Deep Neuro-Adaptive Sliding Mode Controller for Higher-Order Heterogeneous Nonlinear Multi-Agent Teams with Leader
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909710598275072 |
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| author | Chaudhari, Khushal Nath, Krishanu Bera, Manas Kumar |
| author_facet | Chaudhari, Khushal Nath, Krishanu Bera, Manas Kumar |
| contents | This letter proposes a deep neural network (DNN)-based neuro-adaptive sliding mode control (SMC) strategy for leader-follower tracking in multi-agent systems with higher-order, heterogeneous, nonlinear, and unknown dynamics under external disturbances. The DNN is used to compensate the unknown nonlinear dynamics with higher accuracy than shallow neural networks (NNs) and SMC ensures robust tracking. This framework employs restricted potential functions within a set-theoretic paradigm to ensure system trajectories remain bounded within a compact set, improving robustness against approximation errors and external disturbances. The control scheme is grounded in non-smooth Lyapunov stability theory, with update laws derived for both inner and outer layer network weights of DNN. A numerical example is simulated that showcases the proposed controller's effectiveness, adaptability, and robustness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_21667 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep Neuro-Adaptive Sliding Mode Controller for Higher-Order Heterogeneous Nonlinear Multi-Agent Teams with Leader Chaudhari, Khushal Nath, Krishanu Bera, Manas Kumar Systems and Control This letter proposes a deep neural network (DNN)-based neuro-adaptive sliding mode control (SMC) strategy for leader-follower tracking in multi-agent systems with higher-order, heterogeneous, nonlinear, and unknown dynamics under external disturbances. The DNN is used to compensate the unknown nonlinear dynamics with higher accuracy than shallow neural networks (NNs) and SMC ensures robust tracking. This framework employs restricted potential functions within a set-theoretic paradigm to ensure system trajectories remain bounded within a compact set, improving robustness against approximation errors and external disturbances. The control scheme is grounded in non-smooth Lyapunov stability theory, with update laws derived for both inner and outer layer network weights of DNN. A numerical example is simulated that showcases the proposed controller's effectiveness, adaptability, and robustness. |
| title | Deep Neuro-Adaptive Sliding Mode Controller for Higher-Order Heterogeneous Nonlinear Multi-Agent Teams with Leader |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2507.21667 |