A Low-Cost Embedded Controller for Complex Control Systems

Peng, Zhe; Ma, Longhua; Xia, Feng

doi:10.1109/euc.2008.40

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…Embedded control systems [1] continue to grow exponentially and has become critical and complex under usually functional and temporal constraints to be described in user requirements [2]. According to various evolutions of the environment due to incidents or also optimization of performance, the system is required to be flexible by adapting its behavior at run-time.…”

Section: Introductionmentioning

confidence: 99%

RA2DL: New Flexible Solution for Adaptive AADL-based Control Components

Adaili

Mosbahi

Khalgui

et al. 2015

Proceedings of the 5th International Conference on Pervasive and Embedded Computing and Communication Systems

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The paper deals with adaptive component-based control systems following the Architecture Analysis and Design Language (denoted by AADL). A system is assumed to be a network of software and hardware AADL components that share the control of corresponding physical processes. A component is composed of a set of algorithms encoding the control after any reception of external events and data signals. The termination of execution is generally done with the emission of data and event signals to remote components. According to various evolutions in environment, the system is required to be dynamically reconfigured at run-time to adapt its control functions. We are interested in local reconfigurations of components dealing with the activation-deactivation-update of algorithms and/or data-event inputs and outputs. We propose RA2DL as a solution for reconfigurable AADL components, and define a hierarchical-based architecture to dynamically handle all possible reconfiguration scenarios at run-time. We model and verify this solution and develop a tool for its simulation by taking a real-case study as a running example.

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Section: Introductionmentioning

confidence: 99%

RA2DL: New Flexible Solution for Adaptive AADL-based Control Components

Adaili

Mosbahi

Khalgui

et al. 2015

Proceedings of the 5th International Conference on Pervasive and Embedded Computing and Communication Systems

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“…In this context, traditional centralised and distributed control systems (DCS) have greatly promoted the development of industrial control technology in the past few decades. However, with the development of industrial control technology, traditional control systems have been unable to meet the new requirements (such as intelligence, networked, high efficiency, low cost and low power consumption) of next generation control systems [10]- [11]. In recent years, the rapid development of computer and communication technology, especially the rapid advancement of embedded control technology, provides a new research direction for the practical application of complex control methods [12][13].…”

Section: Introductionmentioning

confidence: 99%

Design and Development of an Embedded Intelligent Optimal Control Platform

Fang

Yang²,

Wu³

et al. 2012

Measurement and Control

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An embedded intelligent optimal control platform is designed and developed in this paper. On the hardware side, the embedded industrial controller uses ARM9 as the core processor to meet the requirements of lowpower consumption, low-cost and high computation performance. On the software side, Xenomai Linux OS and ProConOS are adopted to realise hard real-time control performance. Besides, MultiProg is used to support block programming of the control software. To validate the effectiveness of embedded control platform, the grinding circuit mineral processing plants is used as an example to design an intelligent optimal control system and validates its performance through Hardwarein-the-Loop experiment. The experiment results show that this platform can fully satisfy the requirements of industrial optimal control.

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“…There are many different approaches to design and implement embedded control systems, (see, for instance, [6], [8]). In this work, the concept of control kernel [2] is used to compute the control in two stages.…”

Section: Introductionmentioning

confidence: 99%

Control kernel based adaptive control implementation

2013

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Association for Computing Machinery (ACM)Simarro Fernández, R.; Albertos Pérez, P.; Simó Ten, JE. (2013). Control kernel based adaptive control implementation. SIGBED review. 10(1):24-28. doi:10.1145/2492385.2492389. Control kernel based adaptive control implementation ABSTRACTA control system with distributed computing resources always should guarantee the safe control of the plant. In this contribution, the concept of control kernel is used for that purpose. Two types of nodes with different resources are defined: the powerful server node and the resource-constrained light node. This architecture allows to split the control tasks into two blocks. Those demanding strong computing resources are allocated in the server nodes and those compelling tasks required to ensure the safety of the controlled plant are allocated in the light nodes. Resource limitations lead to control adaptation. Two simple applications illustrate some of the benefits of this architecture with one server node and one light node, even the architecture can be extended to several nodes. In the first case, an adaptive control is implemented in the server node, providing the control algorithm to the light node, which is also able to compute a local safe control action. In the second experiment, two different control tasks requiring different resources are implemented in a mobile robot control. To keep bounded the computing time at the local level, the supervisor decides the time allocated to each activity, providing the resulting controller to the light node.

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A Low-Cost Embedded Controller for Complex Control Systems

Cited by 4 publications

References 13 publications

RA2DL: New Flexible Solution for Adaptive AADL-based Control Components

RA2DL: New Flexible Solution for Adaptive AADL-based Control Components

Design and Development of an Embedded Intelligent Optimal Control Platform

Control kernel based adaptive control implementation

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