量子力学系统的输出反馈控制

研究量子系统输出反馈控制问题建模以及控制律设计问题.首先讨论了量子力学VonNeumann测量原理与连续测量模型的一致性;然后在连续测量模型的基础上总结了已有量子反馈模型的结果,归纳出量子输出反馈控制系统模型;最后针对单比特振幅退相干抑制问题,利用线性直接输出反馈控制设计反馈控制律,指出利用最优控制的方法设计线性输出反馈控制的比例系数,可以得到较好的结果.     

不确定时延输出反馈网络化系统保性能控制

研究了一类具有不确定时延的动态输出反馈网络控制系统的保性能控制问题．针对小于等于一个采样周期的不确定时延,利用Lyapunov理论和线性矩阵不等式方法,推导出了动态输出反馈网络控制系统保性能控制律存在的条件,给出了动态输出反馈保性能控制律设计方法．利用LMI工具箱中的目标函数最小化工具,可以求得该类系统优化的保性能控制律参数和优化的性能指标,从而获得该系统的保性能控制律．给出的仿真算例说明了设计方法是有效的所得结果是可行的．     

一类非线性系统的输出反馈容错控制

针对一类非线性系统,研究了故障情况下基于输出反馈的容错控制问题．首先基于Zhang的自适应观测器和Lin的输出反馈控制律设计了正常系统的标称控制律,分析了该控制律的性能,并得到了故障发生后仍然采用该控制律时闭环系统状态有界的充分条件．在此基础上,设计了故障系统的容错控制律,证明了若发生的故障满足前述的充分条件,则故障系统在该容错控制律下稳定．数值仿真表明了该方法的有效性．     

不确定离散时间系统的H_2/H_∞最优保性能控制

针对具有两个不同被调输出的一类不确定离散时间系统 ,研究其 H2 / H∞ 状态反馈保性能控制问题。基于线性矩阵不等式处理方法 ,推导出存在 H2 / H∞ 保性能控制律的充分必要条件 ,并用一个线性矩阵不等式的可行解给出了所有保性能控制律的参数化表示。进而通过建立和求解一个凸优化问题 ,给出了 H2 / H∞ 最优保性能控制律设计方法。最后通过一个例子说明了所提出方法的有效性     

不确定离散时间系统的H2/ H∞最优保性能控制

针对具有两个不同被调输出的一类不确定离散时间系统,研究其H2/H∞状态反馈保性能控制问题,基于线性矩阵不等式处理方法。推导出存在H2/H∞保性能控制律的充分必要条件,并用一个线性矩阵不等式的可行解给出了所有保性能控制律的参数化表示,进而通过建立和求解一个凸优化问题,给出了H2/H∞最优保性能控制律设计方法,最后通过一个例子说明了所提出方法的有效性。     

不确定广义系统的输出反馈鲁棒预测控制

针对一类具有范数有界不确定性的广义系统,当系统状态不可测时,提出了一种基于输出反馈的鲁棒预测控制器综合算法．采用ＬＭＩ方法以及变量变换思想,将无限时域“最小最大”优化问题转化为线性规划问题．确定出一组分段连续的输出反馈控制序列,给出了输出反馈控制律存在的充分条件,证明了优化问题在初始时刻的可行解可以保证广义闭环系统是渐近稳定且正则无脉冲的．仿真实例验证了所提出方法的有效性．     

考虑执行机构故障的离散系统可靠保性能控制设计

对一类离散时间线性时不变系统,研究当执行机构发生连续增益故障情况下的可靠保性能状态反馈控制律的设计问题.应用线性矩阵不等式处理方法,导出系统存在可靠保性能控制律的一个充分条件,进而用一个线性矩阵不等式的可行解给出所有可靠保性能控制律的一个参数化表示.在此基础上,通过建立和求解由一组线性矩阵不等式所表示的凸优化问题,得到最优可靠保性能控制律的设计方法.     

Takagi-Sugeno模糊连续系统的模糊采样控制

研究了T-S模糊连续系统的模糊采样控制问题．利用广义系统的描述方法、Lyapunov-Krasovikii泛函以及线性矩阵不等式(LMI)方法,建立了LMIs形式的依赖于采样时间间隔的模糊采样镇定条件,同时给出了模糊采样控制律的设计方法．所设计的模糊采样控制律可以镇定T-S模糊系统．而且,当连续时间模糊控制律可以镇定T-S模糊系统时,对于足够小的采样时间间隔,带有同样增益矩阵的模糊采样控制律也可以镇定T-S模糊系统．最后,通过两个仿真实例说明了所给方法的有效性．     

受扰奇异摄动时滞组合大系统的近似最优控制

研究一类受扰奇异摄动时滞组合大系统的近似最优控制问题．基于奇异摄动的快慢分解理论．将原组合大系统的最优控制问题分解为组合线性快优化子问题和降阶的受扰时滞组合慢优化子问题．通过采用前馈补偿方法抑制外部扰动，采用参数摄动法求解组合慢优化子问题，得到了系统的前馈反馈组合（FFCC）控制律．通过引入降维扰动观测器解决了FFCC律的物理可实现问题．仿真算例验证了所提出方法的有效性．     

Markov跳变系统的输出反馈鲁棒预测控制

对离散时间Markov跳变系统, 当系统状态不完全可测时, 研究了一类基于输出反馈的鲁棒模型预测控制问题. 所研究系统为准线性参数时变的, 考虑在当前时刻系统的时变参数是已知的, 将来时刻未知的情况. 综合考虑系统存在多胞不确定性和有界噪声等因素, 通过运用线性矩阵不等式方法及变量变换思想, 将无穷时域性能指标的最小最大鲁棒预测控制问题转化为具有线性矩阵不等式约束的凸优化问题, 得到了系统的输出反馈控制律. 引入二次有界概念, 在满足输入输出约束的情况下, 保证闭环系统的随机稳定性. 数值算例验证了方法的有效性.     

Output feedback receding horizon control of constrained systems

This paper considers output feedback control of linear discrete-time systems with convex state and input constraints which are subject to bounded state disturbances and output measurement errors. We show that the non-convex problem of finding a constraint admissible affine output feedback policy over a finite horizon, to be used in conjunction with a fixed linear state observer, can be converted to an equivalent convex problem. When used in the design of a time-varying robust receding horizon control law, we derive conditions under which the resulting closed-loop system is guaranteed to satisfy the system constraints for all time, given an initial state estimate and bound on the state estimation error. When the state estimation error bound matches the minimal robust positively invariant (mRPI) set for the system error dynamics, we show that this control law is time-invariant, but its calculation generally requires solution of an infinite-dimensional optimization problem. Finally, using an invariant outer approximation to the mRPI error set, we develop a time-invariant control law that can be computed by solving a finite-dimensional tractable optimization problem at each time step that guarantees that the closed-loop system satisfies the constraints for all time.     

时变时滞不确定系统的鲁棒输出反馈控制

研究了时变时滞不确定系统基于状态观测器的动态输出反馈实现鲁棒镇定的分 析和综合问题.所研究的系统不仅同时包含时变状态时滞和时变控制时滞,而且包含时变未 知且有界不确定参数.提出了确保该系统可通过输出反馈鲁棒镇定的充分条件,并将该充分 条件转化为线性矩阵不等式(LMI)问题,最终通过求解两个LMI来构造输出反馈控制律.     

Event‐triggered probabilistic robust control of linear systems with input constrains: By scenario optimization approach

This paper addresses the problem of probabilistic robust stabilization for uncertain systems subject to input saturation. A new probabilistic solution framework for robust control analysis and synthesis problems is addressed by a scenario optimization approach, in which the uncertainties are not assumed to be norm bounded. Furthermore, by expressing the saturated linear feedback law on a convex hull of a group of auxiliary linear feedback laws, we establish conditions under which the closed‐loop system is probabilistic stable. Based on these conditions, the problem of designing the state feedback gains for achieving the largest size of the domain of attraction is formulated and solved as a constrained optimization problem with linear matrix inequality constraints. The results are then illustrated by a numerical example.     

Global exponential stabilization of a class of nonlinear systems by output feedback

This work extends the existing output feedback stabilization schemes for the systems in a "perturbed chain-of-integrator" form. In particular, we further relax the triangular-type conditions imposed on the perturbed terms and analyze the robust property of the linear output feedback control law using the newly proposed condition.     

A new OFRMPC formulation with on‐line synthesis of the dynamic output feedback controller

This paper proposes a new approach for the design of output feedback robust model predictive control (OFRMPC) with a dynamic output feedback controller (DOFC) for linear uncertain systems subject to input and output constraints. The main contribution of this work is the full on‐line synthesis of the DOFC as part of a convex optimization problem, with constraint satisfaction and asymptotic stability guarantees. A numerical example is employed to illustrate the advantage of the proposed control law, as compared with another OFRMPC strategy with partial DOFC synthesis. The present paper also points out an inconsistency in the mathematical development of a previous related OFRMPC formulation (‘improved dynamic output feedback RMPC for linear uncertain systems with input constraints’). Copyright © 2017 John Wiley & Sons, Ltd.     

Exponential dissipativeness of the random-structure diffusion processes and problems of robust stabilization

Consideration was given to the class of systems described by a finite set of the controllable control-affine diffusion Ito processes with stepwise transitions defined by the evolution of the uniform Markov chain (Markov switchings). For these systems, the notion of exponential dissipativity was introduced, and its theory was developed and used to estimate the possible variations of the output feedback law under which the system retains its robust stability. For the set of linear systems with uncertain parameters, proposed was a two-step procedure for determination of the output feedback control based on comparison with the stochastic model and providing their simultaneous robust stabilization. At the first step, the robust stabilizing control is established by means of an iterative algorithm. Then, the possible variations of the feedback law for which the robust stability is retained are estimated by solving a system of matrix linear inequalities. An example was presented.     

Robust output regulation problem for linear time-delay systems

In this paper, we study the robust output regulation problem for linear systems with input time-delay. By extending the internal model design method to linear time-delay systems, we have established solvability conditions for the problem by both dynamic state feedback control and dynamic output feedback control. The advantages of internal model approach over the feedforward design approach are that it can handle perturbations of the uncertain parameters in the plant and the control law, and it does not need to solve the regulator equations.     

线性离散时滞系统的鲁棒耗散控制

考虑线性离散时滞系统的二次型耗散控制问题.对于确定系统,给出渐近稳定且严格二次型耗散的条件和动态输出反馈控制器使闭环系统渐近稳定且严格二次型耗散.对于不确定系统,考虑不确定性具有耗散特性的情形,讨论鲁棒耗散性分析和动态输出反馈鲁棒耗散控制问题.通过构造增广系统,将不确定系统的鲁棒严格二次型耗散分析和设计转化为确定系统的情况.所得结果为离散时滞系统的无源控制和H∞控制提供了统一框架,且为离散时滞系统的分析和设计提供了一种更灵活、保守性更小的方法.