Open AccessStudy of a MEMS Vibratory Gyroscope Using Adaptive Iterative Learning Control
Author(s) -
Xiaochun Lu,
Juntao Fei
Publication year - 2014
Publication title -
international journal of advanced robotic systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.394
H-Index - 46
eISSN - 1729-8814
pISSN - 1729-8806
DOI - 10.5772/58933
Subject(s) - iterative learning control , gyroscope , control theory (sociology) , vibrating structure gyroscope , computer science , robustness (evolution) , parametric statistics , convergence (economics) , lyapunov stability , adaptive control , trajectory , stability (learning theory) , control engineering , artificial intelligence , control (management) , mathematics , machine learning , engineering , biochemistry , chemistry , statistics , physics , astronomy , economic growth , economics , gene , aerospace engineering
This paper proposes a framework, namely adaptive iterative learning control (AILC), which is used in the control of a microelectromechanical system (MEMS) gyroscope, to realize high-precision trajectory tracking control. According to the characteristics of the MEMS gyroscope's model, the proposed AILC algorithm includes an adaptive law of parametric estimation and an iteration control law, which is updated in the iterative domain without any prior knowledge of MEMS gyroscopes. The convergence of the method is proven by a Lyapunov-like approach, which shows that the designed controller can guarantee the stability of the system and make the output tracking errors to converge completely to zero while the iteration index tends to infinity. By comparing AILC and traditional PD-ILC, the simulation results demonstrate the effectiveness of AILC and its robustness against external random disturbance
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