Supplementary material for nonparameteric adaptive control of time varying systems using gaussian processes
This technical report has supplementary material for "Bayesian Nonparametric Adaptive Control of Time-varying Systems using Gaussian Processes" American Control Conference paper
| Main Authors: | , , , |
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| Format: | Technical Report |
| Language: | en_US |
| Published: |
2013
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| Online Access: | http://hdl.handle.net/1721.1/77933 |
| _version_ | 1826192674117386240 |
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| author | Chowdhary, Girish Kingravi, Hassan A. How, Jonathan P. Vela, Patricio A. |
| author_facet | Chowdhary, Girish Kingravi, Hassan A. How, Jonathan P. Vela, Patricio A. |
| author_sort | Chowdhary, Girish |
| collection | MIT |
| description | This technical report has supplementary material for "Bayesian Nonparametric Adaptive Control of Time-varying Systems using Gaussian Processes" American Control Conference paper |
| first_indexed | 2024-09-23T09:27:13Z |
| format | Technical Report |
| id | mit-1721.1/77933 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T09:27:13Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | mit-1721.1/779332019-04-10T22:35:13Z Supplementary material for nonparameteric adaptive control of time varying systems using gaussian processes Chowdhary, Girish Kingravi, Hassan A. How, Jonathan P. Vela, Patricio A. adaptive control Bayesian Nonparametric Models time varying systems gaussian processes This technical report has supplementary material for "Bayesian Nonparametric Adaptive Control of Time-varying Systems using Gaussian Processes" American Control Conference paper Real-world dynamical variations make adaptive control of time-varying systems highly relevant. However, most adaptive control literature focuses on analyzing systems where the uncertainty is represented as a weighted linear combination of fixed number of basis functions, with constant weights. One approach to modeling time variations is to assume time varying ideal weights, and use difference integration to accommodate weight variation. However, this approach reactively suppresses the uncertainty, and has little ability to predict system behavior locally. We present an alternate formulation by leveraging Bayesian nonparametric Gaussian Process adaptive elements. We show that almost surely bounded adaptive controllers for a class of nonlinear time varying system can be formulated by incorporating time as an additional input to the Gaussian kernel. Analysis and simulations show that the learning-enabled local predictive ability of our adaptive controllers significantly improves performance. This research was supported by ONR MURI Grant N000141110688 and NSF grant ECS #0846750. 2013-03-15T20:50:56Z 2013-03-15T20:50:56Z 2013-03-15 Technical Report http://hdl.handle.net/1721.1/77933 en_US Attribution-NonCommercial-ShareAlike 3.0 United States http://creativecommons.org/licenses/by-nc-sa/3.0/us/ application/pdf |
| spellingShingle | adaptive control Bayesian Nonparametric Models time varying systems gaussian processes Chowdhary, Girish Kingravi, Hassan A. How, Jonathan P. Vela, Patricio A. Supplementary material for nonparameteric adaptive control of time varying systems using gaussian processes |
| title | Supplementary material for nonparameteric adaptive control of time varying systems using gaussian processes |
| title_full | Supplementary material for nonparameteric adaptive control of time varying systems using gaussian processes |
| title_fullStr | Supplementary material for nonparameteric adaptive control of time varying systems using gaussian processes |
| title_full_unstemmed | Supplementary material for nonparameteric adaptive control of time varying systems using gaussian processes |
| title_short | Supplementary material for nonparameteric adaptive control of time varying systems using gaussian processes |
| title_sort | supplementary material for nonparameteric adaptive control of time varying systems using gaussian processes |
| topic | adaptive control Bayesian Nonparametric Models time varying systems gaussian processes |
| url | http://hdl.handle.net/1721.1/77933 |
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