Accelerated Gradient Approach For Neural Network Adaptive Control Of Nonlinear Systems
Recent connections in the adaptive control literature to continuous-time analogues of Nesterov's accelerated gradient method have led to the development of new real-time adaptation laws based on accelerated gradient methods. However, previous results assume the system's uncertainties are l...
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| Vydáno v: | Proceedings of the IEEE Conference on Decision & Control s. 3475 - 3480 |
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| Hlavní autoři: | , , , |
| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
| Vydáno: |
IEEE
06.12.2022
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| Témata: | |
| ISSN: | 2576-2370 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | Recent connections in the adaptive control literature to continuous-time analogues of Nesterov's accelerated gradient method have led to the development of new real-time adaptation laws based on accelerated gradient methods. However, previous results assume the system's uncertainties are linear-in-the-parameters (LIP). In this paper, a new NN-based accelerated gradient adaptive controller is developed to achieve trajectory tracking in general nonlinear systems subject to unstructured uncertainties that do not satisfy the LIP assumption. Higher-order accelerated gradient-based adaptation laws are developed to generate real-time estimates of both the unknown ideal output-and hidden-layer weights of a NN. A nonsmooth Lyapunov-based method is used to guarantee the closed-loop error system achieves global asymptotic tracking. Simulations are conducted to demonstrate the improved performance from the developed method. Results show the higher-order adaptation outperforms the standard gradient-based NN adaptation by 32.3% in terms of the root mean squared function approximation error. |
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| ISSN: | 2576-2370 |
| DOI: | 10.1109/CDC51059.2022.9993122 |