基于扰动补偿趋近律的非匹配离散广义准滑模控制

研究了非匹配不确定离散广义系统准滑模控制策略的综合问题.给出了具有前级状态向量的动态切换函数,使得系统能够在切换带内稳定.设计了两种带有扰动补偿的离散广义趋近律,消除了常规滑模控制策略中不确定项必须有界的限制,且不必满足匹配条件.给出了系统准滑动模态保持逐步穿越切换面的必要条件,减小了准滑动模态切换带的带宽.所设计的滑模控制器在有限时间内可达切换面,削弱了系统抖振,有效地改善了系统动态品质.最后,数值算例验证了该控制策略的可行性.     

一类时滞非完整系统的反馈控制问题的研究

针对一类具有时滞项的非完整系统,研究了其反馈控制器的设计问题.采用状态转换技术和反推方法,设计了不依赖于时滞的反馈控制器.同时为了处理初值为零的情况,提出了一种新颖的基于第一个子系统输出值的切换控制策略,最后通过仿真算例说明了控制器的有效性.     

基于快速输出采样反馈技术的无抖振离散滑模控制

针对一类单输入单输出的离散时间系统,提出了一种基于快速输出采样反馈技术(FOS)的无抖振滑模控制器.该控制策略避免了在控制器中采用切换模式,从而消除了切换面附近的抖振.理论分析和仿真结果表明该方法不仅能保证闭环系统的稳定性,而且能提高系统的稳态精度.     

一类随机时延网络控制系统的神经网络自适应容错控制

针对一类随机时延网络控制系统,提出一种基于RBF神经网络自适应动态补偿的容错控制策略.该方法通过在线估计时延将系统建模为随机切换系统,并在模型参考自适应方法的基础上设计RBF神经网络动态补偿容错控制器,利用Lyapunov稳定性理论给出神经网络补偿器的在线权值学习算法,以保证网络控制系统在故障情况下的跟踪性能和状态一致最终有界稳定.最后通过仿真验证了该方法的有效性.     

切换多项式非线性系统的输入-状态稳定性

利用耗散不等式研究了切换多项式非线性系统的输入-状态稳定性分析问题,在任意切换信号下,给出了使得切换多项式非线性系统输入-状态稳定的充分条件.采用平方和分解方法来寻找切换多项式非线性系统的输入-状态稳定共同Lyapunov函数.数值算例验证了所提方法的可行性.     

一类切换线性系统的慢切换镇定设计

研究一类线性切换系统的慢切换镇定设计问题.基于时间驱动和状态反馈驱动相结合的思想,在系统存在稳定凸组合的基础上介绍了非保守的切换设计.对切换线性系统设计了两种新的切换规则,分别使系统渐近稳定和指数稳定在设计的切换规则下,类李雅普诺夫函数在状态反馈期允许增加固定的比例.切换设计不仅能降低切换频率还能给出状态驱动持续时间的一个下界.最后,数例仿真验证了切换设计的有效性.     

切换系统的全局指数稳定性

提出了一种确定切换系统稳定性分析的方法.引入了两个相关的实例(非完整系统和约束摆)进行说明.用有限个模型的集合组成非线性模型,且切换序列可以是任意的.假定在切换瞬间状态不出现跳跃,并且不出现Zeno现象,即在每个有界时间段上,切换次数是有限的.在对所确定切换系统的分析中,应用了多次Liapunov函数,并证明了全局指数稳定性.系统的指数稳定性平衡关系到实际应用,因为这样的系统有着更强健的抗干扰能力.     

金融动力系统的同步脉冲控制

针对一类金融三维动力系统,提出了一种同步控制法.基于微分脉冲系统理论,设计了带状态反馈的脉冲控制策略来实现系统的同步控制.理论分析和数值模拟的结果都验证了控制器的有效性.这一控制策略简单便于实现,更可以用于一般非线性混沌系统的控制.     

切换线性系统的暂态性能优化

研究切换自治线性系统在切换镇定前提下的暂态性能优化.给出系统超调和调节时间的定义,提出基于分路径状态反馈切换的局部暂态优化的设计算法,获得暂态性能估计.     

时滞供应链网络系统的牛鞭效应切换控制方法

研究了具有时滞因素供应链网络系统中牛鞭效应的稳定化控制问题,构建了具有时变时滞的供应链库存系统模型.由于时滞的时变特性,使得系统在不同时刻表现为不同的动态,从而可将供应链库存系统建模为一类具有有限个子系统的切换系统模型.采用平均驻留时间方法,给出了一个使得供应链库存波动切换系统指数稳定的充分条件.进而,通过求解一组线性矩阵不等式,给出了订单补偿控制策略的设计方法.最后,通过仿真验证了设计的订单补偿控制策略能有效抑制供应链库存网络系统中的牛鞭效应.     

Adaptive output feedback asymptotic stabilization of nonholonomic systems with uncertainties

A global adaptive output feedback control strategy is presented for a class of nonholonomic systems in generalized chained form with drift nonlinearity and unknown virtual control parameters. The purpose is to design a nonlinear output feedback switching controller such that the closed-loop system is globally asymptotically stable. By using the input-state scaling technique and an integrator back-stepping approach, an output feedback controller is given. A filter of observer gain is introduced for state and parameter estimates. Meanwhile, in order to avoid the over-parameters, a tuning function technique is utilized. A novel switching control strategy based on the output measurement of the first subsystem rather than time is used to overcome the uncontrollability of the x₀-subsystem in the origin. The proposed controller can guarantee that all the system states globally converge to the origin, while other signals maintain bounded. The numerical simulation testifies the effectiveness.     

On output feedback control of singularly perturbed systems

We treat the problem of robustness of output feedback controllers with respect to singular perturbations. Given a singularly perturbed control system whose boundary layer system is exponentially stable and whose reduced order system is exponentially stabilizable via a (possibly dynamical) output feedback controller, we present a sufficient condition which ensures that the system obtained by applying the same controller to the original full order singularly perturbed control system is exponentially stable for sufficiently small values of the perturbation parameter. This condition, which is less restrictive than those previously given in the literature, is shown to be always satisfied when the singular perturbation is due to the presence of fast actuators and/or sensors. Furthermore, we show explicitly that, in the linear time-invariant case, if this condition is not satisfied then there exists an output feedback controller which stabilizes the reduced order system but destabilizes the full order system.     

Global asymptotical synchronization of chaotic neural networks by output feedback impulsive control: An LMI approach

In this paper, impulsive control for master–slave synchronization schemes consisting of identical chaotic neural networks is studied. Impulsive control laws are derived based on linear static output feedback. A sufficient condition for global asymptotic synchronization of master–slave chaotic neural networks via output feedback impulsive control is established, in which synchronization is proven in terms of the synchronization errors between the full state vectors. An LMI-based approach for designing linear static output feedback impulsive control laws to globally asymptotically synchronize chaotic neural networks is discussed. With the help of LMI solvers, linear output feedback impulsive controllers can be easily obtained along with the bounds of the impulsive intervals for global asymptotic synchronization. The method is finally illustrated by numerical simulations.     

Output feedback guaranteed cost control of uncertain linear discrete systems with interval time-varying delays

This paper deals with output feedback guaranteed cost control problem for a general class of uncertain linear discrete delay systems, where the state and the observation output are subjected to interval time-varying delay. The proposed output feedback controller uses the observation measurement to exponentially stabilize the closed-loop system and guarantee an adequate level of system performance. By constructing a set of augmented Lyapunov–Krasovskii functionals, a delay-dependent condition for the robust output feedback guaranteed cost control is established in terms of linear matrix inequalities (LMIs). Three numerical examples are provided to demonstrate the efficiency of the proposed method.     

Semi-global finite-time output feedback stabilization for a class of large-scale uncertain nonlinear systems

This paper addresses the problem of semi-global finite-time decentralized output feedback control for large-scale systems with both higher-order and lower-order terms. A new design scheme is developed by coupling the finite-time output feedback stabilization method with the homogeneous domination approach. Specifically, we first design a homogeneous observer and an output feedback control law for each nominal subsystem without the nonlinearities. Then, based on the homogeneous domination approach, we relax the linear growth condition to a polynomial one and construct decentralized controllers to render the nonlinear system semi-globally finite-time stable.     

Output Feedback H∞ Control for a Class of Nonlinear Stochastic Systems

This paper deals with the output feedback H∞ control problem for a class of nonlinear stochastic systems. Based on the latest developed theory of stochastic dissipation, a notable result about the nonlinear H∞ output feedback control of deterministic system is generalized to the stochastic case. Finally, in the cases of state feedback and output feedback, two families of controllers are provided respectively.     

Adaptive Predictor-Based Output Feedback Control for a Class of Unknown MIMO Linear Systems

In this paper, the problem of characterizing adaptive output feedback control laws for a general class of unknown MIMO linear systems is considered. Specifically, the presented control approach relies on three components, i.e., a predictor, a reference model and a controller. The predictor is designed to predict the system’s output with arbitrary accuracy, for any admissible control input. Subsequently, a full state feedback control law is designed to control the predictor output to approach the reference system, while the reference system tracks the desired trajectory. Ultimately, the control objective of driving the actual system output to track the desired trajectories is achieved by showing that the system output, the predictor output and the reference system trajectories all converge to each other.     

New Algorithm for Computing LQ Suboptimal Output Feedback Gains of Decentralized Control Systems

In this paper, the problem of computing the suboptimal output feedback gains of decentralized control systems is investigated. First, the problem is formulated. Then, the gradient matrices based on the index function are derived and a new algorithm is established based on some nice properties. This algorithm shows that a suboptimal gain can be computed by solving several ordinary differential equations (ODEs). In order to find an initial condition for the ODEs, an algorithm for finding a stabilizing output feedback gain is exploited, and the convergence of this algorithm is discussed. Finally, an example is given to illustrate the proposed algorithm.     

Optimal output feedback without trace

For a linear system (C,A,B) with integral quadratic cost, an optimal control problem is presented which has as its solution an output feedback control. The output feedback chosen ensures that the closed-loop cost is not worse than the open-loop cost for any initial condition, which is not guaranteed by the standard optimization method for finding output feedback (optimization with respect to the feedback matrix of an average over initial conditions of the closed-loop cost). The most severe restriction involved is thatker[C]? R[B]. Finite- and infinite-time cases are discussed.