Local Adjustment and Global Adaptation of Control Periods for QoC Management of Control Systems

Tian, Yu-Chu; Jiang, Xiefu; Levy, David; Agrawala, Ashok K.

doi:10.1109/tcst.2011.2141133

Cited by 10 publications

(8 citation statements)

References 47 publications

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“…Linking task schedulability directly to the QoC, a hierarchical feedback QoC management framework was proposed in Tian et al (2011) for real-time control systems with multiple control tasks. It uses a task decomposition mode for continuous QoC evaluation, and then dynamically adjusts the control periods for QoC improvement.…”

Section: Related Workmentioning

confidence: 99%

“…A sufficient stability condition is also derived for a class of control systems with delay and period switching. In comparison with the approach developed in Tian et al (2011) which is based on heuristic rules, the QoC elastic scheduling in this paper will be established from mathematical optimization.…”

Section: Related Workmentioning

confidence: 99%

“…Inspired by the idea of feeding the instantaneous controlled variable back to the controller for control computation, this paper proposes to feed an instantaneous QoC index back to the elastic scheduler for period adaptation. A similar idea has also been used in Tian et al (2011).…”

Section: Qoc Characterizationmentioning

confidence: 99%

“…Therefore, the following index E i is defined to characterize the instantaneous QoC of the ith control loop to drive our dynamic period assignment (Tian et al 2011):…”

Section: Qoc Characterizationmentioning

confidence: 99%

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QoC elastic scheduling for real-time control systems

Tian

Gui

2011

Real-Time Syst

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The elastic task model, a significant development in scheduling of realtime control tasks, provides a mechanism for flexible workload management in uncertain environments. It tells how to adjust the control periods to fulfill the workload constraints. However, it is not directly linked to the quality-of-control (QoC) management, the ultimate goal of a control system. As a result, it does not tell how to make the best use of the system resources to maximize the QoC improvement. To fill in this gap, a new feedback scheduling framework, which we refer to as QoC elastic scheduling, is developed in this paper for real-time process control systems. It addresses the QoC directly through embedding both the QoC management and workload adaptation into a constrained optimization problem. The resulting solution for period adjustment is in a closed-form expressed in QoC measurements, enabling closed-loop feedback of the QoC to the task scheduler. Whenever the QoC elastic scheduler is activated, it improves the QoC the most while still meeting the system constraints. Examples are given to demonstrate the effectiveness of the QoC elastic scheduling.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Qoc Characterizationmentioning

confidence: 99%

“…Therefore, the following index E i is defined to characterize the instantaneous QoC of the ith control loop to drive our dynamic period assignment (Tian et al 2011):…”

Section: Qoc Characterizationmentioning

confidence: 99%

See 2 more Smart Citations

QoC elastic scheduling for real-time control systems

Tian

Gui

2011

Real-Time Syst

Self Cite

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“…Adaptation is a well-known topic in Control Systems [11], because of this, the integration of quality adaptation routines can enhance the control performance [167].…”

Section: Service Providers and Intelligent Devicesmentioning

confidence: 99%

Mission-Oriented Heterogeneous Robot Cooperation Based on Smart Resources Execution

Sánchez¹

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Home environments are changing as more technological devices are used to improve daily life. The growing demand for high technology in our homes means that robot integration will soon arrive. Home devices are evolving in a connected paradigm in which data flows to perform efficient home task management. Heterogeneous home robots connected in a network can establish a workflow that complements their capabilities and so increases performance within a mission execution. This work addresses the definition and requirements of a robot-group mission in the home context. The proposed solution relies on a network of smart resources, which are defined as cyber-physical systems that provide high-level service execution. Firstly, control middleware architecture is introduced as the execution base for the Smart resources.Next, the Smart resource topology and its integration within a robotic platform are addressed. Services supplied by Smart resources manage their execution through a robot behavior architecture. Robot behavior execution is hierarchically organized through a mission definition that can be established as an individual or collective approach. Environment model and interaction tasks characterize the operation capabilities of each robot within a mission. Mission goal achievement in a heterogeneous group is enhanced through the complement of the interaction capabilities of each robot. To offer a clearer explanation, a full use case is presented in which two robots cooperate to execute a mission and the previously detailed steps are evaluated. Finally, some of the obtained results are discussed as conclusions and future works is introduced. ResumenLos entornos domésticos se encuentran sometidos a un proceso de cambio gracias al empleo de dispositivos tecnológicos que mejoran la calidad de vida de las personas. La creciente demanda de alta tecnología en los hogares señala una próxima incorporación de la robótica de servicio. Los dispositivos domésticos están evolucionando hacia un paradigma de conexión en el cual la información fluye para ofrecer una gestión más eficiente. En este entorno, robots heterogéneos conectados a la red pueden establecer un flujo de trabajo que ofreciendo nuevas soluciones y incrementando la eficiencia en la ejecución de tareas. Este trabajo aborda la definición y los requisitos necesarios para la ejecución de misiones en grupos de robots heterogéneos en entornos domésticos. La solución propuesta se apoya en una red de Smart resources, que son definidos como sistemas ciber-físicos que proporcionan servicios de alto nivel. En primer lugar, se presenta la arquitectura del middleware de control en la cual se basa la ejecución de los Smart resources. A continuación se detalla la topología de los Smart resources, así como su integración en plataformas robóticas. Los servicios proporcionados por los Smart resources gestionan su ejecución mediante una arquitectura de comportamientos para robots. La ejecución de estos comportamientos se organiza de forma jerárquica mediante la definición de ...

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