Abstract
This paper presents a novel Gaussian process (GP) based adaptive tracking control method for autonomous underwater vehicles subject to unknown system dynamics and uncertain smooth actuator nonlinearity. We deploy sparse online Gaussian process (SOGP) technique to estimate the unknown system dynamics and uncertain actuator nonlinearity simultaneously, with a given prior dynamic model. Based on an adaptive sliding mode control framework, the posterior means of GPs are used to compensate for all the unknown terms. Besides, the corresponding posterior variances, which indicate the probabilistic confidence intervals, are applied to update robust control gains. Theoretical analysis is performed to prove the stability of a closed-loop system with our proposed control law. Comparison simulation results validate the effectiveness of our GP-based adaptive tracking control method.
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Acknowledgement
This research was funded by National CMOST Key Research & Development projects (Nos. 2017YFC1404100, 2017YFC1404103 and 2017YFC1404104), the NSFC (No. 41676088) and the Fundamental Research Funds for the Central Universities(No. 3072021CFJ0401).
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He, Y., Zhao, Y., Deng, X., Xu, G. (2022). Learning Based Trajectory Tracking Control of Autonomous Underwater Vehicles with Actuator Nonlinearity. In: Wu, M., Niu, Y., Gu, M., Cheng, J. (eds) Proceedings of 2021 International Conference on Autonomous Unmanned Systems (ICAUS 2021). ICAUS 2021. Lecture Notes in Electrical Engineering, vol 861. Springer, Singapore. https://doi.org/10.1007/978-981-16-9492-9_100
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