Neural dynamics and sliding mode integration for the guidance of unmanned surface vehicles

Lecce, N. Di; Laschi, Cecilia; Bibuli, Marco; Bruzzone, Gabriele; Zereik, Enrica

doi:10.1109/oceans-genova.2015.7271572

Cited by 5 publications

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This paper concentrates on estimating an aerodynamic parameter and process the trajectory data. In 2015, Lecce et al [74] analyzed the filtered data. The aerodynamic coefficients had been taken to compare with data obtained from the vehicle configuration that used the traditional UKF and ISPKF method.…”

Section: Kalman Filteringmentioning

confidence: 99%

Navigation and motion control techniques for surface unmanned vehicle and autonomous ground vehicle: a review

2021

View full text Add to dashboard Cite

An excellent navigation, guidance, and control (NGC) system had a high impact on trajectory tracking and the following scenarios. Both scenarios will include the heading, tangent, and velocity parameters in the computation. However, the control system design problem is not a new issue in the unmanned surface vehicle (USV) and autonomous ground vehivle (AGV) due to this constraint faced by many researchers since early these autonomy developments. Hence, this paper listed and emphasizing the techniques, including techniques implementation, strength, and the algorithm's constraints, a fusion of several techniques implemented for vehicle's stability, a turning ahead, and heading estimation. This paper concerns the similar algorithm used in the USV and AGV. Most of the selected techniques are basic algorithms and have been frequently implemented to control both vehicles' systems. Previous research shows pure pursuit guidance is the most popular technique in AGV to control the degree-of-freedom (DOF) velocity and the dynamic rate (sway, surge, and yaw). Simultaneously, the line of sight (LOS) controller is very compatible with controlling the movement of the USV. In conclusion, the technique's simulation test needs further research that will expose in the actual situation.

show abstract

Section: Kalman Filteringmentioning

confidence: 99%