Zobrazeno 1 - 10
of 520
pro vyhledávání: '"Jean-Jacques E"'
Many energy-based control strategies for mechanical systems require the choice of a Coriolis factorization satisfying a skew-symmetry property. This paper explores (a) if and when a control designer has flexibility in this choice, (b) what choice sho
Externí odkaz:
http://arxiv.org/abs/2312.14425
Autor:
Zhang, Thomas T. C. K., Tu, Stephen, Boffi, Nicholas M., Slotine, Jean-Jacques E., Matni, Nikolai
Motivated by bridging the simulation to reality gap in the context of safety-critical systems, we consider learning adversarially robust stability certificates for unknown nonlinear dynamical systems. In line with approaches from robust control, we c
Externí odkaz:
http://arxiv.org/abs/2112.10690
Publikováno v:
Annual Reviews in Control; Volume 52; 2021; Pages 135-169; ISSN 1367-5788
Contraction theory is an analytical tool to study differential dynamics of a non-autonomous (i.e., time-varying) nonlinear system under a contraction metric defined with a uniformly positive definite matrix, the existence of which results in a necess
Externí odkaz:
http://arxiv.org/abs/2110.00675
A key assumption in the theory of nonlinear adaptive control is that the uncertainty of the system can be expressed in the linear span of a set of known basis functions. While this assumption leads to efficient algorithms, it limits applications to v
Externí odkaz:
http://arxiv.org/abs/2106.03589
The stable combination of optimal feedback policies with online learning is studied in a new control-theoretic framework for uncertain nonlinear systems. The framework can be systematically used in transfer learning and sim-to-real applications, wher
Externí odkaz:
http://arxiv.org/abs/2104.02709
This work develops a new direct adaptive control framework that extends the certainty equivalence principle to general nonlinear systems with unmatched model uncertainties. The approach adjusts the rate of adaptation online to eliminate the effects o
Externí odkaz:
http://arxiv.org/abs/2012.15815
We study the problem of adaptively controlling a known discrete-time nonlinear system subject to unmodeled disturbances. We prove the first finite-time regret bounds for adaptive nonlinear control with matched uncertainty in the stochastic setting, s
Externí odkaz:
http://arxiv.org/abs/2011.13101
This work proposes a quaternion-based sliding variable that describes exponentially convergent error dynamics for any forward complete desired attitude trajectory. The proposed sliding variable directly operates on the non-Euclidean space formed by q
Externí odkaz:
http://arxiv.org/abs/2011.03648
We present Neural Stochastic Contraction Metrics (NSCM), a new design framework for provably-stable robust control and estimation for a class of stochastic nonlinear systems. It uses a spectrally-normalized deep neural network to construct a contract
Externí odkaz:
http://arxiv.org/abs/2011.03168
Many existing tools in nonlinear control theory for establishing stability or safety of a dynamical system can be distilled to the construction of a certificate function that guarantees a desired property. However, algorithms for synthesizing certifi
Externí odkaz:
http://arxiv.org/abs/2008.05952