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Adaptive Neuro-Fuzzy Control of a Spherical Rolling Robot Using Sliding-Mode-Control-Theory-Based Online Learning Algorithm
- Source :
- IEEE Transactions on Cybernetics, vol. 43, no. 1, pp. 170-179, Feb. 2013
- Publication Year :
- 2021
-
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.
Details
- Database :
- arXiv
- Journal :
- IEEE Transactions on Cybernetics, vol. 43, no. 1, pp. 170-179, Feb. 2013
- Publication Type :
- Report
- Accession number :
- edsarx.2104.07160
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1109/TSMCB.2012.2202900.