一类MIMO继电反馈系统极限环的全局稳定性

研究了分散继电反馈系统极限环的全局稳定性问题.主要思想是将所研究的极限环的全局稳定性问题转化为不确定离散系统的渐近稳定性问题.并给出了判定稳定性的充分条件.结果表明,在某些约束条件下,所有系统轨迹最终进入并停留在某一特定区域,而且趋于极限环.数值例子仿真验证了提出的方法的有效性.     

倒立摆系统中强化学习的极限环问题

倒立摆系统是强化学习的一种重要的应用领域。首先分析指出在倒立摆系统中,常用的强化学习算法存在着极限环问题,算法无法正确收敛、控制策略不稳定。但是由于在简单的一级倒立摆系统中算法的控制策略不稳定的现象还不明显,因此极限环问题常常被忽视。针对强化学习算法中极限环问题,提出基于动作连续性准则的强化学习算法。算法采用修正强化信号和改进探索策略的方法克服极限环对倒立摆系统的影响。将提出的算法用于二级倒立摆的实际系统控制中,实验结果证明算法不仅能成功控制倒立摆,而且可以保持控制策略的稳定。     

Limit Cycles in a Passive Compass Gait Biped and Passivity-Mimicking Control Laws

It is well-known that a suitably designed unpowered mechanical bipedrobot can walk down an inclined plane with a steady periodicgait. The energy required to maintain the motion comes from theconversion of the biped's gravitational potential energy as itdescends. Investigation of such passive natural motions maypotentially lead us to strategies useful for controlling activewalking machines as well as to understand human locomotion.In this paper we demonstrate the existence and the stability ofsymmetric and asymmetric passive gaits using a simple nonlinear bipedmodel. Kinematically the robot is identical to a double pendulum(similar to the Acrobot and the Pendubot) and is able to walk withthe so-called compass gait. Using the passivebehavior as a reference we also investigate the performance ofseveral active control schemes. Active control can enlarge the basinof attraction of passive limit cycles and can create new gaits.     

面向自动化装配的一类智能混杂系统

针对混杂系统理论和方法尚不能处理不确定性以及意外事件的发生等问题,将已有的智能理论和混杂系统理论进行有机结合,是一项有效的解决途径。在分析自动化装配中连续和离散变量组成混杂系统基础上,提出了集成装配计划和调度的一类智能混杂系统,给出了智能决策接口以及和智能技术的互连,分析了基于模糊神经网络和粗糙集的智能解决策略,使一类混杂系统具有智能决策功能。通过对一自动化装配实例建立极大代数模型和神经网络的仿真实验,验证了系统分析方法的可行性和有效性。     

Optimal Control of Switching Surfaces in Hybrid Dynamical Systems

This paper concerns an optimal control problem defined on a class of switched-mode hybrid dynamical systems. The system's mode is changed (switched) whenever the state variable crosses a certain surface in the state space, henceforth called a switching surface. These switching surfaces are parameterized by finite-dimensional vectors called the switching parameters. The optimal control problem is to minimize a cost functional, defined on the state trajectory, as a function of the switching parameters. The paper derives the gradient of the cost functional in a costate-based formula that reflects the special structure of hybrid systems. It then uses the formula in a gradient-descent algorithm for solving an obstacle-avoidance problem in robotics. The work of Boccadoro has been partially supported by MIUR under Grant PRIN 2003090090. The work of Wardi has been partly supported by a grant from the Georgia Tech Manufacturing Research Center. The work of Egerstedt has been partly supported by the National Science Foundation under Grant \# 0237971 ECS NSF-CAREER, and by a grant from the Georgia Tech Manufacturing Research Center.     

The Krakovskii-LaSalle Invariance Principle for a Class of Unilateral Dynamical Systems

This paper is devoted to the study of the extension of the invariance lemma to a class of hybrid dynamical systems, namely evolution variational inequalities. Applications can be found in models of electrical circuits with ideal diodes or oligopolistic market equilibrium.Acknowledgements. This work was partially supported by the European project SICONOS IST2001-37172.     

一类混合动态系统的能控性和能观性研究

针对一类混合动态系统———周期型线性切换系统, 给出系统能控性、能观性的充分必要条件, 并通过例子加以验证.     

一类混杂系统的建模与控制器设计

研究一类离散事件系统和连接时间系统相混合的混杂系统建模与控制器设计问题。首先用不同的模型方法建立混杂系统的层次模型，分析连续时间系统状态空间的离散化和系统的可控性；在此基础上利用ＤＥＳ监控理论设计控制器，以规范系统的时域和逻辑行为。最后的示例表明了层次模型与控制算法的有效性。     

Stable and Real-Time Scheduling of a Class of Hybrid Dynamic Systems

Based on some practical engineering problems arising from process control and space-structure control, this paper studies a class of hybrid dynamic systems in which N plants are controlled by a central controller in sharing time manner, where the plants are described by differential equations and the controller works according to the mechanism of discrete events. An event feedback strategy is suggested to be a scheduling policy such that one and only one plant among N plants is chosen to be controlled at any time. Some conditions of asymptotical and exponential stability are then given and an exponential upper bound of states norm is also estimated for the event feedback scheduling strategy. An algorithm based on event feedback strategy is presented to determine the control laws of the plants to meet the given performance. An example follows to illustrate the application and effect of the results.     

A Limit Law for Outputs in Random Recursive Circuits

We study the structure of uniform random binary recursive circuits. We show that a suitably normalized version of the number of outputs converges in distribution to a normal random variate. We also discuss the connection of the number of outputs to a non-classical urn model, and our investigation provides a first solved instance of this new class of urns. Received September 6, 2000; revised October 5, 2000.     

一类混杂系统建模和优化控制的研究

为解决混杂系统优化控制的计算复杂性问题,针对结合逻辑规则的工业过程混杂模型,采用结合约束程序的混合整数非线性规划算法,求解这种混杂模型的优化控制。计算实例表明,通过混杂建模方法,可以充分利用工业对象的机理模型以及操作工经验或专家经验,建立系统的更精确模型;结合约束程序混合整数非线性规划算法可以较迅速地求解混杂模型优化控制问题,从而使该方法可以用于工业过程实时控制中。     

A Converse Lyapunov Theorem for a Class of Dynamical Systems which Undergo Switching

The authors investigate the stability of a system in which the dynamics at any instant in time will follow one of a fixed set of vector fields. They allow switching between members of the family of vector fields to be completely random. The focus of this paper is to prove a converse Lyapunov theorem for this class of systems.     

利用临界点聚类的平面向量场极限环的检出及可视化

检出和可视化极限环已经成为向量场拓扑分析中日益重要的研究课题．提出了一个基于临界点聚类的检出算法,将向量场的全部临界点以几何距离为相似性判据聚类成一棵二叉树,通过只检查临界点指数和为＋1的少数树结点,以及在算法中增加检测和剔除中心型闭轨的部分,获得了比Wischgoll算法更好的结果．     

On an Optimization Problem for a Class of Impulsive Hybrid Systems

This contribution addresses the problem of optimal control for a class of hybrid systems, where discrete transitions are accompanied by instantaneous changes in the continuous state variables, and where these changes can be considered as control variables. Based on a variational approach, necessary conditions of optimality are first established. The problem is then cast as a parametric optimization problem for which gradient information is derived. Finally, we discuss assumptions that guarantee convergence of a conceptual algorithm to a stationary solution. A brief discussion on the main implementation issues is also included.     

一种自适应控制器设计及其仿真研究

利用将Backstping方法和神经网络相结合,设计了一类非线性不确定系统的鲁棒自适应控制器。该文利用在线神经网络控制器,从而,不要求系统不确定部分有界。通过李亚谱诺夫方法,验证了系统的稳定性;阐述了该系统具有较好的鲁棒性。最后,将所设计的控制器用于仿真实例,得到很好的结果,说明了该方法的有效性。     

Balanced Parametrizations of Stable SISO All-Pass Systems in Discrete Time

A balanced canonical form for discrete-time stable SISO all-pass systems is obtained by requiring the realization to be balanced and such that the reachability matrix is upper triangular with positive diagonal entries, in analogy to the continuous-time balanced canonical form of Ober [O1]. It is shown that the resulting balanced canonical form can be parametrized by Schur parameters. The relation with the Schur parameters for stable AR systems is established. Using the structure of the canonical form it is shown that, for the space of stable all-pass systems of order less than or equal to a fixed number n, the topology of pointwise convergence and the topology induced by H ₂ coincide. The topological space thus obtained has the structure of a hypersphere. Model reduction procedures based on truncation, which respect the canonical form, are discussed. Date received: April 1, 1997. Date revised: April 9, 1999.     

Support Vector Machine and Artificial Neural Networks for Hydrological Cycles Classifications of a Water Reservoir in the Amazon

This paper presents a methodology based on computational intelligence techniques for classification of hydrological cycles that can infer the change in the physico-chemieal parameters and metals from the water of a reservoir in the Amazon. The methodology initially consists in perform a pre-processing the data to select the most relevant variables of the samples. After that, we compared two different machine learning classifiers, namely SVM （support vector machine） and ANN （artificial neural network）. The automatic model selection is made to choose the parameters of the classifiers. The results indicate that the support vector machine classifier using radial basis function or polynomial kernel exhibited superior results to ANN in terms of overall accuracy and robustness. The SVM classifier accuracy （89.1%） can be considered satisfactory, since there is a great variability of physico-chemical parameters and metals in the hydrological cycles and in the different ecosystems where are the sampling station.     

一类非线性系统的模糊滑模控制

针对一类非线性不确定系统,提出了一种模糊滑模控制器的设计方法。采用模糊神经网络设计系统控制器,基于梯度符号变化的局部学习率自适应BP算法,对模糊神经网络的隶属函数中心、宽度和网络权值进行调节,使滑模等效控制的模糊系统后件参数是自适应的;在系统存在外部干扰并有界时,利用李亚普诺夫方法证明了系统控制的稳定性。通过Matlab仿真分析,表明了所设计的模糊滑模控制器不但具有鲁棒性,而且具有良好的跟踪性能。     

一类基于线性补偿模型的网络系统的设计

通过某企业网络系统的建立,以线性补偿模型为基础,研究了网络信号补偿的方法。提出了线性补偿模型并对其进行了探讨,对企业网络系统进行结构需求分析和功能分析,并给出网络的逻辑功能拓扑图。结合企业的实际情况,讨论了企业网络安全的方法,为企业的网络的建立,提供了有力的科学依据。