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

Abstract

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.
Pages8230-8237
Number of pages8
ISBN (Print)9781728140049
DOIs
StatePublished - Nov 1 2019
Externally publishedYes

Bibliographical note

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

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