Matches in SemOpenAlex for { <https://semopenalex.org/work/W2995486512> ?p ?o ?g. }
- W2995486512 endingPage "4169" @default.
- W2995486512 startingPage "4154" @default.
- W2995486512 abstract "Combining control engineering with nonparametric modeling techniques from machine learning allows for the control of systems without analytic description using data-driven models. Most of the existing approaches separate learning, i.e., the system identification based on a fixed dataset, and control, i.e., the execution of the model-based control law. This separation makes the performance highly sensitive to the initial selection of training data and possibly requires very large datasets. This article proposes a learning feedback linearizing control law using online closed-loop identification. The employed Gaussian process model updates its training data only if the model uncertainty becomes too large. This event-triggered online learning ensures high data efficiency and thereby reduces computational complexity, which is a major barrier for using Gaussian processes under real-time constraints. We propose safe forgetting strategies of data points to adhere to budget constraints and to further increase data efficiency. We show asymptotic stability for the tracking error under the proposed event-triggering law and illustrate the effective identification and control in simulation." @default.
- W2995486512 created "2019-12-26" @default.
- W2995486512 creator A5024376647 @default.
- W2995486512 creator A5028926639 @default.
- W2995486512 date "2020-10-01" @default.
- W2995486512 modified "2023-10-03" @default.
- W2995486512 title "Feedback Linearization Based on Gaussian Processes With Event-Triggered Online Learning" @default.
- W2995486512 cites W1576808520 @default.
- W2995486512 cites W1586409345 @default.
- W2995486512 cites W1956078349 @default.
- W2995486512 cites W1978518835 @default.
- W2995486512 cites W1985235885 @default.
- W2995486512 cites W1986061648 @default.
- W2995486512 cites W1988212844 @default.
- W2995486512 cites W1997543377 @default.
- W2995486512 cites W2007864935 @default.
- W2995486512 cites W2018869705 @default.
- W2995486512 cites W2038436774 @default.
- W2995486512 cites W2041242313 @default.
- W2995486512 cites W2042882799 @default.
- W2995486512 cites W2054184020 @default.
- W2995486512 cites W2059315199 @default.
- W2995486512 cites W2078394884 @default.
- W2995486512 cites W2078609652 @default.
- W2995486512 cites W2085330455 @default.
- W2995486512 cites W2115932303 @default.
- W2995486512 cites W2121764658 @default.
- W2995486512 cites W2133255100 @default.
- W2995486512 cites W2136638407 @default.
- W2995486512 cites W2166566250 @default.
- W2995486512 cites W2173709365 @default.
- W2995486512 cites W2480512873 @default.
- W2995486512 cites W2512923311 @default.
- W2995486512 cites W2559850580 @default.
- W2995486512 cites W2567017688 @default.
- W2995486512 cites W2735248836 @default.
- W2995486512 cites W2738836907 @default.
- W2995486512 cites W2761385663 @default.
- W2995486512 cites W2807198100 @default.
- W2995486512 cites W2808816909 @default.
- W2995486512 cites W2854640194 @default.
- W2995486512 cites W2950630402 @default.
- W2995486512 cites W3098713169 @default.
- W2995486512 cites W3099516967 @default.
- W2995486512 doi "https://doi.org/10.1109/tac.2019.2958840" @default.
- W2995486512 hasPublicationYear "2020" @default.
- W2995486512 type Work @default.
- W2995486512 sameAs 2995486512 @default.
- W2995486512 citedByCount "64" @default.
- W2995486512 countsByYear W29954865122019 @default.
- W2995486512 countsByYear W29954865122020 @default.
- W2995486512 countsByYear W29954865122021 @default.
- W2995486512 countsByYear W29954865122022 @default.
- W2995486512 countsByYear W29954865122023 @default.
- W2995486512 crossrefType "journal-article" @default.
- W2995486512 hasAuthorship W2995486512A5024376647 @default.
- W2995486512 hasAuthorship W2995486512A5028926639 @default.
- W2995486512 hasBestOaLocation W29954865122 @default.
- W2995486512 hasConcept C105795698 @default.
- W2995486512 hasConcept C111919701 @default.
- W2995486512 hasConcept C11210021 @default.
- W2995486512 hasConcept C116834253 @default.
- W2995486512 hasConcept C119247159 @default.
- W2995486512 hasConcept C119857082 @default.
- W2995486512 hasConcept C121332964 @default.
- W2995486512 hasConcept C138885662 @default.
- W2995486512 hasConcept C154945302 @default.
- W2995486512 hasConcept C158622935 @default.
- W2995486512 hasConcept C163716315 @default.
- W2995486512 hasConcept C2775924081 @default.
- W2995486512 hasConcept C2777851325 @default.
- W2995486512 hasConcept C2779662365 @default.
- W2995486512 hasConcept C33923547 @default.
- W2995486512 hasConcept C41008148 @default.
- W2995486512 hasConcept C41895202 @default.
- W2995486512 hasConcept C47446073 @default.
- W2995486512 hasConcept C59822182 @default.
- W2995486512 hasConcept C61326573 @default.
- W2995486512 hasConcept C62520636 @default.
- W2995486512 hasConcept C67186912 @default.
- W2995486512 hasConcept C7149132 @default.
- W2995486512 hasConcept C77088390 @default.
- W2995486512 hasConcept C86803240 @default.
- W2995486512 hasConcept C98045186 @default.
- W2995486512 hasConceptScore W2995486512C105795698 @default.
- W2995486512 hasConceptScore W2995486512C111919701 @default.
- W2995486512 hasConceptScore W2995486512C11210021 @default.
- W2995486512 hasConceptScore W2995486512C116834253 @default.
- W2995486512 hasConceptScore W2995486512C119247159 @default.
- W2995486512 hasConceptScore W2995486512C119857082 @default.
- W2995486512 hasConceptScore W2995486512C121332964 @default.
- W2995486512 hasConceptScore W2995486512C138885662 @default.
- W2995486512 hasConceptScore W2995486512C154945302 @default.
- W2995486512 hasConceptScore W2995486512C158622935 @default.
- W2995486512 hasConceptScore W2995486512C163716315 @default.
- W2995486512 hasConceptScore W2995486512C2775924081 @default.
- W2995486512 hasConceptScore W2995486512C2777851325 @default.
- W2995486512 hasConceptScore W2995486512C2779662365 @default.