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• W1999471622 abstract "As a model is only an abstraction of the real system, unmodeled dynamics, parameter variations, and disturbances can result in poor performance of a conventional controller based on this model. In such cases, a conventional controller cannot remain well tuned. This paper presents the control of a spherical rolling robot by using an adaptive neuro-fuzzy controller in combination with a sliding-mode control (SMC)-theory-based learning algorithm. The proposed control structure consists of a neuro-fuzzy network and a conventional controller which is used to guarantee the asymptotic stability of the system in a compact space. The parameter updating rules of the neuro-fuzzy system using SMC theory are derived, and the stability of the learning is proven using a Lyapunov function. The simulation results show that the control scheme with the proposed SMC-theory-based learning algorithm is able to not only eliminate the steady-state error but also improve the transient response performance of the spherical rolling robot without knowing its dynamic equations." @default.
• W1999471622 created "2016-06-24" @default.
• W1999471622 creator A5046206126 @default.
• W1999471622 creator A5052916956 @default.
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• W1999471622 date "2013-02-01" @default.
• W1999471622 modified "2023-09-25" @default.
• W1999471622 title "Adaptive Neuro-Fuzzy Control of a Spherical Rolling Robot Using Sliding-Mode-Control-Theory-Based Online Learning Algorithm" @default.
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• W1999471622 doi "https://doi.org/10.1109/tsmcb.2012.2202900" @default.
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