scheme is developed with an allowable sampling period. By constructing an appropriate Lyapunov function, it can be shown that all states of the resulting closed‐loop system are globally uniformly ultimately bounded. Finally, the validity of the proposed control approach is verified by using two examples.