Hierarchical Cognitive Control for Unknown Dynamic Systems Tracking

A general control system tracking learning framework is proposed, by which an optimal learned tracking behavior called ‘primitive’ is extrapolated to new unseen trajectories without requiring relearning. This is considered intelligent behavior and strongly related to the neuro-motor cognitive contro...

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Autores principales: Mircea-Bogdan Radac, Timotei Lala
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Lenguaje:EN
Publicado: MDPI AG 2021
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Acceso en línea:https://doaj.org/article/27eae8132fa040ff8b0ebeacc15e0503
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spelling oai:doaj.org-article:27eae8132fa040ff8b0ebeacc15e05032021-11-11T18:17:56ZHierarchical Cognitive Control for Unknown Dynamic Systems Tracking10.3390/math92127522227-7390https://doaj.org/article/27eae8132fa040ff8b0ebeacc15e05032021-10-01T00:00:00Zhttps://www.mdpi.com/2227-7390/9/21/2752https://doaj.org/toc/2227-7390A general control system tracking learning framework is proposed, by which an optimal learned tracking behavior called ‘primitive’ is extrapolated to new unseen trajectories without requiring relearning. This is considered intelligent behavior and strongly related to the neuro-motor cognitive control of biological (human-like) systems that deliver suboptimal executions for tasks outside of their current knowledge base, by using previously memorized experience. However, biological systems do not solve explicit mathematical equations for solving learning and prediction tasks. This stimulates the proposed hierarchical cognitive-like learning framework, based on state-of-the-art model-free control: (1) at the low-level L1, an approximated iterative Value Iteration for linearizing the closed-loop system (CLS) behavior by a linear reference model output tracking is first employed; (2) an experiment-driven Iterative Learning Control (EDILC) applied to the CLS from the reference input to the controlled output learns simple tracking tasks called ‘primitives’ in the secondary L2 level, and (3) the tertiary level L3 extrapolates the primitives’ optimal tracking behavior to new tracking tasks without trial-based relearning. The learning framework relies only on input-output system data to build a virtual state space representation of the underlying controlled system that is assumed to be observable. It has been shown to be effective by experimental validation on a representative, coupled, nonlinear, multivariable real-world system. Able to cope with new unseen scenarios in an optimal fashion, the hierarchical learning framework is an advance toward cognitive control systems.Mircea-Bogdan RadacTimotei LalaMDPI AGarticlehierarchical controlreinforcement learning and approximate dynamic programmingiterative learning controlprimitivesunknown dynamicsinput-output observable systemMathematicsQA1-939ENMathematics, Vol 9, Iss 2752, p 2752 (2021)
institution DOAJ
collection DOAJ
language EN
topic hierarchical control
reinforcement learning and approximate dynamic programming
iterative learning control
primitives
unknown dynamics
input-output observable system
Mathematics
QA1-939
spellingShingle hierarchical control
reinforcement learning and approximate dynamic programming
iterative learning control
primitives
unknown dynamics
input-output observable system
Mathematics
QA1-939
Mircea-Bogdan Radac
Timotei Lala
Hierarchical Cognitive Control for Unknown Dynamic Systems Tracking
description A general control system tracking learning framework is proposed, by which an optimal learned tracking behavior called ‘primitive’ is extrapolated to new unseen trajectories without requiring relearning. This is considered intelligent behavior and strongly related to the neuro-motor cognitive control of biological (human-like) systems that deliver suboptimal executions for tasks outside of their current knowledge base, by using previously memorized experience. However, biological systems do not solve explicit mathematical equations for solving learning and prediction tasks. This stimulates the proposed hierarchical cognitive-like learning framework, based on state-of-the-art model-free control: (1) at the low-level L1, an approximated iterative Value Iteration for linearizing the closed-loop system (CLS) behavior by a linear reference model output tracking is first employed; (2) an experiment-driven Iterative Learning Control (EDILC) applied to the CLS from the reference input to the controlled output learns simple tracking tasks called ‘primitives’ in the secondary L2 level, and (3) the tertiary level L3 extrapolates the primitives’ optimal tracking behavior to new tracking tasks without trial-based relearning. The learning framework relies only on input-output system data to build a virtual state space representation of the underlying controlled system that is assumed to be observable. It has been shown to be effective by experimental validation on a representative, coupled, nonlinear, multivariable real-world system. Able to cope with new unseen scenarios in an optimal fashion, the hierarchical learning framework is an advance toward cognitive control systems.
format article
author Mircea-Bogdan Radac
Timotei Lala
author_facet Mircea-Bogdan Radac
Timotei Lala
author_sort Mircea-Bogdan Radac
title Hierarchical Cognitive Control for Unknown Dynamic Systems Tracking
title_short Hierarchical Cognitive Control for Unknown Dynamic Systems Tracking
title_full Hierarchical Cognitive Control for Unknown Dynamic Systems Tracking
title_fullStr Hierarchical Cognitive Control for Unknown Dynamic Systems Tracking
title_full_unstemmed Hierarchical Cognitive Control for Unknown Dynamic Systems Tracking
title_sort hierarchical cognitive control for unknown dynamic systems tracking
publisher MDPI AG
publishDate 2021
url https://doaj.org/article/27eae8132fa040ff8b0ebeacc15e0503
work_keys_str_mv AT mirceabogdanradac hierarchicalcognitivecontrolforunknowndynamicsystemstracking
AT timoteilala hierarchicalcognitivecontrolforunknowndynamicsystemstracking
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