Learning-based Nonlinear Model Predictive Control of Reconfigurable Autonomous Robotic Boats: Roboats

Erkan Kayacan, Shinkyu Park, Carlo Ratti, Daniela Rus

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations


This paper presents a Learning-based Nonlinear Model Predictive Control (LB-NMPC) algorithm for reconfigurable autonomous vessels to facilitate high-accurate path tracking. Each vessel is designed to latch to a pre-defined point of another vessel that allows the vessels to form a rigid body. The number of possible configurations of such vessels exponentially grows as the total number of vessels increases, which imposes a technical challenge in modeling and identification. In this work, we propose a framework consisting of a real-time parameter estimator and a feedback control strategy, which is capable of ensuring high-accurate path tracking for any feasible configuration of vessels. Novelty of our method is in that the parameter is estimated on-line and adjusts control parameters (e.g., cost function and dynamic model) simultaneously to improve path-tracking performance. Through experiments on different configurations of connected-vessels, we demonstrate stability of our proposed approach and its effectiveness in high-accuracy in path tracking.
Original languageEnglish (US)
Title of host publicationIEEE International Conference on Intelligent Robots and Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Print)9781728140049
StatePublished - Nov 1 2019
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2022-09-13


Dive into the research topics of 'Learning-based Nonlinear Model Predictive Control of Reconfigurable Autonomous Robotic Boats: Roboats'. Together they form a unique fingerprint.

Cite this