A dual adaptive control theory inspired by Hebbian associative learning
Hebbian associative learning is a common form of neuronal adaptation in the brain and is important for many physiological functions such as motor learning, classical conditioning and operant conditioning. Here we show that a Hebbian associative learning synapse is an ideal neuronal substrate for the...
| Main Authors: | , , |
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| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers
2010
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| Online Access: | http://hdl.handle.net/1721.1/59424 |
| _version_ | 1826201731691708416 |
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| author | Feng, Jun-e Tin, Chung Poon, Chi-Sang |
| author2 | Harvard University--MIT Division of Health Sciences and Technology |
| author_facet | Harvard University--MIT Division of Health Sciences and Technology Feng, Jun-e Tin, Chung Poon, Chi-Sang |
| author_sort | Feng, Jun-e |
| collection | MIT |
| description | Hebbian associative learning is a common form of neuronal adaptation in the brain and is important for many physiological functions such as motor learning, classical conditioning and operant conditioning. Here we show that a Hebbian associative learning synapse is an ideal neuronal substrate for the simultaneous implementation of high-gain adaptive control (HGAC) and model-reference adaptive control (MRAC), two classical adaptive control paradigms. The resultant dual adaptive control (DAC) scheme is shown to achieve superior tracking performance compared to both HGAC and MRAC, with increased convergence speed and improved robustness against disturbances and adaptation instability. The relationships between convergence rate and adaptation gain/error feedback gain are demonstrated via numerical simulations. According to these relationships, a tradeoff between the convergence rate and overshoot exists with respect to the choice of adaptation gain and error feedback gain. |
| first_indexed | 2024-09-23T11:56:13Z |
| format | Article |
| id | mit-1721.1/59424 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T11:56:13Z |
| publishDate | 2010 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | mit-1721.1/594242022-10-01T07:04:49Z A dual adaptive control theory inspired by Hebbian associative learning Feng, Jun-e Tin, Chung Poon, Chi-Sang Harvard University--MIT Division of Health Sciences and Technology Massachusetts Institute of Technology. Department of Mechanical Engineering Poon, Chi-Sang Poon, Chi-Sang Feng, Jun-e Tin, Chung Hebbian associative learning is a common form of neuronal adaptation in the brain and is important for many physiological functions such as motor learning, classical conditioning and operant conditioning. Here we show that a Hebbian associative learning synapse is an ideal neuronal substrate for the simultaneous implementation of high-gain adaptive control (HGAC) and model-reference adaptive control (MRAC), two classical adaptive control paradigms. The resultant dual adaptive control (DAC) scheme is shown to achieve superior tracking performance compared to both HGAC and MRAC, with increased convergence speed and improved robustness against disturbances and adaptation instability. The relationships between convergence rate and adaptation gain/error feedback gain are demonstrated via numerical simulations. According to these relationships, a tradeoff between the convergence rate and overshoot exists with respect to the choice of adaptation gain and error feedback gain. National Institutes of Health (U.S.) (HL072849) National Institutes of Health (U.S.) (HL067966) National Institutes of Health (U.S.) (EB005460) 2010-10-20T15:33:00Z 2010-10-20T15:33:00Z 2010-01 2009-12 Article http://purl.org/eprint/type/ConferencePaper 978-1-4244-3871-6 0191-2216 INSPEC Accession Number: 11148353 http://hdl.handle.net/1721.1/59424 Jun-e Feng, Chung Tin, and Chi-Sang Poon. “A dual adaptive control theory inspired by Hebbian associative learning.” Decision and Control, 2009 held jointly with the 2009 28th Chinese Control Conference. CDC/CCC 2009. Proceedings of the 48th IEEE Conference on. 2009. 4505-4510. © 2010 Institute of Electrical and Electronics Engineers. en_US http://dx.doi.org/10.1109/CDC.2009.5400831 Proceedings of the 48th IEEE Conference on Decision and Control, 2009 held jointly with the 2009 28th Chinese Control Conference. CDC/CCC 2009 Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Institute of Electrical and Electronics Engineers IEEE |
| spellingShingle | Feng, Jun-e Tin, Chung Poon, Chi-Sang A dual adaptive control theory inspired by Hebbian associative learning |
| title | A dual adaptive control theory inspired by Hebbian associative learning |
| title_full | A dual adaptive control theory inspired by Hebbian associative learning |
| title_fullStr | A dual adaptive control theory inspired by Hebbian associative learning |
| title_full_unstemmed | A dual adaptive control theory inspired by Hebbian associative learning |
| title_short | A dual adaptive control theory inspired by Hebbian associative learning |
| title_sort | dual adaptive control theory inspired by hebbian associative learning |
| url | http://hdl.handle.net/1721.1/59424 |
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