FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3213596036> ?p ?o ?g. }

• W3213596036 endingPage "169" @default.
• W3213596036 startingPage "135" @default.
• W3213596036 abstract "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 necessary and sufficient characterization of incremental exponential stability of multiple solution trajectories with respect to each other. By using a squared differential length as a Lyapunov-like function, its nonlinear stability analysis boils down to finding a suitable contraction metric that satisfies a stability condition expressed as a linear matrix inequality, indicating that many parallels can be drawn between well-known linear systems theory and contraction theory for nonlinear systems. Furthermore, contraction theory takes advantage of a superior robustness property of exponential stability used in conjunction with the comparison lemma. This yields much-needed safety and stability guarantees for neural network-based control and estimation schemes, without resorting to a more involved method of using uniform asymptotic stability for input-to-state stability. Such distinctive features permit systematic construction of a contraction metric via convex optimization, thereby obtaining an explicit exponential bound on the distance between a time-varying target trajectory and solution trajectories perturbed externally due to disturbances and learning errors. The objective of this paper is therefore to present a tutorial overview of contraction theory and its advantages in nonlinear stability analysis of deterministic and stochastic systems, with an emphasis on deriving formal robustness and stability guarantees for various learning-based and data-driven automatic control methods. In particular, we provide a detailed review of techniques for finding contraction metrics and associated control and estimation laws using deep neural networks." @default.
• W3213596036 created "2021-11-22" @default.
• W3213596036 creator A5020563795 @default.
• W3213596036 creator A5031408324 @default.
• W3213596036 creator A5044026747 @default.
• W3213596036 date "2021-01-01" @default.
• W3213596036 modified "2023-09-30" @default.
• W3213596036 title "Contraction theory for nonlinear stability analysis and learning-based control: A tutorial overview" @default.
• W3213596036 cites W1561789346 @default.
• W3213596036 cites W1576413672 @default.
• W3213596036 cites W1593496074 @default.
• W3213596036 cites W1970789124 @default.
• W3213596036 cites W1971735090 @default.
• W3213596036 cites W1981723834 @default.
• W3213596036 cites W1988505764 @default.
• W3213596036 cites W1994616650 @default.
• W3213596036 cites W1995502176 @default.
• W3213596036 cites W1997867294 @default.
• W3213596036 cites W2001579955 @default.
• W3213596036 cites W2006555889 @default.
• W3213596036 cites W2006798482 @default.
• W3213596036 cites W2009127079 @default.
• W3213596036 cites W2014126048 @default.
• W3213596036 cites W2020105238 @default.
• W3213596036 cites W2023126268 @default.
• W3213596036 cites W2036216368 @default.
• W3213596036 cites W2036580231 @default.
• W3213596036 cites W2041076275 @default.
• W3213596036 cites W2042106612 @default.
• W3213596036 cites W2045689494 @default.
• W3213596036 cites W2045814754 @default.
• W3213596036 cites W2045938917 @default.
• W3213596036 cites W2047397174 @default.
• W3213596036 cites W2050050710 @default.
• W3213596036 cites W2053238375 @default.
• W3213596036 cites W2055093780 @default.
• W3213596036 cites W2056706729 @default.
• W3213596036 cites W2065720856 @default.
• W3213596036 cites W2066425650 @default.
• W3213596036 cites W2082114857 @default.
• W3213596036 cites W2089543834 @default.
• W3213596036 cites W2091565802 @default.
• W3213596036 cites W2096504654 @default.
• W3213596036 cites W2097796524 @default.
• W3213596036 cites W2103496339 @default.
• W3213596036 cites W2104730516 @default.
• W3213596036 cites W2105758800 @default.
• W3213596036 cites W2106711397 @default.
• W3213596036 cites W2109640889 @default.
• W3213596036 cites W2112651507 @default.
• W3213596036 cites W2115046037 @default.
• W3213596036 cites W2115424783 @default.
• W3213596036 cites W2117079884 @default.
• W3213596036 cites W2129492921 @default.
• W3213596036 cites W2129863797 @default.
• W3213596036 cites W2130308142 @default.
• W3213596036 cites W2134889984 @default.
• W3213596036 cites W2135234927 @default.
• W3213596036 cites W2137983211 @default.
• W3213596036 cites W2140380216 @default.
• W3213596036 cites W2142224528 @default.
• W3213596036 cites W2142428281 @default.
• W3213596036 cites W2146369320 @default.
• W3213596036 cites W2150535417 @default.
• W3213596036 cites W2155724501 @default.
• W3213596036 cites W2158513532 @default.
• W3213596036 cites W2164152021 @default.
• W3213596036 cites W2165861322 @default.
• W3213596036 cites W2167806727 @default.
• W3213596036 cites W2194775991 @default.
• W3213596036 cites W2270472693 @default.
• W3213596036 cites W2320012146 @default.
• W3213596036 cites W2328736443 @default.
• W3213596036 cites W2341165706 @default.
• W3213596036 cites W2528508328 @default.
• W3213596036 cites W2566491579 @default.
• W3213596036 cites W2789826932 @default.
• W3213596036 cites W2804893772 @default.
• W3213596036 cites W2897861898 @default.
• W3213596036 cites W2943909059 @default.
• W3213596036 cites W2962711206 @default.
• W3213596036 cites W2962786526 @default.
• W3213596036 cites W2963001155 @default.
• W3213596036 cites W2963009856 @default.
• W3213596036 cites W2964337106 @default.
• W3213596036 cites W2964791347 @default.
• W3213596036 cites W2972864268 @default.
• W3213596036 cites W2992833799 @default.
• W3213596036 cites W2999467926 @default.
• W3213596036 cites W3033405912 @default.
• W3213596036 cites W3033496312 @default.
• W3213596036 cites W3046206616 @default.
• W3213596036 cites W3047258103 @default.
• W3213596036 cites W3100238596 @default.
• W3213596036 cites W3103572865 @default.
• W3213596036 cites W3111862348 @default.
• W3213596036 cites W3116868211 @default.
• W3213596036 cites W3181621367 @default.

• next