Design, Modeling, and Nonlinear Model Predictive Tracking Control of a Novel Autonomous Surface Vehicle
Name
NMPCImplementation.pdf
Description
Accepted version
Size
2.47 MB
Format
Adobe PDF
Checksum (MD5)
6fba8d15c09c85303ccd2a56ff3321e5
Author(s)
Wang, Wei
Mateos, Luis A.
Park, Shinkyu
Leoni, Pietro
Gheneti, Banti
Duarte, Fabio
Ratti, Carlo
Rus, Daniela
Date Issued
May 2018
Publisher
IEEE
Citation
Wang, Wei, Mateos, Luis A., Park, Shinkyu, Leoni, Pietro, Gheneti, Banti et al. 2018. "Design, Modeling, and Nonlinear Model Predictive Tracking Control of a Novel Autonomous Surface Vehicle."
Version
Author's final manuscript
Abstract
© 2018 IEEE. In this paper, we present the design, modeling, and real-time nonlinear model predictive control (NMPC) of an autonomous robotic boat. The robot is easy to manufacture, highly maneuverable, and capable of accurate trajectory tracking in both indoor and outdoor environments. In particular, a cross type four-thruster configuration is proposed for the robotic boat to produce efficient holonomic motions. The robot prototype is rapidly 3D-printed and then sealed by adhering several layers of fiberglass. To achieve accurate tracking control, we formulate an NMPC strategy for the four-control-input boat with control input constraints, where the nonlinear dynamic model includes a Coriolis and centripetal matrix, the hydrodynamic added mass, and damping. By integrating 'GPS' modules and an inertial measurement unit (IMU) into the robot, we demonstrate accurate trajectory tracking of the robotic boat along preplanned paths in both a swimming pool and a natural river. Furthermore, the code generation strategy employed in our paper yields a two order of magnitude improvement in the run time of the NMPC algorithm compared to similar systems. The robot is designed to form the basis for surface swarm robotics testbeds, on which collective algorithms for surface transportation and self-assembly of dynamic floating infrastructures can be assessed.
MIT Department
Senseable City Laboratory
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/icra.2018.8460632
