非时间参考的机器人路径规划与控制方法

本文面向非结构化环境,针对传统的基于时间的规划方法的不足,提出了一种非时间参考的 机器人路径规划方法;并根据该规划的特点,设计了相应的控制算法．在这种规划方法下, 机器人遇到障碍时能自动停止运动,而当障碍物被清除后,又能沿以前的规划继续运动,避 免了系统所受到的损害和任务的重规划,提高了系统处理不确定事件的能力．     

多产品间歇生产过程离散事件的建模与分析

本文根据多产品间歇复杂生产过程离散事件活动的特点，建立了ＦＩＳ，ＮＩＳ，ＵＩＳ，ＺＷ及ＭＩＳ情形在极大代数和意义下的“线模型。基于所建立模型分析可以得到过程的生产时间和生产周期，以及求解过程的一些生产控制问题。     

冷连轧混合系统中离散事件仿真的建模及关键技术

本文以冷连轧过程仿真为例，分析了混合系统中离散事件的特点以及与连续部分的关 系，对系统进行了层次化分解，讨论了在混合系统仿真过程中对于离散事件部分的建模需要 注意的问题，同时给出了用有限状态机方法实现仿真建模的过程，以及实际应用中的关键技 术．     

离散事件仿真及其在半导体生产线建模中的应用

详细阐述了离散事件仿真技术的原理及其关键技术,并将其应用于半导体生产线建模。在分析半导体生产线系统的仿真需求的基础上,以单进程为离散事件处理机制,对12个加工中心、40台机器组成的半导体生产线进行了建模研究,验证了半导体生产线仿真的正确性。     

面向对象的离散事件仿真建模与实现

该文论述了面向对象方法在离散事件仿真中的应用，提出了一种建模与开发仿真程序的新方法，其特点是易于在单进程操作系统、普通编程平台上实现对并发事件的准确描述和仿真执行。     

基于规则的模糊离散事件系统建模与控制研究

用模糊离散事件系统（FDES）从离散事件的角度描述一类包含“具有确定性的不确定／模糊”问题以及与人的主观观察／判断密切相关的复杂系统。提出了基于规则的FDES描述方法及自寻优监督控制策略,为FDES的实际应用提供了一种方法,提高了FDES在具体应用中的可操作性,并介绍了其在纺织染整控制过程中的具体应用．     

一种基于测地线的机器人轨迹规划方法

提出了一种基于测地线的机器人轨迹规划方法.该方法克服了传统的轨迹规划方法的某些不足,其规划是在关节空间(黎曼空间)内进行的,规划目标是直角坐标空间内的直线,即两点之间的最短路径,也可以是系统动能最小,或某项综合指标最优.该规划方法直接得到机器人的各关节的转角和角速度,无需进行运动学反解和多项式插值.本文的基于测地线的轨迹规划是以轨迹弧长作为参考变量的,因此它还具有非时间参考的机器人轨迹规 划的优点.􀁱     

基于对象Petri网的离散事件系统建模仿真环境(OPMSE)

首先论述了Ｐｅｔｒｉ网和高级Ｐｅｔｒｉ网发展状况,提出了一种高级Ｐｅｔｒｉ网,即对象Ｐｅｔｒｉ网。然后介绍了基于对象Ｐｅｔｒｉ网的离散事件系统建模仿真环境ＯＰＭＳＥ。文中详细介绍了ＯＰＭＳＥ的特点与组成,并对ＯＰＭＳＥ的核心－对象Ｐｅｔｒｉ网（ＯＰＮ）的语法描述进行了重点介绍。     

分布式多机器人运动控制的离散事件系统方法

传统多机器人系统的运动控制主要依赖于机器人的动力学方程或运动学方程,通过求解微分方程组来获得机器人的输入控制信号.随着系统中机器人数量的增加和运行环境的复杂化,动力学方程很难描述多机器人系统的运动行为,且无法很好地解决诸如死锁等逻辑故障.本文简略综述了国内外的研究现状,重点介绍笔者所在研究组开展的关于离散事件系统方法在多机器人运动控制方面的应用性研究工作.其动机在于:1)基于离散事件系统方法的运动控制能够有效地解决系统运行过程中产生的诸如死锁等逻辑故障.首先,利用离散事件系统模型对多机器人系统的运动进行建模,从而降低计算复杂性;其次,基于所得离散事件系统模型,设计分布式安全运动控制算法,使各个机器人可以自主地、无碰撞地、无死锁地运动;设计分布式鲁棒运动控制算法,使得失效的机器人对系统的影响最小.2)基于离散事件系统方法的运动控制策略可以结合传统的基于运动学方程的运动控制方法,从而使系统不但能够避免顶层的逻辑故障,而且能够确定机器人执行器的输入信号.     

离散事件动态系统的概率建模

本文通过对一类离散事件动态系统——拆卸生产线的建模,介绍了一种新的概率建模方法——概率流方法.所谓“概率流”是状态间转移概率的一种形象描述,概率流建模的出发点是充分小时间内状态转移概率的流平衡,所得到模型是非线性方程组形式,通过定性分析可证系统是渐近稳定的.此外,本文还讨论了概率流方法与马氏理论的联系与区别.     

Distributed fuzzy discrete event system for robotic sensory information processing

Abstract: This paper presents a novel intelligent sensory information processing technique using a fuzzy discrete event system (FDES) for robotic control. The proposed method combines the predictive control approach of a discrete event system with the approximate reasoning aspect of fuzzy logic. It develops a supervisory control strategy for behavior-based robotic control using distributed FDES. The application of distributed FDES eliminates the formation of complex fuzzy predicates and a large fuzzy rule-base. The FDES-based approach also provides means for analyzing behavior-based decision-making using the observability and controllability of an FDES. The observability of an FDES describes uncertainties in sensory data, and the controllability of an FDES exploits uncertain state transitions in a dynamic environment. Comprehensive experiments on behavior-based mobile robot navigation are presented to authenticate the performance of the proposed methodology.     

基于事件图的网络化PLC控制程序编译方法

针对网络化PLC控制系统特点,提出了基于事件图的控制程序建模与编译方法。通过将控制系统映射为离散事件系统,建立了控制程序的事件图模型;通过改进的深度优先搜索算法实现了事件图解耦,将串行执行的控制程序分解为可并行执行的事件序列;根据IO变量位置以及指令预期执行时间为事件序列分组,并下载至最佳设备;通过插入网络通讯指令,实现设备间变量同步。实验结果表明该方法可有效识别与提取控制程序中的并行任务,将其合理分配下载至不同的控制器中,同时保证控制逻辑的正确与同步。     

A control synthesis approach for time discrete event systems

In this paper, we introduce a control synthesis method for discrete event systems whose behavior is dependent on explicit values of time. Our goal is to control the occurrence dates of the controllable events so that the functioning of the system respects given specifications. The system to be controlled is modeled by a time Petri net. In a previous work we proposed a systematic method to build the timed automaton which models the exact behavior of a time Petri net. Furthermore, the forbidden behaviors of the system are modeled by forbidden timed automaton locations. This paper focuses on the control synthesis method, which consists in computing new firing conditions for the timed automaton transitions so that the forbidden locations are no longer reachable.     

A modeling strategy for hybrid systems based on event structures

This paper considers hybrid systems which are continuous/discrete-time systems interacting with a decision maker which oversees the control and structure of the continuous/discrete-time system. These two segments combine with a processor which evaluates data to produce a three-segment model of a hybrid system having sufficient flexibility to represent a broad range of real-time situations and sufficient generality to incorporate the essential aspects of other models into a single framework. In particular the paper uses a graphically expressive controlled Petri net formulation of the decision maker and any of the usual models for the continuous/discrete-time system. Interaction between the systems occurs via three types of events: continuous/discrete-time events, decision-making events, and processor events. These types of events and their composition are rigorously defined to produce event structures and event histories. These events and event histories are used for the domain of interaction functions which specify the channels of communication between the three essential segments of the hybrid system. The event-based domains allow the disassociation of these communication channels from dependence on particular kinds of models or applications. The range of the interaction functions are binary vector-valued indicating the activation/deactivation processes in the respective segments. The entire modeling strategy is motivated by applications and models found in the literature especially flexible manufacturing systems and the C-net model of a hybrid system.This work was partially supported by the National Science Foundation under Grant No. ECS-8800910.     

A conceptual modeling framework for discrete event simulation using hierarchical control structures

Conceptual Modeling (CM) is a fundamental step in a simulation project. Nevertheless, it is only recently that structured approaches towards the definition and formulation of conceptual models have gained importance in the Discrete Event Simulation (DES) community. As a consequence, frameworks and guidelines for applying CM to DES have emerged and discussion of CM for DES is increasing. However, both the organization of model-components and the identification of behavior and system control from standard CM approaches have shortcomings that limit CM’s applicability to DES. Therefore, we discuss the different aspects of previous CM frameworks and identify their limitations. Further, we present the Hierarchical Control Conceptual Modeling framework that pays more attention to the identification of a models’ system behavior, control policies and dispatching routines and their structured representation within a conceptual model. The framework guides the user step-by-step through the modeling process and is illustrated by a worked example.     

Ordinal optimization approach to rare event probability problems

In this paper we introduce a new approach to rare event simulation. Because of the extensive simulation required for precise estimation of performance criterion dependent on rare event occurrences, obstacles such as computing budget/time constraints and pseudo-random number generator limitations can become prohibitive, particularly if comparative study of different system designs is involved. Existing methods for rare events simulation have focused on simulation budget reduction while attempting to generate accurate performance estimates. In this paper we propose a new approach for rare events system analysis in which we relax the simulation goal to the isolation of a set of good enough designs with high probability. Given this relaxation, referred to as ordinal optimization and advanced by Ho et al. (1992), this paper's approach calls instead for the consideration of an appropriate surrogate design problem This surrogate problem is characterized by its approximate ordinal equivalence to the original problem and its performance criterion's dependence not on rare event occurrences, but on more frequent events. Evaluation of such a surrogate problem under the relaxed goals of ordinal optimization has experimentally resulted in orders of magnitude reduction in simulation burden.     

Grid modeling of robot cells: A memory-efficient approach

This article introduces a new approach to modeling robot workspace in two dimensions (and its extension to 2^1/2 dimensions and to mobile objects). The scheme proves to be attractive because it requires less memory space and is execution-efficient. It is very useful for real-time applications.The grid modeling consists in dividing the space into horizontal and vertical strips whose intersection will form elementary cells. A function giving the access conditions (either a binary one allowing access or nonallowed access, or a more elaborate one including other elements (task, geometries)) is associated to each cell. The size of the adaptive grid will depend on the criterion of the obstacle minimal approach by the robot.Then a method for path planning is developed. The Lee and A^* algorithms being the starting point, the method has been conceived by executing mask operations. So the method requires less memory space and allows an admissible trajectory to be quickly found.     

An extremum-seeking control approach for constrained robotic motion tasks

In this paper, we propose two adaptive control schemes for multiple-input systems for execution of robot end-effector movements in the presence of parametric system uncertainties. The design of these schemes is based on Model Reference Adaptive Control (MRAC) while the adaptation of the controller parameters is achieved by Extremum Seeking Control (ESC). The two control schemes, which are called Multiple-Input ESC–MRAC and Multiple-Input Adaptive-Dynamic-Inversion ESC–MRAC, are suitable for linear and nonlinear systems respectively. Lyapunov and averaging analysis shows that the proposed schemes achieve practical asymptotic reference state tracking. The proposed methods are evaluated in simulations and in a real-world robotic experiment.     

Stabilization and blocking in state feedback control of discrete event systems

We consider controlled discrete event systems modeled by the Ramadge-Wonham model, with a control specification given in terms of both admissible and target states. We define blocking in state feedback control using the notion of stability of discrete event systems. Intuitively, a system is said to be blocking if some trajectories of admissible states cannot reach target states. For control-invariant predicates we define two performance measures called a prestabilizing measure and a blocking measure. First, we present an algorithm to compute the minimally restrictive nonblocking solution. But the nonblocking solution may be restrictive. Then we present design methods to improve the two performance measures. And we show that the task of logical optimization of a blocking feedback can be done by two steps.     

Supervisory control using augmented languages in discrete event systems

This paper introduces the concept of an augmented (super) language of a specified language and studies its application to finite state supervisory control. First, we investigate several properties of an augmented language, especially related to controllability of the specified language. We propose an algorithm for the computation of a controllable sublanguage for which a finite state supervisor exists, using an augmented language, and show a sufficient condition for the controllable sublanguage to be supremal. It is shown, however, that such a finite state supervisor is sometimes blocking. Moreover, we discuss the relationship between the Wonham-Ramadge algorithm and our proposed one.