Decentralized tube‐based model predictive control of uncertain nonlinear multiagent systems. (12th March 2019)

• Record Type: Journal Article
• Authors: Nikou, Alexandros; Dimarogonas, Dimos V.
• Is Part Of: International journal of robust and nonlinear control. Volume 29:Number 10(2019)
• Page Start: 2799
• Page End: 2818
• Publication Date: 2019-03-12
• Subjects: connectivity maintenance -- multiagent systems -- nonlinear model predictive control (NMPC) -- robust control -- tube‐based MPC; Automatic control -- Periodicals; Control theory -- Periodicals; Nonlinear systems -- Periodicals; 629.836
• DOI: 10.1002/rnc.4522
• Languages: English
• ISSNs: 1049-8923

Abstract

Summary: This paper addresses the problem of decentralized tube‐based nonlinear model predictive control (NMPC) for a general class of uncertain nonlinear continuous‐time multiagent systems with additive and bounded disturbance. In particular, the problem of robust navigation of a multiagent system to predefined states of the workspace while using only local information is addressed under certain distance and control input constraints. We propose a decentralized feedback control protocol that consists of two terms: a nominal control input, which is computed online and is the outcome of a decentralized finite horizon optimal control problem that each agent solves at every sampling time, for its nominal system dynamics; and an additive state‐feedback law which is computed offline and guarantees that the real trajectories of each agent will belong to a hypertube centered along the nominal trajectory, for all times. The volume of the hypertube depends on the upper bound of the disturbances as well as the bounds of the derivatives of the dynamics. In addition, by introducing certain distance constraints, the proposed scheme guarantees that the initially connected agents remain connected for all times. Under standard assumptions that arise in nominal NMPC schemes, controllability assumptions, communication capabilities between the agents, it is guaranteed that the multiagent system is input‐to‐state stable with respect to the disturbances, for all initial conditions satisfying the state constraints. Simulation results verify the correctness of the proposed framework.