Exploiting Differential Flatness for Robust Learning-Based Tracking Control Using Gaussian Processes
| Autor: | Melissa Greeff, Angela P. Schoellig |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Control and Optimization Computer science Probabilistic logic 02 engineering and technology Linear-quadratic regulator 01 natural sciences Tracking error Nonlinear system symbols.namesake 020901 industrial engineering & automation Control and Systems Engineering Linearization Robustness (computer science) Control theory Control system 0103 physical sciences symbols 010303 astronomy & astrophysics Gaussian process |
| Zdroj: | IEEE Control Systems Letters. 5:1121-1126 |
| ISSN: | 2475-1456 |
| DOI: | 10.1109/lcsys.2020.3009177 |
| Popis: | Learning-based control has shown to outperform conventional model-based techniques in the presence of model uncertainties and systematic disturbances. However, most state-of-the-art learning-based nonlinear trajectory tracking controllers still lack any formal guarantees. In this letter, we exploit the property of differential flatness to design an online, robust learning-based controller to achieve both high tracking performance and probabilistically guarantee a uniform ultimate bound on the tracking error. A common control approach for differentially flat systems is to try to linearize the system by using a feedback (FB) linearization controller designed based on a nominal system model. Performance and safety are limited by the mismatch between the nominal model and the actual system. Our proposed approach uses a nonparametric Gaussian Process (GP) to both improve FB linearization and quantify, probabilistically, the uncertainty in our FB linearization. We use this probabilistic bound in a robust linear quadratic regulator (LQR) framework. Through simulation, we highlight that our proposed approach significantly outperforms alternative learning-based strategies that use differential flatness. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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