Momentum-Based Adaptive Laws for Identification and Control
In this paper, we develop momentum-based adaptive update laws for parameter identification and control to improve parameter estimation error convergence and control system performance for uncertain dynamical systems. Specifically, we introduce three novel continuous-time, momentum-based adaptive est...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/11/12/1017 |
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| _version_ | 1846106502321930240 |
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| author | Luke Somers Wassim M. Haddad |
| author_facet | Luke Somers Wassim M. Haddad |
| author_sort | Luke Somers |
| collection | DOAJ |
| description | In this paper, we develop momentum-based adaptive update laws for parameter identification and control to improve parameter estimation error convergence and control system performance for uncertain dynamical systems. Specifically, we introduce three novel continuous-time, momentum-based adaptive estimation and control algorithms and evaluate their effectiveness via several numerical examples. Our proposed adaptive architectures show faster parameter convergence rates as compared to the classical gradient descent and model reference adaptive control methods. |
| format | Article |
| id | doaj-art-9a0df63ff55740d887697b82fc7dfdb2 |
| institution | Kabale University |
| issn | 2226-4310 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj-art-9a0df63ff55740d887697b82fc7dfdb22024-12-27T14:02:33ZengMDPI AGAerospace2226-43102024-12-011112101710.3390/aerospace11121017Momentum-Based Adaptive Laws for Identification and ControlLuke Somers0Wassim M. Haddad1School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0150, USASchool of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0150, USAIn this paper, we develop momentum-based adaptive update laws for parameter identification and control to improve parameter estimation error convergence and control system performance for uncertain dynamical systems. Specifically, we introduce three novel continuous-time, momentum-based adaptive estimation and control algorithms and evaluate their effectiveness via several numerical examples. Our proposed adaptive architectures show faster parameter convergence rates as compared to the classical gradient descent and model reference adaptive control methods.https://www.mdpi.com/2226-4310/11/12/1017parameter identificationonline learningMRACmomentum-based learninghigh-order tunersexponential convergence |
| spellingShingle | Luke Somers Wassim M. Haddad Momentum-Based Adaptive Laws for Identification and Control Aerospace parameter identification online learning MRAC momentum-based learning high-order tuners exponential convergence |
| title | Momentum-Based Adaptive Laws for Identification and Control |
| title_full | Momentum-Based Adaptive Laws for Identification and Control |
| title_fullStr | Momentum-Based Adaptive Laws for Identification and Control |
| title_full_unstemmed | Momentum-Based Adaptive Laws for Identification and Control |
| title_short | Momentum-Based Adaptive Laws for Identification and Control |
| title_sort | momentum based adaptive laws for identification and control |
| topic | parameter identification online learning MRAC momentum-based learning high-order tuners exponential convergence |
| url | https://www.mdpi.com/2226-4310/11/12/1017 |
| work_keys_str_mv | AT lukesomers momentumbasedadaptivelawsforidentificationandcontrol AT wassimmhaddad momentumbasedadaptivelawsforidentificationandcontrol |