An adaptive learning controller for MIMO uncertain feedback linearizable nonlinear systems

Most of available results in adaptive learning controllers (ALCs) with input learning technique have considered the single-input single-output nonlinear systems. This paper presents an ALC for MIMO uncertain feedback linearizable systems whose uncertainty is in their linear parameters. Since only an...

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Bibliographic Details
Published in:Nonlinear dynamics 2015-04, Vol.80 (1-2), p.999-1016
Main Authors: Kim, Minsung, Kuc, Tae-Yong, Kim, Hyosin, Wi, Seok-min, Lee, Jin S.
Format: Article
Language:English
Subjects:
Adaptive control
Adaptive control systems
Adaptive systems
Automotive Engineering
Classical Mechanics
Control
Control systems
Control theory
Dynamical Systems
Engineering
Equations of state
Feedback
High gain
Learning
Mechanical Engineering
MIMO (control systems)
Nonlinear dynamics
Nonlinear systems
Original Paper
Parameter uncertainty
SISO (control systems)
Tracking errors
Vibration
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Summary:Most of available results in adaptive learning controllers (ALCs) with input learning technique have considered the single-input single-output nonlinear systems. This paper presents an ALC for MIMO uncertain feedback linearizable systems whose uncertainty is in their linear parameters. Since only an output signal is available for measurement, a high gain observer is used to estimate the unmeasurable state. The estimated state is then utilized to implement the ALC. The proposed ALC learns the input gain parameters of the state equation as well as the internal parameters. In addition, the desired input is also learned using an input learning rule to track the whole command history. In the proposed ALC, the tracking errors are bounded and the mean-square tracking error is O ( ϵ ) as the task is repeated. Single-link and two-link manipulators are presented as simulation examples to confirm the feasibility and the performance of the proposed ALC.
ISSN:0924-090X
1573-269X
DOI:10.1007/s11071-015-1924-5