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Towards data-based adaptive control

机译:迈向基于数据的自适应控制

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摘要

This paper presents a possible new direction for the design of adaptive controllers. It begins by outlining a measurement-based approach to the analysis, synthesis, and design of unknown systems. The systems under consideration can typically be electrical, mechanical, hydraulic, continuous, discrete, real, artificial, or biological, or of societal origin. Our approach is novel in the sense that we propose to use the measurement, or measured data, to directly design the control or decision variables whereas the current approach in the mainstream of system theory advocates that measured data be used to first identify the plant, and control design, or synthesis, then be based on the identified model. This latter approach has necessitated the creation of fields such as robust control and (indirect) adaptive control that try to account for errors in the identified model. Our approach, being data based, would potentially be automatically robust and adaptive. We show that such a measurement-based approach is certainly feasible by considering DC circuits, AC circuits, block diagrams, and control systems. These results are then used to formulate the research problems for data-based adaptive control.
机译:本文为自适应控制器的设计提出了一个可能的新方向。它首先概述了基于度量的方法,用于未知系统的分析,综合和设计。所考虑的系统通常可以是电气的,机械的,液压的,连续的,离散的,真实的,人工的或生物的或社会起源的。在我们建议使用测量值或测量数据直接设计控制或决策变量的意义上,我们的方法是新颖的,而系统理论主流的当前方法则主张将测量数据用于首先识别工厂,以及控制设计或综合,然后基于识别出的模型。后一种方法必须创建诸如鲁棒控制和(间接)自适应控制之类的字段,以尝试解决所识别模型中的错误。我们基于数据的方法可能会自动健壮和自适应。通过考虑直流电路,交流电路,框图和控制系统,我们证明了这种基于测量的方法当然是可行的。然后将这些结果用于制定基于数据的自适应控制的研究问题。

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