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http://dx.doi.org/10.18419/opus-11990
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DC Element | Wert | Sprache |
---|---|---|
dc.contributor.author | Lefarov, Maksym | - |
dc.date.accessioned | 2022-02-22T11:23:50Z | - |
dc.date.available | 2022-02-22T11:23:50Z | - |
dc.date.issued | 2018 | de |
dc.identifier.other | 1795301953 | - |
dc.identifier.uri | http://nbn-resolving.de/urn:nbn:de:bsz:93-opus-ds-120074 | de |
dc.identifier.uri | http://elib.uni-stuttgart.de/handle/11682/12007 | - |
dc.identifier.uri | http://dx.doi.org/10.18419/opus-11990 | - |
dc.description.abstract | Due to its simplicity and demonstrated performance, Proportional Integral and Derivative (PID) controller remains one of the most widely-used closed-loop control mechanisms in industrial applications. For the unknown model of a system, however, a PID design can become a significantly complex task especially for a Multiple Input Multiple Output (MIMO) case. For the efficient control of a nonlinear and non-stationary systems, a scheduled PID controller can be designed. The classical approach to gain scheduling is a system linearization and the design of controllers at different operating points with a subsequent application of interpolation. This thesis continues on the recent advances in application of Reinforcement Learning (RL) to a multivariate PID tuning. In this work we extend the multivariate PID tuning framework based on the Probabilistic Inference for Learning Control (PILCO) algorithm to tune a scheduled PID controllers. The developed method does not require the linear model of a system dynamics and is not restricted to the low-order or Single Input Single Output (SISO) systems. The algorithm is evaluated using the Noisy Cart-Pole and Non-stationary Mass-Damper systems. Additionally, the proposed method is applied to the tuning of a scheduled PID controllers of autonomous Remote Control (RC) car. | en |
dc.language.iso | en | de |
dc.rights | info:eu-repo/semantics/openAccess | de |
dc.subject.ddc | 004 | de |
dc.title | Model-based policy search for learning mulitvariate PID gain scheduling control | en |
dc.type | masterThesis | de |
ubs.fakultaet | Informatik, Elektrotechnik und Informationstechnik | de |
ubs.institut | Institut für Parallele und Verteilte Systeme | de |
ubs.publikation.seiten | 74 | de |
ubs.publikation.typ | Abschlussarbeit (Master) | de |
Enthalten in den Sammlungen: | 05 Fakultät Informatik, Elektrotechnik und Informationstechnik |
Dateien zu dieser Ressource:
Datei | Beschreibung | Größe | Format | |
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18-lefarov-MSc.pdf | 3,3 MB | Adobe PDF | Öffnen/Anzeigen |
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