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Guaranteed Nonlinear Tracking in the Presence of DNN-Learned Dynamics With Contraction Metrics and Disturbance Estimation. (arXiv:2112.08222v3 [eess.SY] UPDATED)
April 25, 2022, 1:11 a.m. | Pan Zhao, Ziyao Guo, Yikun Cheng, Aditya Gahlawat, Hyungsoo Kang, Naira Hovakimyan
cs.LG updates on arXiv.org arxiv.org
This paper presents an approach to trajectory-centric learning control based
on contraction metrics and disturbance estimation for nonlinear systems subject
to matched uncertainties. The proposed approach allows for the use of deep
neural networks to learn uncertain dynamics while still providing guarantees of
transient tracking performance throughout the learning phase. Within the
proposed approach, a disturbance estimation law is adopted to estimate the
pointwise value of the uncertainty, with pre-computable estimation error bounds
(EEBs). The learned dynamics, the estimated disturbances, …
More from arxiv.org / cs.LG updates on arXiv.org
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