NDLI: Adaptive estimation of nonlinear parameters of a nonholonomic spherical robot using a modified fuzzy-based speed gradient algorithm

Content Provider	Springer Nature Link
Author	Roozegar, Mehdi Mahjoob, Mohammad J. Ayati, Moosa
Copyright Year	2017
Abstract	This paper deals with adaptive estimation of the unknown parameters and states of a pendulum-driven spherical robot (PDSR), which is a nonlinear in parameters (NLP) chaotic system with parametric uncertainties. Firstly, the mathematical model of the robot is deduced by applying the Newton–Euler methodology for a system of rigid bodies. Then, based on the speed gradient (SG) algorithm, the states and unknown parameters of the robot are estimated online for different step length gains and initial conditions. The estimated parameters are updated adaptively according to the error between estimated and true state values. Since the errors of the estimated states and parameters as well as the convergence rates depend significantly on the value of step length gain, this gain should be chosen optimally. Hence, a heuristic fuzzy logic controller is employed to adjust the gain adaptively. Simulation results indicate that the proposed approach is highly encouraging for identification of this NLP chaotic system even if the initial conditions change and the uncertainties increase; therefore, it is reliable to be implemented on a real robot.
Starting Page	226
Ending Page	238
Page Count	13
File Format	PDF
ISSN	15603547
Journal	Regular and Chaotic Dynamics
Volume Number	22
Issue Number	3
e-ISSN	14684845
Language	English
Publisher	Pleiades Publishing
Publisher Date	2017-06-04
Publisher Place	Moscow
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	nonholonomic spherical robot adaptive estimation nonlinear in parameters speed gradient method fuzzy logic controller Newton–Euler strategy Dynamical Systems and Ergodic Theory
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Statistical and Nonlinear Physics Mathematical Physics Mechanical Engineering Modeling and Simulation

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