约束非线性系统多变量最优控制研究

近年来，非线性规划算法在最优控制领域中正受到越来越多的关注。该文深人研究并实现了一种新的非线性规划算法——FSQP算法，该算法具有所有迭代点均处于可行域之内、收敛速度较快的特点。提出了一种基于FSQP算法的约束非线性系统最优控制方法。然后，运用该方法解决了带有约束的复杂非线性系统的多变量时间最优控制问题，并通过计算机仿真表明了该控制算法的可行性和良好的控制效果。     

一类分段线性混杂系统的最优控制策略研究

针对连续模态驻留的时延是确定性的分段线性混杂系统最优控制问题，首先给出该类混杂系统模型，采用一种新方法，即混合动态规划方法来研究混杂系统的最优控制，然后利用Lyapunov方法证明采用这种控制策略时混杂系统的稳定性，最后通过一个数值例子来说明所提出方法的有效性。     

非齐次马尔可夫跳跃正线性系统的稳定与镇定

本文研究一类非齐次马尔可夫跳跃正线性系统的稳定与镇定问题.该系统中模态的变化服从非齐次马尔可夫过程,其模态转移速率/概率矩阵是随时间随机变化的,且变化规律由一个高层马尔可夫过程描述,本文提出一种双层马尔可夫跳跃正系统模型来刻画此类系统特征.在此基础上,利用切换线性余正李雅普诺夫函数给出此类连续和离散时间非齐次马尔可夫跳跃正线性系统平均稳定的判据.然后,运用线性规划方法设计依赖于模态–模态转移速率/概率矩阵的状态反馈控制器,进而实现闭环系统的平均稳定性.最后,以功率分配系统为例给出仿真算例,验证了所设计控制策略的有效性.     

基于自适应动态规划的导弹制导律研究综述

自适应动态规划（Adaptive dynamic programming,ADP）作为最优控制领域的近似优化方法,是求解复杂非线性系统最优控制问题的有力工具.近年来,已成为控制理论与计算智能领域的研究热点.本文着重介绍ADP算法的理论研究进展及其在航空航天领域的应用.分析了几种典型的制导律优化设计方法,以及ADP方法在导弹制导律设计中的应用现状和前景.     

不确定离散半马尔可夫跳变系统的有限时间鲁棒$H_infty$控制

研究一类不确定离散时间半马尔可夫跳变系统的有限时间鲁棒H_∞控制问题.首先,应用半马尔可夫核方法建模离散时间系统的半马尔可夫跳变过程,其中驻留时间的概率密度函数依赖系统当前模态和下一模态,使得所提出理论可考虑不同的驻留时间概率分布类型,针对随机跳变系统有限时间分析中的跳变次数问题,提出估计跳变次数最大值的方法;然后,考虑系统模态驻留时间的上下界,提出新的有限时间有界概念,并保证基于半马尔可夫核方法给出的不确定离散时间半马尔可夫随机跳变系统有限时间有界判据数值可解;接着,在此基础上,分析系统的H_∞性能指标,设计模态依赖的状态反馈控制律保证闭环系统的有限时间鲁棒H_∞性能;最后,通过2个算例仿真验证所提出理论的可行性和有效性.     

具有随机丢包的一类网络控制系统的故障检测

针对存在随机数据包丢失的网络环境,研究了一类网络控制系统的故障检测问题.考虑随机丢包同时发生在传感器与控制器以及控制器与执行器之间,将网络控制系统建模为含有4个模态的马尔可夫跳变线性系统.基于此类模型,构造了系统的残差发生器,相应的故障检测问题转化为H∞滤波问题.利用马尔可夫跳变线性系统理论,设计了故障检测滤波器,使得...     

基于自适应动态规划的一类带有时滞的离散时间非线性系统的最优控制策略

针对一类状态和控制变量均带有时滞的非线性系统的带有二次性能指标函数最优控制问题, 本文提出了一种基于新的迭代自适应动态规划算法的最优控制方案. 通过引进时滞矩阵函数, 应用动态规划理论, 本文获得了最优控制的显式表达式, 然后通过自适应评判技术获得最优控制量. 本文给出了收敛性证明以保证性能指标函数收敛到最优. 为了实现所提出的算法, 本文采用神经网络近似性能指标函数、计算最优控制策略、求解时滞矩阵函数、以及给非线性系统建模. 最后本文给出了两个仿真例子说明所提出的最优策略的有效性.     

基于混合逻辑动态的混杂系统研究及应用

综述了一种在工业领域中使用的混杂系统建模方法，即基于混合逻辑动态MLD（Mixed Logic Dynamic）的建模方法。分析了在该建模框架下系统的可观性、可控性、形式验证、稳定性、状态估计及故障检测、最优控制和预测控制，并且以水电厂为例讨论了该方法在实际工程中的应用。基于MLD的混杂系统研究目前仍处于起步阶段，在理论上混杂系统预测控制、最优控制、多目标控制及混合整数二次规划求解都有待研究，在应用上如何根据不同问题有效地建模混杂系统也有待研究。     

计算机病毒的最优控制模型*

提出一个带有最优控制的SAIC模型,让感染病毒的节点数目和系统消耗保持在较低的水平。运用最优控制理论的相关原理和方法,证明了最优控制的存在性,并给出了刻画最优控制的最优系统。数值仿真结果表明,使用适当的控制策略后,计算机病毒的传播得到了有效的控制。所提的方法有望成为一种有用的工具来控制计算机病毒的传播。     

基于马尔可夫模型的多agent自适应在线验证

用于领域中业务关键型的自适应系统(self-adaptive system,SAS)需要遵从严格的质量要求,在多agent自适应系统运行过程中需要根据动态环境与需求实现自适应调整。针对上述问题,提出了一种基于马尔可夫的多agent自适应在线验证方法。首先将多agent系统中环境影响因素转换为概率形式,然后将系统形式化为马尔可夫模型,最后通过模型检查的方式进行在线验证,使系统通过验证结果对自身进行调控。通过现实的智能无人停车场案例进行了实验,论述了方法的使用流程以及验证效果。实验结果表明,该方法适用于具有多agent的自适应系统,并能够在系统出现故障情况下及时进行调控,相比原始系统的稳定性有了较大的改善。     

Stochastic stability and robust stabilization of semi‐Markov jump linear systems

The semi‐Markov jump linear system (S‐MJLS) is more general than the Markov jump linear system (MJLS) in modeling some practical systems. Unlike the constant transition rates in the MJLS, the transition rates of the S‐MJLS are time varying. This paper focuses on the robust stochastic stability condition and the robust control design problem for the S‐MJLS with norm‐bounded uncertainties. The infinitesimal generator for the constructed Lyapunov function is first derived. Numerically solvable sufficient conditions for the stochastic stability of S‐MJLSs are then established in terms of linear matrix inequalities. To reduce the conservativeness of the stability conditions, we propose to incorporate the upper and lower bounds of the transition rate and meanwhile apply a new partition scheme. The robust state feedback controller is accordingly developed. Simulation studies and comparisons demonstrate the effectiveness and advantages of the proposed methods. Copyright © 2012 John Wiley & Sons, Ltd.     

Stochastic model predictive control for constrained discrete-time Markovian switching systems

In this paper we study constrained stochastic optimal control problems for Markovian switching systems, an extension of Markovian jump linear systems (MJLS), where the subsystems are allowed to be nonlinear. We develop appropriate notions of invariance and stability for such systems and provide terminal conditions for stochastic model predictive control (SMPC) that guarantee mean-square stability and robust constraint fulfillment of the Markovian switching system in closed-loop with the SMPC law under very weak assumptions. In the special but important case of constrained MJLS we present an algorithm for computing explicitly the SMPC control law off-line, that combines dynamic programming with parametric piecewise quadratic optimization.     

Delay-dependent H ∞ stabilisation criterion for continuous-time networked control systems with random delays

This article mainly addresses the H _∞ stabilisation problem for a class of networked control systems (NCSs) with random input delays in the continuous-time domain. A Markov jump linear system (MJLS) model with two jump parameters is developed which takes both the sensor-to-controller and controller-to-actuator delays into account. Based on such a MJLS, a new Lyapunov–Krasovskii functional is proposed to establish a delay-dependent H _∞ stabilisation criterion in terms of linear matrix inequalities (LMIs), and a mode-dependent state-feedback controller of NCSs can be determined. Two numerical examples are included to illustrate the derived result.     

Fault detection for networked control systems subject to quantisation and packet dropout

This article addresses the stochastic fault detection (SFD) problem in finite-frequency domain for a class of networked control systems (NCSs) with respect to signal quantisation and data packet dropout. Considering a logarithmic quantiser and Markovian packet dropout, the NCS is modelled as a Markov jump linear system (MJLS) with quantisation error. Further, a new definition of finite-frequency stochastic H _? index is given, which gives a measurement of sensitivity. Subsequently, sufficient conditions are derived to guarantee that the MJLS can achieve such a performance. By virtue of the obtained conditions, the fault detection filters (FDFs) are designed in finite-frequency domain, which are valid in characterising the disturbance attenuation performance and finite-frequency fault sensitivity performance. Finally, a simulation example is given to illustrate the method and its effectiveness.     

一类离散时间非齐次马尔可夫跳跃系统最优控制

本文研究了一类离散时间非齐次马尔可夫跳跃线性系统的线型二次高斯(linear quadratic Gaussian,LQG)问题,其中系统模态转移概率矩阵随时间随机变化,其变化特性由一高阶马尔可夫链描述.对于该系统的LQG问题,文中首先给出了线性最优滤波器,得到最优状态估计;其次,验证分离定理成立,并利用利用动态规划方法设计了系统最优控制器;最后,数值仿真结果验证了所设计控制器的有效性.     

Optimal filtering for networked systems with Markovian communication delays

This paper is concerned with optimal filter problems for networked systems with random transmission delays, while the delay process is modeled as a multi-state Markov chain. By defining a delay-free observation sequence, the optimal filter problems are transformed into ones of the Markov jumping parameter system. We first present an optimal Kalman filter, which is with time-varying, path-dependent filter gains, and the number of the paths grows exponentially in time delay. Thus an alternative optimal Markov jump linear filter is presented, in which the filter gains just depend on the present value of the Markov chain. Further, an optimal filter with constant-gains is developed, the existence condition for the stabilizing solutions to the filter is given, and it can be shown that the proposed Markov jump linear filter converges to the constant-gain filter under appropriate assumptions.     

Model reduction of discrete Markovian jump systems with time‐weighted H2 performance

This paper is concerned with the optimal time‐weighted H₂ model reduction problem for discrete Markovian jump linear systems (MJLSs). The purpose is to find a mean square stable MJLS of lower order such that the time‐weighted H₂ norm of the corresponding error system is minimized for a given mean square stable discrete MJLSs. The notation of time‐weighted H₂ norm of discrete MJLS is defined for the first time, and then a computational formula of this norm is given, which requires the solution of two sets of recursive discrete Markovian jump Lyapunov‐type linear matrix equations. Based on the time‐weighted H₂ norm formula, we propose a gradient flow method to solve the optimal time‐weighted H₂ model reduction problem. A necessary condition for minimality is derived, which generalizes the standard result for systems when Markov jumps and the time‐weighting term do not appear. Finally, numerical examples are used to illustrate the effectiveness of the proposed approach. Copyright © 2015 John Wiley & Sons, Ltd.     

Maximal versus strong solution to algebraic Riccati equations arising in infinite Markov jump linear systems

We deal with a perturbed algebraic Riccati equation in an infinite dimensional Banach space which appears, for instance, in the optimal control problem for infinite Markov jump linear systems (from now on iMJLS). Infinite or finite here has to do with the state space of the Markov chain being infinite countable or finite (see, e.g., [M.D. Fragoso, J. Baczynski, Optimal control for continuous time LQ—problems with infinite Markov jump parameters, SIAM J. Control Optim. 40(1) (2001) 270–297]). By using a certain concept of stochastic stability (a sort of L₂-stability), we have proved in [J. Baczynski, M.D. Fragoso, Maximal solution to algebraic Riccati equations linked to infinite Markov jump linear systems, Internal Report LNCC, no. 6, 2006] existence (and uniqueness) of maximal solution for this class of equations. As it is noticed in this paper, unlike the finite case (including the linear case), we cannot guarantee anymore that maximal solution is a strong solution in this setting. Via a discussion on the main mathematical hindrance behind this issue, we devise some mild conditions for this implication to hold. Specifically, our main result here is that, under stochastic stability, along with a condition related with convergence in the infinite dimensional scenario, and another one related to spectrum—weaker than spectral continuity—we ensure the maximal solution to be also a strong solution. These conditions hold trivially in the finite case, allowing us to recover the result of strong solution of [C.E. de Souza, M.D. Fragoso, On the existence of maximal solution for generalized algebraic Riccati equations arising in stochastic control, Systems Control Lett. 14 (1990) 233–239] set for MJLS. The issue of whether the convergence condition is restrictive or not is brought to light and, together with some counterexamples, unveil further differences between the finite and the infinite countable case.     

H ∞ model reduction for discrete-time Markov jump linear systems with partially known transition probabilities

In this article, the H _∞ model reduction problem for a class of discrete-time Markov jump linear systems (MJLS) with partially known transition probabilities is investigated. The proposed systems are more general, relaxing the traditional assumption in Markov jump systems that all the transition probabilities must be completely known. A reduced-order model is constructed and the LMI-based sufficient conditions of its existence are derived such that the corresponding model error system is internally stochastically stable and has a guaranteed H _∞ performance index. A numerical example is given to illustrate the effectiveness and potential of the developed theoretical results.     

Filtering of discrete‐time Markov jump linear systems with uncertain transition probabilities

This article addresses the filtering design problem for discrete‐time Markov jump linear systems (MJLS) under the assumption that the transition probabilities are not completely known. We present the methods to determine ??₂‐ and ??_∞‐norm bounded filters for MJLS whose transition probability matrices have uncertainties in a convex polytope and establish an equivalence with the ones with partly unknown elements. The proposed design, based on linear matrix inequalities, allows different assumptions on Markov mode availability to the filter and on system parameter uncertainties to be taken into account. Under mode‐dependent assumption and internal model knowledge, observer‐based filters can be obtained and it is shown theoretically that our method outperforms some available ones in the literature to date. Numerical examples illustrate this claim. Copyright © 2010 John Wiley & Sons, Ltd.