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| Main Author: | |
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| Format: | Preprint |
| Published: |
2024
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| Online Access: | https://arxiv.org/abs/2412.01103 |
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| _version_ | 1866912142321516544 |
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| author | Fujimori, Takahito |
| author_facet | Fujimori, Takahito |
| contents | Linear Quadratic Regulator (LQR) is often combined with feedback linearization (FBL) for nonlinear systems that have the nonlinearity additive to the input. Conventional approaches estimate and cancel the nonlinearity based on the first principle or data-driven methods such as Gaussian Processes (GPs). However, the former needs an elaborate modeling process, and the latter provides a fixed learned model, which may be suffering when the model dynamics are changing. In this letter, we take a Deep Neural Network (DNN) using a real-time-updated dataset to approximate the unknown nonlinearity while the controller is running. Spectrally normalizing the weights in each time-step, we stably incorporate the DNN prediction to an LQR controller and compensate for the nonlinear term. Leveraging the property of the bounded Lipschitz constant of the DNN, we provide theoretical analysis and locally exponential stability of the proposed controller. Simulation results show that our controller significantly outperforms Baseline controllers in trajectory tracking cases. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_01103 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | FRIDAY: Real-time Learning DNN-based Stable LQR controller for Nonlinear Systems under Uncertain Disturbances Fujimori, Takahito Systems and Control F.2.2; I.2.7 Linear Quadratic Regulator (LQR) is often combined with feedback linearization (FBL) for nonlinear systems that have the nonlinearity additive to the input. Conventional approaches estimate and cancel the nonlinearity based on the first principle or data-driven methods such as Gaussian Processes (GPs). However, the former needs an elaborate modeling process, and the latter provides a fixed learned model, which may be suffering when the model dynamics are changing. In this letter, we take a Deep Neural Network (DNN) using a real-time-updated dataset to approximate the unknown nonlinearity while the controller is running. Spectrally normalizing the weights in each time-step, we stably incorporate the DNN prediction to an LQR controller and compensate for the nonlinear term. Leveraging the property of the bounded Lipschitz constant of the DNN, we provide theoretical analysis and locally exponential stability of the proposed controller. Simulation results show that our controller significantly outperforms Baseline controllers in trajectory tracking cases. |
| title | FRIDAY: Real-time Learning DNN-based Stable LQR controller for Nonlinear Systems under Uncertain Disturbances |
| topic | Systems and Control F.2.2; I.2.7 |
| url | https://arxiv.org/abs/2412.01103 |