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.     

Robust stabilization of stochastic systems based on the LQ cont roller

The robust exponential stability in mean square for a class of linear stochastic uncertain control systems is dealt with. For the uncertain stochastic systems ,we have designed an optimal controller which guarantees the exponential stability of the system. Actually ,we employed Lyapunov function approach and the stochastic algebraic Riccati equation (SARE) to have shown the robustness of the linear quadratic (LQ) optimal control law. And the algebraic criteria for the exponential stability on the linear stochastic uncertain closed- loop systems are given.     

随机时滞Lurie系统的鲁棒H∞和L2-L∞指数控制

针对一类具有范数有界不确定性和时变时滞的It^o型随机Lurie系统, 研究了鲁棒H_∞和L₂–L_∞指数控制问题. 利用Lyapunov-Krasovskii泛函方法和It^o微分公式, 得到了以线性矩阵不等式(LMIs)表示的控制器存在的充分条件. 对所有容许的参数不确定性, 设计的无记忆状态反馈控制器使闭环系统鲁棒指数均方稳定, 且具有给定的H_∞和L₂–L_∞干扰抑制度. 最后, 通过两个仿真例子说明了所提方法的有效性.     

Robust stabilization of stochastic systems based on the LQ controller

The robust exponential stability in mean square for a class of linear stochastic uncertain control systems is dealt with. For the uncertain stochastic systems, we have designed an optimal controller which guarantees the exponential stability of the system. Actually, we employed Lyapunov fimction approach and the stochastic algebraic Riccati equation (SARE) to have shown the robusmess of the linear quadratic(LQ) optimal control law.And the algebraic criteria for the exponential stability on the linear stochastic uncertain closed-loop systems are given.     

不确定多重时滞随机中立系统鲁棒H∞控制

针对一类不确定多重时滞随机中立系统,研究了鲁棒H_∞控制设计问题．该随机中立系统的状态项、控制项、微分项、外部干扰输入项均含有时滞,系统中的不确定性满足广义匹配条件．首先,利用随机Lyapunov稳定性理论和Ito微分法则,推导出系统的随机鲁棒可镇定的充分条件．在此基础上,进一步给出了鲁棒H_∞控制器存在的充分条件．本文的研究结果以线性矩阵不等式的形式给出,仿真结果表明了此控制器设计方法的有效性．     

Robust resilient control for parametric strict feedback systems with prescribed output and virtual tracking errors

This paper investigates the robust resilient control problem for a class of parametric strict feedback nonlinear systems with prescribed output and virtual tracking errors performance. The resilience is governed by a continuously nonlinear control gains function, which endows the virtual and actual controllers with self‐adjusting abilities with respect to transformed error surfaces. The proposed control scheme is adaptation, estimation, and approximation‐free in the presence of unknown parameters and nonlinearities, and only a number of control gains, which is equal to the relative degree of the considered plants, need to be selected in applications. It is proved by rigorous analysis that the output tracking errors are confined in predefined prescribed performance functions under some nonrestrictive initial conditions, and the bounds of states are obtained characterized as control gains and systemic function‐associated constants. Finally, comparative illustrative examples are given to demonstrate the effectiveness of the proposed control scheme.     

Robust performance for both fixed and worst‐case inputs

We consider the problem of robust performance analysis when some of the exogenous inputs acting on the system are assumed to be fixed and known, while others are unknown but bounded. In particular, we consider the case where performance is measured by the ?_∞ norm of the output signals, and the uncertainty on the nominal model is described by LTV perturbations of bounded ?_∞‐induced norm. We first address the special case when all the exogenous inputs are fixed and known. We propose upper and lower bounds for the measure of robust performance. Two upper bounds are derived, which trade off accuracy versus computational expense. Both conditions are much less conservative than what one would obtain from assuming a worst‐case exogenous input. We then generalize the conditions to the more general case, where both fixed and worst‐case inputs act on the system. All these conditions are readily computable, and yield much less conservative results than one would obtain from applying standard worst‐case analysis methods. Copyright © 2005 John Wiley & Sons, Ltd.     

鲁棒辨识问题评述

本文综述了一类新的辨识问题－鲁棒辨识问题的研究进展，首先介绍了鲁棒辨识问题产生的背景，然后对各种不同的辨识方法进行了评述，并指出了各自的特点，最后总结了今后的研究方向。     

Robust reliable control of uncertain nonlinear stochastic systems subject to actuator fault

This paper addresses the issue of robust reliable stabilization for a class of uncertain nonlinear stochastic systems with both discrete and distributed time-varying delays and possible occurrence of actuator faults. By constructing a new Lyapunov functional and using linear matrix inequality technique, a new set of sufficient conditions is established for the stochastic stability of the uncertain nonlinear stochastic systems. Then, sufficient conditions are obtained for the solvability of the robust stabilization problem via robust reliable controller. More precisely, the derived control law guarantees the robust stabilization of nonlinear stochastic systems in the presence of known actuator failure matrix and uncertainties. Further, the results are extended to study the stabilization of stochastic systems with unknown actuator failure matrix. Moreover, the obtained criteria are formulated in terms of LMIs and also the reliable controller can be designed in terms of the solutions to certain linear matrix inequalities. Finally, numerical examples with simulation result are presented to demonstrate the validity and less conservatism of the obtained results.     

学习理论与鲁棒控制

首先简要介绍了ＰＡＣ（ｐｒｏｂａｂｌｙ ａｐｐｒｏｘｉｍａｔｅｌｙ ｃｏｒｒｅｃｔ）学习理论的基本原理,分析了ＰＡＣ学习理论在处理鲁棒控制中某些问题时的可行性和有效性,阐述了目前主要的研究结果,指出了存在的主要困难及未来的研究方向,另外也简要介绍了其在系统辩识方面的应用情况。     

基于混沌多项式的指令鲁棒优化及在飞行控制中的应用

本文提出一种新的方法对随机系统进行运动预测和控制指令设计,该方法可以充分利用已知信息设计控制指令以提高闭环随机系统的鲁棒性.首先采用混沌多项式对随机信息进行数学表述,并利用Galerkin投影法将随机变量的混沌多项式引入常微分方程中.然后,将随机变量的均值和方差考虑至优化问题的成本函数中,并利用伪谱法对控制指令进行鲁棒优化.最后,将该方法应用于飞行器的动力学预测以及控制指令设计.仿真结果表明,该方法能够预测飞行器飞行过程中不确定性的演化,其精度与蒙特卡罗方法相当,并且计算效率更高.此外,获得的控制指令对存在不确定参数或初始条件的随机系统具有强鲁棒性.     

随机状态空间系统的梯度优化辨识

提出了随机状态空间系统参数的梯度优化辨识方法。通过极小化输出预报误差而获得系统的参数估计。提出了动态选择雅可比矩阵奇异值比率确定参数搜索方向的方法,用以解决因雅可比矩阵的线性相关性引起的算法失效问题。给出了融合参数局部逼近性能信息的辨识算法,并得到了算法收敛速度的解析表达式。数值仿真实验的结果说明了所提出方法的有效性。     

一种基于随机平均的最优时滞控制方法

基于时滞系统的随机平均法与随机动态规划原理,提出一种非线性系统的随机最优时滞控制方法．先应用时滞随机平均法,将非线性系统的随机最优时滞控制问题变换成非时滞的最优控制问题;再根据随机动态规划原理,建立其动态规划方程;由此确定最优时滞控制律;最后,通过一个例子说明该时滞控制方法的控制效果．     

基于鲁棒控制的期权套期保值策略

在标的资产价格服从带有随机方差几何布朗运动的非完全市场假设条件下,应用随机微分对策方法,研究与标的资产有关的欧式期权的动态套期保值策略问题。建立了最优动态套期保值策略的随机微分对策数学模型,给出了基于鲁棒控制的均方复制误差最小的自融资动态套期保值策略。当方差为时间的确定性函数时,最优动态套期保值策略与用Black-Scholes套期比表示的delta套期保值策略是一致的。     

基于坡度的鲁棒优化方法

提出一种基于坡度的鲁棒性评价指标, 通过三阶反距离平方权差分算法计算坡度, 以坡度指标来反映函数的变化趋势和平滑程度, 从而设计了一种求解鲁棒优化问题的多目标进化方法, 仿真实验证明其有效性。实验还表明该方法在解的分布性与区分度上更有优势。     

Robust fault diagnosis for non-Gaussian stochastic systems based on the rational square-root approximation model

The task of robust fault detection and diagnosis of stochastic distribution control （SDC） systems with uncertainties is to use the measured input and the system output PDFs to still obtain possible faults information of the system. Using the rational square-root B-spline model to represent the dynamics between the output PDF and the input, in this paper, a robust nonlinear adaptive observer-based fault diagnosis algorithm is presented to diagnose the fault in the dynamic part of such systems with model uncertainties. When certain conditions are satisfied, the weight vector of the rational square-root B-spline model proves to be bounded. Conver- gency analysis is performed for the error dynamic system raised from robust fault detection and fault diagnosis phase. Computer simulations are given to demon- strate the effectiveness of the proposed algorithm.     

基于广义双曲正切模型的机器人鲁棒模糊自适应控制

利用广义模糊双曲正切模型的全局逼近特点,设计一种模糊自适应控制器用于机器人轨迹跟踪控制.广义双曲正切模型利用输入变量的平移能以任意精度逼近系统的不确定动态.对于系统不确定外界干扰和模糊系统的逼近误差,通过求解一个线性矩阵不等式来保证闭环系统的鲁棒稳定性.对比传统的模糊基函数,在保证系统跟踪精度的前提下,双曲正切模糊基函数的自适应调整参数大大减少,仿真表明该控制算法具有较强的鲁棒性能和较好的跟踪性能.     

基于形状记忆合金驱动器的微纳定位系统鲁棒自适应控制

针对基于智能材料驱动器串联驱动的微纳定位系统,本文主要探讨了此类高精定位系统的控制设计策略.其控制设计的主要任务是消除驱动器中未知回滞特性对系统性能所造成的负面影响.本文重点以形状记忆合金驱动器为例,采用基于广义play算子的广义Prandtl-Ishlinskii回滞模型来表征形状记忆合金驱动器中的未知饱和回滞非线性,并在此基础上提出了一种鲁棒自适应控制设计方法来消除前置回滞存在的影响.设计的控制器在保证全局稳定性的基础上能实现理想的跟踪精度,仿真结果验证了控制策略的有效性和正确性.     

具概率分布变时滞随机系统鲁棒稳定性

本文研究了一类有非线性时变随机时滞的线性不确定系统的鲁棒稳定性．其中时变随机时滞表征为伯努利随机过程,具有已知的概率分布和变化范围．通过构造新泛函,建立了基于线性矩阵不等式的鲁棒均方指数稳定的充分条件,此条件易于用MATLABH2具箱来验证．本文所获得结果的主要特征是稳定性条件依赖时滞的概率分布和时滞导数的上界．同时也证明了允许时变随机时滞的时滞比之传统的确定性时滞有更大的变化范围,因此我们的条件比确定性时滞更为保守．算例表明了文中所提方法的有效性．     

基于Volterra泛函级数的非线性系统的鲁棒辨识

针对弱非线性系统的鲁棒建模问题, 基于Volterra泛函级数, 结合集员辨识理论, 提出了广义频率响应函数的鲁棒辨识方法, 形成了一套较完整的弱非线性系统的鲁棒建模方法, 仿真结果表明该方法是行之有效的.