@article{GENERAL,
      recid = {11138},
      author = {Menghini, M and Mallipeddi, SK and Castaldi, P and De  Marchi, L},
      title = {Neuro Adaptive Integral Sliding mode Control based on  Composite Learning for Path Following of Underactuated  Underwater Vehicle : Blucy},
      journal = {Conference Proceedings of iSCSS},
      address = {2024-11-07},
      number = {GENERAL},
      abstract = {This paper presents an application of an intelligent  control system for an underactuated underwater vehicle  called Blucy, developed for non-invasive underwater  monitoring. During the monitoring, it is crucial to  maintain an attitude towards the target and stay on the  survey's path to improve the collected data quality, this  highlights the need for developing a sophisticated guidance  and control system. Specifically, intelligent controls  demonstrate adaptability in challenging environments, such  as sea current disturbances, and handle both unmodelled and  nonlinear dynamics of the system, like the presence of the  fiber optic cable during remotely operated inspections. In  this work, a robust path-following algorithm is developed.  A line-of-sight guidance is used to tackle the problem of  under actuation, which outputs a virtual input to the  control system. A composite error learning based  methodology is implemented to design a control system. An  integral sliding mode control that uses a radial basis  neural network to estimate the unmodelled dynamics and  uncertainties is developed. In addition, a disturbance  observer is designed to approximate the external noise and  the error made by the neural network. Furthermore, a state  estimator is implemented whose error, along with the  tracking error made by the controller, is used in training  the neural network and disturbance observer, regarded as  so-called composite error learning, which enhances the  learning process, making the controller more robust against  disturbances and uncertainties. The efficiency and  performance of the proposed control methodology in  following the desired path are studied through simulation.},
      url = {http://library.imarest.org/record/11138},
      doi = {https://doi.org/10.24868/11138},
}