GP-Based NMPC for Aerial Transportation of Suspended Loads

IF 4.6 2区 计算机科学 Q2 ROBOTICS
Fotis Panetsos;George C. Karras;Kostas J. Kyriakopoulos
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引用次数: 0

Abstract

In this work, we leverage Gaussian Processes (GPs) and present a learning-based control scheme for the transportation of cable-suspended loads with multirotors. Our ultimate goal is to approximate the model discrepancies that exist between the actual and nominal system dynamics. Towards this direction, weighted and sparse Gaussian Process (GP) regression is exploited so as to approximate online the model errors and guarantee real-time performance while also ensuring adaptability to the conditions prevailing in the outdoor environment where the multirotor is deployed. The learned model errors are fed into a nonlinear Model Predictive Controller (NMPC), formulated for the corrected system dynamics, which achieves the transportation of the multirotor towards reference positions with simultaneous minimization of the cable angular motion, regardless of the outdoor conditions and the existence of external disturbances, primarily stemming from the unknown wind. The proposed scheme is validated through simulations and real-world experiments with an octorotor, demonstrating an 80% reduction in the steady-state position error under 4 Beaufort wind conditions compared to the nominal NMPC.
基于gps的空中悬吊物运输NMPC
在这项工作中,我们利用高斯过程(GPs)并提出了一种基于学习的多转子悬索载荷运输控制方案。我们的最终目标是接近实际和名义系统动力学之间存在的模型差异。为此,利用加权稀疏高斯过程(GP)回归对模型误差进行在线逼近,保证了模型的实时性,同时保证了多旋翼对室外环境的适应性。学习到的模型误差被输入到一个非线性模型预测控制器(NMPC)中,该控制器是为校正后的系统动力学而制定的,它实现了多转子向参考位置的运输,同时最小化了电缆角运动,而不考虑室外条件和外部干扰的存在,主要是由未知风引起的。所提出的方案通过模拟和真实世界的转子实验进行了验证,表明与名义NMPC相比,4波弗特风条件下的稳态位置误差减少了80%。
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来源期刊
IEEE Robotics and Automation Letters
IEEE Robotics and Automation Letters Computer Science-Computer Science Applications
CiteScore
9.60
自引率
15.40%
发文量
1428
期刊介绍: The scope of this journal is to publish peer-reviewed articles that provide a timely and concise account of innovative research ideas and application results, reporting significant theoretical findings and application case studies in areas of robotics and automation.
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