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A FIRST APPROACH TO AN ARTIFICIAL NETWORKED COGNITIVE CONTROL SYSTEM BASED ON THE SHARE! CIRCUITS MODEL OF SOCIOCOGNITIVE CAPACITIES

机译:基于份额的人工网络认知控制系统的第一种方法!社会经影能能力电路模型

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摘要

The top priority in high-performance manufacturing processes is the development of a new generation of control systems to enable faster, more efficient manufacturing by means of cooperative, self-organized, self-optimized behaviour. Some natural cognitive systems display effective behaviour through perception, action, deliberation, communication, and interaction with other individuals and with the environment. An artificial cognitive control architecture is presented which is based on the shared circuits model (SCM) of sociocognitive skills proposed by Hurley.1 The proposal consists of a five-layer architecture in which the SCM approach is used to emulate such sociocognitive skills as imitation, deliberation, and mindreading. In Hurley's approach, these capacities can be enabled by mechanisms of control, mirroring the actions of others, and simulation. A control system thus designed should be capable of responding efficiently and robustly to the problems it is set. In the present implementation, the original SCM approach was enriched and modified in the light of constructive suggestions and evaluations in the literature. With these modifications, SCM served as the foundation for the design of a networked control architecture for application to an industrial case study - a high-performance drilling process. Experiments demonstrated that the proposed artificial cognitive control system can deal with nonlinearities and uncertainties in the drilling process, providing a good transient response and good error-based performance indices.
机译:高性能制造过程中的首要任务是开发新一代控制系统,以通过合作,自组织,自我优化行为实现更快,更高效的制造。一些自然认知系统通过感知,行动,审议,沟通和与其他人和环境的互动显示有效的行为。提出了一种人工认知控制架构,其基于Hurley提出的社会认知技能的共享电路模型(SCM).1该提案包括五层架构,其中SCM方法用于将这种社会认知技能仿作为模仿,审议和思维方式。在Hurley的方法中,这些能力可以通过控制机制,镜像其他人的动作和仿真。如此设计的控制系统应该能够有效且鲁棒地响应其设置的问题。在本实施中,根据文献中的建议建议和评估,富集和修改了原始的SCM方法。通过这些修改,SCM担任用于应用于工业案例研究的网络控制架构的基础 - 一种高性能钻探过程。实验表明,所提出的人工认知控制系统可以处理钻井过程中的非线性和不确定性,提供良好的瞬态响应和基于良好的基于​​误差的性能指标。

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