Print Email Facebook Twitter Adaptive predictive path following control based on least squares support vector machines for underactuated autonomous vessels Title Adaptive predictive path following control based on least squares support vector machines for underactuated autonomous vessels Author Liu, Chenguang (Wuhan University of Technology) Zheng, Huarong (Zhejiang University) Negenborn, R.R. (TU Delft Transport Engineering and Logistics) Chu, Xiumin (Wuhan University of Technology) Xie, Shuo (Wuhan University of Technology) Date 2019 Abstract Since vessel dynamics could vary during maneuvering because of load changes, speed changing, environmental disturbances, aging of mechanism, etc., the performance of model-based path following control may be degraded if the controller uses the same motion model all the time. This article proposes an adaptive path following control method based on least squares support vector machines (LS-SVM) to deal with parameter changes of the motion model. The path following controller consists of two components: the online identification of varying parameters and model predictive control (MPC) using the adaptively identified models. For the online parameter identification, an improved online LS-SVM identification method is proposed based on weighted LS-SVM. Specifically, the objective function of LS-SVM is modified to decrease the errors of parameter estimation, an index is proposed to detect the possible model changes, which speeds up the rate of parameter convergence, and the sliding data window strategy is used to realize the online identification. MPC is combined with the line-of-sight guidance to track straight line reference paths. Finally, case studies are conducted to verify the effectiveness of the proposed path following adaptive controller. Typical parameter varying scenarios, such as rudder aging, current variations and changes of the maneuverability are considered. Simulation results show that the proposed method can handle the above situations effectively. Subject autonomous surface vessels (ASV)least squares support vector machines (LS-SVM)model predictive control (MPC)parameter identificationpath following To reference this document use: http://resolver.tudelft.nl/uuid:a3084af7-029c-4044-8c3f-759c010e6e03 DOI https://doi.org/10.1002/asjc.2208 Embargo date 2020-11-20 ISSN 1561-8625 Source Asian Journal of Control, 2021 (23) (1) Bibliographical note Accepted Author Manuscript Part of collection Institutional Repository Document type journal article Rights © 2019 Chenguang Liu, Huarong Zheng, R.R. Negenborn, Xiumin Chu, Shuo Xie Files PDF paper.pdf 1.42 MB Close viewer /islandora/object/uuid:a3084af7-029c-4044-8c3f-759c010e6e03/datastream/OBJ/view