A Control Engineer's Guide to the Active Disturbance Rejection Control: A Case of Study in a Mecanum-Wheeled Mobile Robot

Mecanum-wheeled mobile robots enable omnidirectional motion, allowing for enhanced motion and benefiting applications like surveillance, exploration, and transport. However, strong susceptibility to slippage and vibrations cause several positioning errors. Therefore, advanced control techniques still require attention. This work presents a practical guide using a quasi-model-free control approach based on the active disturbance rejection framework, supported by theoretical background. The experimental results show that, even with minimal knowledge of model parameters, the quasi-modelfree control approach achieves motion accuracy improvements compared with a proportional-derivative controller ranging for 49% to 60%.

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Published
2025-08-25
DOI
https://doi.org/10.1109/ccta53793.2025.11151488
Primary Topic
Robotic Path Planning Algorithms
Type
article
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article

A Control Engineer's Guide to the Active Disturbance Rejection Control: A Case of Study in a Mecanum-Wheeled Mobile Robot

David I. Rosas Almeida, José Carlos Ortiz Hernández
Robotic Path Planning Algorithms
article

A Control Engineer's Guide to the Active Disturbance Rejection Control: A Case of Study in a Mecanum-Wheeled Mobile Robot

David I. Rosas Almeida, José Carlos Ortiz Hernández
article en

Abstract

Mecanum-wheeled mobile robots enable omnidirectional motion, allowing for enhanced motion and benefiting applications like surveillance, exploration, and transport. However, strong susceptibility to slippage and vibrations cause several positioning errors. Therefore, advanced control techniques still require attention. This work presents a practical guide using a quasi-model-free control approach based on the active disturbance rejection framework, supported by theoretical background. The experimental results show that, even with minimal knowledge of model parameters, the quasi-modelfree control approach achieves motion accuracy improvements compared with a proportional-derivative controller ranging for 49% to 60%.

Mexicali Institute of Technology (MX)
Openalex Percentile: Top 76%
Robotic Path Planning Algorithms
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