Towards Self-Manageable and Adaptive Cyber-Physical Systems for Manufacturing Automation
面向制造自动化的自我管理和自适应网络物理系统
基本信息
- 批准号:RGPIN-2018-03856
- 负责人:
- 金额:$ 1.97万
- 依托单位:
- 依托单位国家:加拿大
- 项目类别:Discovery Grants Program - Individual
- 财政年份:2018
- 资助国家:加拿大
- 起止时间:2018-01-01 至 2019-12-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
To remain competitive in today's global market, manufacturers require systems that are capable of quickly responding to change while maintaining stable and efficient operation. Increasingly, the industrial automation and control system is viewed as being central to achieving this goal. ******Current industrial automation and control systems are typically implemented using large, and often expensive, hardware platforms that support monolithic computer control applications. As a result, when the industrial automation and control system is installed, commissioning can take months to complete, and once the system is operational, changes are often complex and difficult. Both of these factors contribute significantly to the total cost of the project. The proposed research is intended to rectify these problems through the development of a distributed intelligent sensing and control solution that is inherently adaptable and dynamically re-configurable. This approach will take advantage of distributed artificial intelligence at the planning and control levels to achieve significantly shorter up-front commissioning times as well as significantly more responsiveness to change. These potential benefits, in combination with the current trend towards cyber-physical production through the fusion of physical and digital operations, will result in a much more attractive, low-cost automation solution for future manufacturers than current centralized solutions.******The objective of this research program is to develop innovative technologies and practical solutions to realize industrial automation systems that are both flexible (i.e., capable of reconfiguration) and responsive (i.e., capable of recovering from disturbances). More specifically, this work will build on the conceptual work on cyber-physical systems (CPS) with a focus on developing a design methodology and appropriate analysis techniques for device-level sensing and control in harsh, industrial environments (e.g., discrete-part manufacturing, chemical and process industries). Our approach focuses on design strategies to support self-management at the device level, with the specific goals of developing techniques and services for self-configuration and self-healing. The overall aim of this research is to help Canadian manufacturers remain competitive in today's global market by enabling them to respond quickly to change while maintaining stable system operation and efficient use of available resources.
为了在当今的全球市场上保持竞争力,制造商需要能够快速响应变化同时保持稳定高效运行的系统。工业自动化和控制系统越来越被视为实现这一目标的核心。** 当前的工业自动化和控制系统通常使用支持单片计算机控制应用的大型且通常昂贵的硬件平台来实现。因此,当工业自动化和控制系统安装时,调试可能需要数月才能完成,并且一旦系统运行,更改通常是复杂和困难的。这两个因素对项目的总成本都有很大影响。拟议的研究旨在通过开发具有内在适应性和动态可重新配置的分布式智能传感和控制解决方案来纠正这些问题。这种方法将在规划和控制层面利用分布式人工智能,以显著缩短前期调试时间,并显著提高对变化的响应能力。这些潜在的好处,结合当前通过物理和数字操作融合实现网络物理生产的趋势,将为未来的制造商带来比当前集中式解决方案更具吸引力的低成本自动化解决方案。该研究计划的目标是开发创新技术和实用的解决方案,以实现工业自动化系统,既灵活(即,能够重新配置)和响应(即,能够从干扰中恢复)。更具体地说,这项工作将建立在网络物理系统(CPS)的概念工作的基础上,重点是开发一种设计方法和适当的分析技术,用于在恶劣的工业环境中进行设备级传感和控制(例如,离散部件制造、化学和加工工业)。我们的方法侧重于设计策略,以支持在设备级的自我管理,开发技术和服务的自我配置和自我修复的具体目标。这项研究的总体目标是帮助加拿大制造商在当今的全球市场上保持竞争力,使他们能够快速响应变化,同时保持稳定的系统运行和有效利用可用资源。
项目成果
期刊论文数量(0)
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BRENNAN, Robert其他文献
BRENNAN, Robert的其他文献
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{{ truncateString('BRENNAN, Robert', 18)}}的其他基金
Towards Self-Manageable and Adaptive Cyber-Physical Systems for Manufacturing Automation
面向制造自动化的自我管理和自适应网络物理系统
- 批准号:
RGPIN-2018-03856 - 财政年份:2022
- 资助金额:
$ 1.97万 - 项目类别:
Discovery Grants Program - Individual
Towards Self-Manageable and Adaptive Cyber-Physical Systems for Manufacturing Automation
面向制造自动化的自我管理和自适应网络物理系统
- 批准号:
RGPIN-2018-03856 - 财政年份:2021
- 资助金额:
$ 1.97万 - 项目类别:
Discovery Grants Program - Individual
Towards Self-Manageable and Adaptive Cyber-Physical Systems for Manufacturing Automation
面向制造自动化的自我管理和自适应网络物理系统
- 批准号:
RGPIN-2018-03856 - 财政年份:2020
- 资助金额:
$ 1.97万 - 项目类别:
Discovery Grants Program - Individual
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