Model-Based Predictive Control for Position and Orientation Tracking in a Multilayer Architecture for a Three-Wheeled Omnidirectional Mobile Robot
This paper presents the design and implementation of a Model-based Predictive Control (MPC) strategy integrated within a modular multilayer architecture for a three-wheeled omnidirectional mobile robot, the Robotino 4 from Festo. The implemented architecture is organized into three hierarchical laye...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:dnet:digitalcsic_::a3461b596845cf88554d7317fc25dcc0 |
| Acceso en línea: | http://hdl.handle.net/10261/427834 https://api.elsevier.com/content/abstract/scopus_id/105009252338 |
| Access Level: | acceso abierto |
| Palabra clave: | Model-based predictive control Motion control Multilayer architecture Position and orientation tracking Three-wheeled omnidirectional mobile robot |
| Sumario: | This paper presents the design and implementation of a Model-based Predictive Control (MPC) strategy integrated within a modular multilayer architecture for a three-wheeled omnidirectional mobile robot, the Robotino 4 from Festo. The implemented architecture is organized into three hierarchical layers to support modularity and system scalability. The upper layer is responsible for trajectory planning. This planned trajectory is forwarded to the intermediate layer, where the MPC computes the optimal velocity commands to follow the reference path, taking into account the kinematic model and actuator constraints of the robot. Finally, these velocity commands are processed by the lower layer, which uses three independent PID controllers to regulate the individual wheel speeds. To evaluate the proposed control scheme, it was implemented in MATLAB R2024a using a lemniscate trajectory as the reference. The MPC problem was formulated as a quadratic optimization problem that considered the three states: the global position coordinates and orientation angle. The simulation included state estimation errors and motor dynamics, which were experimentally identified to closely match real-world behavior. The simulation and experimental results demonstrate the capability of the MPC to track the lemniscate trajectory efficiently. Notably, the close agreement between the simulated and experimental results validated the fidelity of the simulation model. In a real-world scenario, the MPC controller enabled simultaneous regulation of both the position and orientation, which offered a greater performance compared with approaches that assume a constant orientation. |
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