Arom Moreno-Ortiz, Daniela Sánchez-Orozco, Luis López-Estrada, C. Tutivén, Y. Vidal, Marcelo Fajardo-Pruna
{"title":"Modelling of an Intelligent Control Strategy for an Autonomous Sailboat - SenSailor","authors":"Arom Moreno-Ortiz, Daniela Sánchez-Orozco, Luis López-Estrada, C. Tutivén, Y. Vidal, Marcelo Fajardo-Pruna","doi":"10.1109/IC_ASET53395.2022.9765928","DOIUrl":null,"url":null,"abstract":"This paper studies the mathematical model required to implement an intelligent control system for the autonomous sailboat SenSailor Drone. This work presents the required equations and defines the hardware configuration and interactions between sensors and actuators in the system to be mounted. The proposed model was developed in Python, and it is feasible to interact with open source tools of machine learning. The generated trajectories will be used as input trajectories to train a Neural Network that identifies a plant model and gives an optimal controller for the desired behaviors.","PeriodicalId":6874,"journal":{"name":"2022 5th International Conference on Advanced Systems and Emergent Technologies (IC_ASET)","volume":"4 1","pages":"34-38"},"PeriodicalIF":0.0000,"publicationDate":"2022-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 5th International Conference on Advanced Systems and Emergent Technologies (IC_ASET)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IC_ASET53395.2022.9765928","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This paper studies the mathematical model required to implement an intelligent control system for the autonomous sailboat SenSailor Drone. This work presents the required equations and defines the hardware configuration and interactions between sensors and actuators in the system to be mounted. The proposed model was developed in Python, and it is feasible to interact with open source tools of machine learning. The generated trajectories will be used as input trajectories to train a Neural Network that identifies a plant model and gives an optimal controller for the desired behaviors.