Matches in SemOpenAlex for { <https://semopenalex.org/work/W4291036574> ?p ?o ?g. }
- W4291036574 endingPage "9293" @default.
- W4291036574 startingPage "9275" @default.
- W4291036574 abstract "Abstract This article presents an online learning stochastic model predictive control method for linear uncertain systems with state‐dependent additive uncertainties, where the uncertainty is modeled as Gaussian process. The proposed scheme utilizes the probabilistic reachable sets as time‐varying tubes, which are formulated by forecasting the variance propagation of uncertainty via Gaussian process regression, to embody the chance constraints. The proposed learning based stochastic model predictive control algorithm is designed by refining the active data dictionary to train the Gaussian process model of uncertainty online. In particular, the data points in the active data dictionary are selected from the raw data around the predicted optimal nominal trajectories, which reduces the computational load as well as preserves the control performance. Then the algorithm feasibility and closed‐loop stability of the developed algorithm are analyzed. Finally, the efficacy and superiority over existing methods are verified by simulation studies." @default.
- W4291036574 created "2022-08-13" @default.
- W4291036574 creator A5005228021 @default.
- W4291036574 creator A5023604125 @default.
- W4291036574 creator A5027292166 @default.
- W4291036574 creator A5031353872 @default.
- W4291036574 date "2022-08-12" @default.
- W4291036574 modified "2023-10-18" @default.
- W4291036574 title "Online learning stochastic model predictive control of linear uncertain systems" @default.
- W4291036574 cites W1524886571 @default.
- W4291036574 cites W1965842330 @default.
- W4291036574 cites W1978956894 @default.
- W4291036574 cites W1981723834 @default.
- W4291036574 cites W1984935210 @default.
- W4291036574 cites W1997370378 @default.
- W4291036574 cites W2045925641 @default.
- W4291036574 cites W2087152401 @default.
- W4291036574 cites W2102341081 @default.
- W4291036574 cites W2119974577 @default.
- W4291036574 cites W2138410957 @default.
- W4291036574 cites W2146851580 @default.
- W4291036574 cites W2149316177 @default.
- W4291036574 cites W2150529049 @default.
- W4291036574 cites W2313845455 @default.
- W4291036574 cites W2396317032 @default.
- W4291036574 cites W2399440847 @default.
- W4291036574 cites W2418467170 @default.
- W4291036574 cites W2537300304 @default.
- W4291036574 cites W2557055507 @default.
- W4291036574 cites W2751786346 @default.
- W4291036574 cites W2789418969 @default.
- W4291036574 cites W2798274031 @default.
- W4291036574 cites W2803107565 @default.
- W4291036574 cites W2954115742 @default.
- W4291036574 cites W2963945659 @default.
- W4291036574 cites W2972868310 @default.
- W4291036574 cites W2979317251 @default.
- W4291036574 cites W2982046139 @default.
- W4291036574 cites W3037742481 @default.
- W4291036574 cites W3081272029 @default.
- W4291036574 cites W3093540494 @default.
- W4291036574 cites W3104125295 @default.
- W4291036574 cites W3105252106 @default.
- W4291036574 cites W3117140904 @default.
- W4291036574 cites W3119715644 @default.
- W4291036574 cites W3156259240 @default.
- W4291036574 cites W4246038204 @default.
- W4291036574 cites W4255050574 @default.
- W4291036574 doi "https://doi.org/10.1002/rnc.6338" @default.
- W4291036574 hasPublicationYear "2022" @default.
- W4291036574 type Work @default.
- W4291036574 citedByCount "1" @default.
- W4291036574 countsByYear W42910365742023 @default.
- W4291036574 crossrefType "journal-article" @default.
- W4291036574 hasAuthorship W4291036574A5005228021 @default.
- W4291036574 hasAuthorship W4291036574A5023604125 @default.
- W4291036574 hasAuthorship W4291036574A5027292166 @default.
- W4291036574 hasAuthorship W4291036574A5031353872 @default.
- W4291036574 hasConcept C111919701 @default.
- W4291036574 hasConcept C112972136 @default.
- W4291036574 hasConcept C119857082 @default.
- W4291036574 hasConcept C121332964 @default.
- W4291036574 hasConcept C121955636 @default.
- W4291036574 hasConcept C126255220 @default.
- W4291036574 hasConcept C144133560 @default.
- W4291036574 hasConcept C154945302 @default.
- W4291036574 hasConcept C163716315 @default.
- W4291036574 hasConcept C172205157 @default.
- W4291036574 hasConcept C196083921 @default.
- W4291036574 hasConcept C2775924081 @default.
- W4291036574 hasConcept C33923547 @default.
- W4291036574 hasConcept C41008148 @default.
- W4291036574 hasConcept C47446073 @default.
- W4291036574 hasConcept C49937458 @default.
- W4291036574 hasConcept C61326573 @default.
- W4291036574 hasConcept C62520636 @default.
- W4291036574 hasConcept C81692654 @default.
- W4291036574 hasConcept C98045186 @default.
- W4291036574 hasConceptScore W4291036574C111919701 @default.
- W4291036574 hasConceptScore W4291036574C112972136 @default.
- W4291036574 hasConceptScore W4291036574C119857082 @default.
- W4291036574 hasConceptScore W4291036574C121332964 @default.
- W4291036574 hasConceptScore W4291036574C121955636 @default.
- W4291036574 hasConceptScore W4291036574C126255220 @default.
- W4291036574 hasConceptScore W4291036574C144133560 @default.
- W4291036574 hasConceptScore W4291036574C154945302 @default.
- W4291036574 hasConceptScore W4291036574C163716315 @default.
- W4291036574 hasConceptScore W4291036574C172205157 @default.
- W4291036574 hasConceptScore W4291036574C196083921 @default.
- W4291036574 hasConceptScore W4291036574C2775924081 @default.
- W4291036574 hasConceptScore W4291036574C33923547 @default.
- W4291036574 hasConceptScore W4291036574C41008148 @default.
- W4291036574 hasConceptScore W4291036574C47446073 @default.
- W4291036574 hasConceptScore W4291036574C49937458 @default.
- W4291036574 hasConceptScore W4291036574C61326573 @default.
- W4291036574 hasConceptScore W4291036574C62520636 @default.
- W4291036574 hasConceptScore W4291036574C81692654 @default.
- W4291036574 hasConceptScore W4291036574C98045186 @default.