Path following with an underactuated self-balancing spherical-wheel mobile robot

Inal, Ali Nail; Morgül, Ömer; Saranlı, Uluç

doi:10.1109/icar.2015.7251455

Cited by 2 publications

(1 citation statement)

References 22 publications

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“…System Description of the Two-Wheeled Self-Balancing Mobile Robot System. As shown in Figure 1, the mechanical part of the two-wheeled self-balancing mobile robot [23][24][25] basically consists of two wheels and a body. The parameters of the two wheels of the robot are the same in terms of mass, diameter and rotational inertia; additionally, these wheels are coaxially installed and independently driven.…”

Section: Two-wheeled Self-balancing Mobilementioning

confidence: 99%

Open‐Closed‐Loop PD Iterative Learning Control with a Variable Forgetting Factor for a Two‐Wheeled Self‐Balancing Mobile Robot

Dong

Zhang

et al. 2019

Complexity

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A novel iterative learning control (ILC) algorithm for a two-wheeled self-balancing mobile robot with time-varying, nonlinear, and strong-coupling dynamics properties is presented to resolve the trajectory tracking problem in this research. A kinematics model and dynamic model of a two-wheeled self-balancing mobile robot are deduced in this paper, and the combination of an open-closed-loop PD-ILC law and a variable forgetting factor is presented. The open-closed-loop PD-ILC algorithm adopts current and past learning items to drive the state variables and input variables, and the output variables converge to the bounded scope of their desired values. In addition, introducing a variable forgetting factor can enhance the robustness and stability of ILC. Numerous simulation and experimental data demonstrate that the proposed control scheme has better feasibility and effectiveness than the traditional control algorithm.

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Section: Two-wheeled Self-balancing Mobilementioning

confidence: 99%