Identification of intermittent control in man and machine
Identifieur interne : 002398 ( Main/Merge ); précédent : 002397; suivant : 002399Identification of intermittent control in man and machine
Auteurs : Ian D. Loram [Royaume-Uni] ; Cornelis Van De Kamp [Royaume-Uni] ; Henrik Gollee [Royaume-Uni] ; Peter J. Gawthrop [Royaume-Uni]Source :
- Journal of the Royal Society Interface [ 1742-5689 ] ; 2012.
Abstract
Regulation by negative feedback is fundamental to engineering and biological processes. Biological regulation is usually explained using continuous feedback models from both classical and modern control theory. An alternative control paradigm, intermittent control, has also been suggested as a model for biological control systems, particularly those involving the central nervous system. However, at present, there is no identification method explicitly formulated to distinguish intermittent from continuous control; here, we present such a method. The identification experiment uses a special paired-step set-point sequence. The corresponding data analysis use a conventional ARMA model to relate a theoretically derived equivalent set-point to control signal; the novelty lies in sequentially and iteratively adjusting the timing of the steps of this equivalent set-point to optimize the linear time-invariant fit. The method was verified using realistic simulation data and was found to robustly distinguish not only between continuous and intermittent control but also between event-driven intermittent and clock-driven intermittent control. When applied to human pursuit tracking, event-driven intermittent control was identified, with an intermittent interval of 260–310 ms (
Url:
DOI: 10.1098/rsif.2012.0142
PubMed: 22491973
PubMed Central: 3405763
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PMC:3405763Le document en format XML
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<front><div type="abstract" xml:lang="en"><p>Regulation by negative feedback is fundamental to engineering and biological processes. Biological regulation is usually explained using continuous feedback models from both classical and modern control theory. An alternative control paradigm, intermittent control, has also been suggested as a model for biological control systems, particularly those involving the central nervous system. However, at present, there is no identification method explicitly formulated to distinguish intermittent from continuous control; here, we present such a method. The identification experiment uses a special paired-step set-point sequence. The corresponding data analysis use a conventional ARMA model to relate a theoretically derived equivalent set-point to control signal; the novelty lies in sequentially and iteratively adjusting the timing of the steps of this equivalent set-point to optimize the linear time-invariant fit. The method was verified using realistic simulation data and was found to robustly distinguish not only between continuous and intermittent control but also between event-driven intermittent and clock-driven intermittent control. When applied to human pursuit tracking, event-driven intermittent control was identified, with an intermittent interval of 260–310 ms (<italic>n</italic>
= 6, <italic>p</italic>
< 0.05). This new identification method is applicable for machine and biological applications.</p>
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