A Single-ball-driven Self-Balancing Robot Controller Based on Genetic Algorithm Optimization

Gao, Tianyu; Jin, Jun; Hao, Xiao

doi:10.1145/3421766.3421789

Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture 2020

DOI: 10.1145/3421766.3421789

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A Single-ball-driven Self-Balancing Robot Controller Based on Genetic Algorithm Optimization

Tianyu Gao

Jun Jin

Xiao Hao

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2022

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State feedback based on grey wolf optimizer controller for two-wheeled self-balancing robot

Jasim

2022

Journal of Intelligent Systems

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The two-wheeled self-balancing robot (TWSBR) is based on the axletree and inverted pendulum. Its balancing problem requires a control action. To speed up the response of the robot and minimize the steady state error, in this article, a grey wolf optimizer (GWO) method is proposed for TWSBR control based on state space feedback control technique. The controller stabilizes the balancing robot and minimizes the overshoot value of the system. The dynamic model of the system is derived based on Euler formula and linearized to state space representation to enhance the control technique. Then, the GWO optimizes the state feedback controller parameters. Simulation results show that the system reaches the zero steady-state error with less than 2 ms, which proves the effectiveness of the proposed controller over the classical state feedback controller in terms of fast response, very small overall error, and minimum overshoot.

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State feedback based on grey wolf optimizer controller for two-wheeled self-balancing robot

Jasim

2022

Journal of Intelligent Systems

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show abstract

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A Single-ball-driven Self-Balancing Robot Controller Based on Genetic Algorithm Optimization

Cited by 1 publication

References 3 publications

State feedback based on grey wolf optimizer controller for two-wheeled self-balancing robot

State feedback based on grey wolf optimizer controller for two-wheeled self-balancing robot

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