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Article

Optimizing Unmanned Air–Ground Vehicle Maneuvers Using Nonlinear Model Predictive Control and Moving Horizon Estimation

by
Alessandra Elisa Sindi Morando
1,2,*,
Alessandro Bozzi
1,
Simone Graffione
1,
Roberto Sacile
1 and
Enrico Zero
1
1
Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genova, 16100 Genova, Italy
2
Heudiasyc (Heuristics and Diagnosis of Complex Systems) CNRS Laboratory of the Université de Technologie de Compiègne, 60200 Compiègne, France
*
Author to whom correspondence should be addressed.
Automation 2024, 5(3), 324-342; https://doi.org/10.3390/automation5030020
Submission received: 11 June 2024 / Revised: 15 July 2024 / Accepted: 25 July 2024 / Published: 30 July 2024
(This article belongs to the Special Issue Model Predictive Control: Advances in Sensor Technologies and Applications)
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Abstract

In this paper, Nonlinear Model Predictive Control (NMPC) and Nonlinear Moving Horizon Estimator (NMHE) are combined to control, in a distributed way, a heterogeneous fleet composed of a steering car and a quadcopter. In particular, the ground vehicle in the role of the leader communicates its one-step future position to the drone, which keeps the formation along the desired trajectory. Inequality constraints are introduced in a switching control fashion to the leader’s NMPC formulation to avoid obstacles. In the literature, few works using NMPC and NMHE deal with these two vehicles together. Moreover, the presented scheme can tackle noisy, partial, and missing measurements of the agents’ state. Results show that the ground car can avoid detected obstacles, keeping the tracking errors of both robots in the order of a few centimeters, thanks to trustworthy NMHE estimates and NMPC predictions.
Keywords: unmanned ground vehicle; unmanned aerial vehicle; nonlinear model predictive control; nonlinear moving horizon estimation unmanned ground vehicle; unmanned aerial vehicle; nonlinear model predictive control; nonlinear moving horizon estimation

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MDPI and ACS Style

Morando, A.E.S.; Bozzi, A.; Graffione, S.; Sacile, R.; Zero, E. Optimizing Unmanned Air–Ground Vehicle Maneuvers Using Nonlinear Model Predictive Control and Moving Horizon Estimation. Automation 2024, 5, 324-342. https://doi.org/10.3390/automation5030020

AMA Style

Morando AES, Bozzi A, Graffione S, Sacile R, Zero E. Optimizing Unmanned Air–Ground Vehicle Maneuvers Using Nonlinear Model Predictive Control and Moving Horizon Estimation. Automation. 2024; 5(3):324-342. https://doi.org/10.3390/automation5030020

Chicago/Turabian Style

Morando, Alessandra Elisa Sindi, Alessandro Bozzi, Simone Graffione, Roberto Sacile, and Enrico Zero. 2024. "Optimizing Unmanned Air–Ground Vehicle Maneuvers Using Nonlinear Model Predictive Control and Moving Horizon Estimation" Automation 5, no. 3: 324-342. https://doi.org/10.3390/automation5030020

APA Style

Morando, A. E. S., Bozzi, A., Graffione, S., Sacile, R., & Zero, E. (2024). Optimizing Unmanned Air–Ground Vehicle Maneuvers Using Nonlinear Model Predictive Control and Moving Horizon Estimation. Automation, 5(3), 324-342. https://doi.org/10.3390/automation5030020

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