Robust servo linear quadratic regulator controller based on state compensation and velocity feedforward of the spherical robot: Theory and experimental verification

Wang, Yixu; Liu, Yifan; Guan, Xinping; Hu, Tao; Zhang, Ziang; Wang, You; Hao, Jie; Li, Guang

doi:10.1177/17298806231153229

Cited by 5 publications

(2 citation statements)

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“…[27], and a robust servo LQR controller based on state compensation and velocity feedforward for direction control in Ref. [28], which enabled motion control with higher precision and lower energy consumption. In this paper, we refine algorithms based on Refs.…”

Section: Motion Controlmentioning

confidence: 99%

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Spherical robot: A novel robot for exploration in harsh unknown environments

Ren,

Wang,

Liu

et al. 2023

IET Cyber-Syst and Robotics

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The authors propose a complete software and hardware framework for a novel spherical robot to cope with exploration in harsh and unknown environments. The proposed robot is driven by a heavy pendulum covered by a fully enclosed spherical shell, which is strongly protected, amphibious, anti‐overturn and has a long‐battery‐life. Algorithms for location and perception, planning and motion control are comprehensively designed. On the one hand, the authors fully consider the kinematic model of a spherical robot, propose a positioning algorithm that fuses data from inertial measurement units, motor encoder and Global Navigation Satellite System, improve global path planning algorithm based on Hybrid A* and design an instruction planning controller based on model predictive control (MPC). On the other hand, the dynamic model is built, linear MPC and robust servo linear quadratic regulator algorithm is improved, and a speed controller and a direction controller are designed. In addition, based on the pose and motion characteristics of a spherical robot, a visual obstacle perception algorithm and an electronic image stabilisation algorithm are designed. Finally, the authors build physical systems to verify the effectiveness of the above algorithms through experiments.

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Section: Motion Controlmentioning

confidence: 99%

“…Dynamic model is built up by Euler‐Lagrange method [23, 27, 28]. The definitions of the symbols are shown in Tables 1 and 7, and Euler‐Lagrange equations can be written as Equation ().…”

Section: Motion Controlmentioning

confidence: 99%