Nonlinear Decentralized Model Predictive Control for Unmanned Vehicles Moving in Formation

  • Andrea Monteriù Dipartimento di Ingegneria dell'Informazione Università Politecnica delle Marche
  • Alessandro Freddi Università degli Studi eCampus - Via Isimbardi 10, 22060 Novedrate (CO)
  • Sauro Longhi Università Politecnica delle Marche
Keywords: autonomous vehicles, cooperative control, model predictive control, decentralized systems.


Unmanned vehicles operating in formation may perform more complex tasks than vehicles working indi- vidually. In order to control a formation of unmanned vehicles, however, the following main issues must be faced: vehicle motion is usually described by nonlinear models, feasible control actions for each vehicle are constrained, collision between the members of the formation must be avoided while, at the same time, the computational efforts must be kept low due to limitations on the onboard hardware. To solve these problems, a nonlinear decentralized model predictive control algorithm is presented in this paper. The adopted model is based on the nonlinear kinematic equations describing the motion of a body with six degrees of freedom, where each vehicle shares information with its leader only by means of a wireless local area network. Saturation and collision-free constraints are included within the formulation of the optimization problem, while de- centralization allows to distribute the computational efforts amongst all the vehicles of the formation. In order to show the effectiveness of the proposed approach, it has been applied to a formation of quadrotor vehicles. Simulation results prove that the approach presented in this paper is a valid way to solve the problem of controlling a formation of unmanned vehicles, granting at the same time the possibility to deal with constraints and nonlinearity while limiting the computational efforts through decentralization.


Author Biography

Andrea Monteriù, Dipartimento di Ingegneria dell'Informazione Università Politecnica delle Marche
Andrea Monteriù received the Laurea Degree (joint BSc/MSc equivalent) summa cum laude in Electronic Engineering and the Ph.D. degree in Artificial Intelligence Systems from the Università Politecnica delle Marche (former Università degli Studi di Ancona), Ancona, Italy, in 2003 and 2006, respectively. His MSc thesis has been developed to the Automation Department of the Technical University of Denmark, Lyngby, Denmark. In 2005 he was a visiting researcher at the Center for Robot Assisted Search & Rescue of the University of South Florida, Tampa, Florida. Since 2005, he is Teaching Assistant of Automatic Control, Automation Systems, Industrial Automation, Modelling and Identification of Dynamic Processes. Since 2007, he has a PostDoc and Research Fellowship at the Dipartimento di Ingegneria dell’Informazione of the Università Politecnica delle Marche, where currently he is a Contract Professor. His research interests include fault diagnosis, fault handling, fault tolerant control, system and control theory, nonlinear dynamics and control, periodic systems, stochastic systems, guidance and control of autonomous systems, and applications in a variety of fields including aerospace, marine. He joined to various national and European projects on mobile robotics, ambient assisted living, smart cities.