Supervising manufacturing system operation by DEVS-based intelligent control

Praehofer, I.; Jahn, Gerhard; Jacak, Witold; Haider, G.

doi:10.1109/aihas.1994.390485

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…Because of different underlying architectures and requirements of "Automation Systems" in comparison to "Information Systems" (as pointed out in Kiesz 1995) it seems necessary to modify and adapt these methods and techniques. Smith et al (1994) and Praehofer et al (1994) already proposed coupling simulation with shop floor equipment. But these approaches aim to control and monitor the machinery by the simulation directly and both approaches are tailored to the domain of Flexible Manufacturing Systems (FMS).…”

Section: State Of the Artmentioning

confidence: 99%

Interface driven domain-independent modeling architecture for “soft-commissioning” and “reality in the loop”

Auinger

Vorderwinkler

Buchtela

1999

Proceedings of the 31st Conference on Winter Simulation Simulation---a Bridge to the Future - WSC '99

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As industrial manufacturing and automation systems grow in complexity, there is a need for control software engineering support. Soft-Commissioning and Reality in the Loop (RIL) are two novel approaches which allow coupling simulation models to real world entities and allow the analyst to pre-commission and test the behavior of a system, before it is completely built in reality. To be flexible and fast in building up a simulation model fulfilling the requirements of Soft-Commissioning and RIL there is a need for a component-based modeling architecture. In this paper we define the characteristic requirements, and derive an architecture out of them, which is discussed from different aspects. Finally we briefly present a simple example.

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Section: State Of the Artmentioning

confidence: 99%

Interface driven domain-independent modeling architecture for “soft-commissioning” and “reality in the loop”

Auinger

Vorderwinkler

Buchtela

1999

Proceedings of the 31st Conference on Winter Simulation Simulation---a Bridge to the Future - WSC '99

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“…Parallel control architectures can be used [9]. Self-repairing strategies for 3D motion planning have been investigated [6], and also for production systems [12]. Eventually, self-repairing robots will be built.…”

mentioning

confidence: 99%

Reinforcing the Driving Quality of Soccer Playing Robots by Anticipation (Verbesserung der Fahreigenschaften von fußballspielenden Robotern durch Antizipation)

Gloye¹,

Wiesel²,

Tenchio³

et al. 2005

It - Information Technology

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This paper shows how an omnidirectional robot can learn to correct inaccuracies when driving, or even learn to use corrective motor commands when a motor fails, whether partially or completely. Driving inaccuracies are unavoidable, since not all wheels have the same grip on the surface, or not all motors can provide exactly the same power. When a robot starts driving, the real system response differs from the ideal behavior assumed by the control software. Also, malfunctioning motors are a fact of life that we have to take into account. Our approach is to let the control software learn how the robot reacts to instructions sent from the control computer. We use a neural network, or a linear model for learning the robot's response to the commands. The model can be used to predict deviations from the desired path, and take corrective action in advance, thus increasing the driving accuracy of the robot. The model can also be used to monitor the robot and assess if it is performing according to its learned response function. If it is not, the new response function of the malfunctioning robot can be learned and updated. We show, that even if a robot loses power from a motor, the system can relearn to drive the robot in a straight path, even if the robot is a black-box and we are not aware of how the commands are applied internally.Zusammenfassung In diesem Artikel zeigen wir, wie ein vierrädriger holonomer Roboter Ungenauigkeiten beim Fahren korrigieren kann, indem die Regelungswerte für die Motoren geeignet modifiziert werden. Diese Verfahren arbeiten auch im Falle eines Motordefekts. Die Steuerung wird automatisch so angepasst, dass mit nur drei Motoren gefahren werden kann. Eine unpräzise Steuerung ist im Allgemeinen nicht zu vermeiden, denn die Räder können eine unterschiedliche Haftreibung mit dem Untergrund haben, Motoren haben durch Fertigungstoleranzen und Verschleiss unterschiedliche Leistungsmerkmale. Außerdem sind die Modelle zur Steuerung der Motoren oft so einfach gehalten, dass sie nicht exakt die physikalischen Eigenschaften widerspiegeln. Manchmal fallen Motoren aus, dies sollte ebenfalls berücksich-tigt werden. Bei unserem Ansatz lernt die Steuerungssoftware, wie der Roboter auf unterschiedliche Befehle reagiert und passt die Befehle dementsprechend an, bevor sie an den Roboter übergeben werden. Dafür benutzen wir ein neuronales Netz oder ein lineares Modell, um die Bewegung des Roboters vorherzusagen. Die Abweichungen der tatsächlichen Bewegung bezüglich der gewünschten Bewegung kann dann benutzt werden, um geeignetere Befehle zu wählen. Dies verbessert die Fahrweise des Roboters erheblich. Das gelernte Modell kann außerdem dazu genutzt werden, um den Zustand des Roboters fortlaufend zu testen. Falls eine zu große Abweichung zur Wirklichkeit besteht, kann ein anderes Modell gewählt werden oder ein neues gelernt werden. Wenn ein Motor eines Roboters an Leistung verliert, kann für diesen Roboter eine spezielle Vorhersage trainiert werden, damit der Roboter wieder akkurat fährt. Gerade wenn wenig Informa...

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Interface driven domain-independent modeling architecture for "soft-commissioning" and "reality in the loop"

Auinger¹,

Vorderwinkler²,

Buchtela³

WSC'99. 1999 Winter Simulation Conference Proceedings. 'Simulation - A Bridge to the Future' (Cat. No.99CH37038)

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Supervising manufacturing system operation by DEVS-based intelligent control

Cited by 3 publications

References 10 publications

Interface driven domain-independent modeling architecture for “soft-commissioning” and “reality in the loop”

Interface driven domain-independent modeling architecture for “soft-commissioning” and “reality in the loop”

Reinforcing the Driving Quality of Soccer Playing Robots by Anticipation (Verbesserung der Fahreigenschaften von fußballspielenden Robotern durch Antizipation)

Interface driven domain-independent modeling architecture for "soft-commissioning" and "reality in the loop"

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