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Adaptive Stabilization for a Class of Stochastic Nonlinearly Parameterized Nonholonomic Systems with Unknown Control Coefficients
Author(s) -
Gao Fangzheng,
Yuan Fushun,
Wu Yuqiang
Publication year - 2014
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1002/asjc.814
Subject(s) - backstepping , nonholonomic system , control theory (sociology) , parameterized complexity , integrator , controller (irrigation) , adaptive control , state (computer science) , class (philosophy) , computer science , mathematics , control (management) , robot , algorithm , mobile robot , artificial intelligence , computer network , bandwidth (computing) , agronomy , biology
This paper investigates the problem of adaptive state feedback stabilization for a class of stochastic nonlinearly parameterized nonholonomic systems in chained form with unknown control coefficients. By defining two new unknown parameters whose dynamic updating laws are properly chosen and also by skilfully using the parameter separation, input‐state‐scaling, and integrator backstepping techniques, an adaptive state feedback controller is successfully designed, which guarantees that the closed‐loop system is asymptotically stabilized in probability. A simulation example is provided to illustrate the effectiveness of the proposed approach.