Modeling and adaptive tracking for a class of stochastic Lagrangian control systems

This paper focuses on the problem of modeling and adaptive tracking for a class of stochastic Lagrangian control systems with unknown parameters. By reasonably introducing random noise, a method to construct stochastic Lagrangian control systems is given. Under some milder assumptions, an adaptive t...

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Bibliographic Details
Published in:Automatica (Oxford) 2013-03, Vol.49 (3), p.770-779
Main Authors: Cui, Ming-Yue, Wu, Zhao-Jing, Xie, Xue-Jun, Shi, Peng
Format: Article
Language:English
Subjects:
Adaptive control systems
Adaptive tracking
Applied sciences
Computer science
control theory
systems
Control system analysis
Control system synthesis
Control systems
Control theory. Systems
Design parameters
Exact sciences and technology
Fundamental areas of phenomenology (including applications)
Mean square values
Mechanics model
Physics
Solid mechanics
Stochastic Lagrangian control systems
Stochasticity
Structural and continuum mechanics
Tracking
Tracking errors
Tuning
Vibration, mechanical wave, dynamic stability (aeroelasticity, vibration control...)
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Summary:This paper focuses on the problem of modeling and adaptive tracking for a class of stochastic Lagrangian control systems with unknown parameters. By reasonably introducing random noise, a method to construct stochastic Lagrangian control systems is given. Under some milder assumptions, an adaptive tracking controller is designed such that the mean square of the tracking error converges to an arbitrarily small neighborhood of zero by tuning design parameters. The reasonability of assumptions and the efficiency of the controller are demonstrated by a mechanics model in random vibration environment.
ISSN:0005-1098
1873-2836
DOI:10.1016/j.automatica.2012.11.013