Optimization of ROV Control Based on Genetic Algorithm

Xu, Jiankang; Wang, Nan

doi:10.1109/oceanskobe.2018.8559384

Cited by 6 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The PID controller was chosen in this first study since it is a standard control scheme in the industry, commonly applied to every single degree of freedom of the ROV motion, and it is included in several studies related to the ROV depth control. 20–29 The main contributions of this paper are described as follows:…”

Section: Introductionmentioning

confidence: 99%

Station-keeping of a ROV under wave disturbance: Modeling and control design

Oliveira

Donha

Fleury

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part M:

View full text Add to dashboard Cite

Remotely Operated Vehicles (ROVs) working close to the sea surface are subjected to wave disturbances that affect their positioning. To treat this problem, we present a control scheme for the dynamic positioning of ROVs under wave disturbances and at low operation depths. The approach is composed of an Augmented Wave Filter (AWF) based on the Extended Kalman Filter (EKF) algorithm and an Adaptive-Model Predictive Control (A-MPC). The filter provides the optimal motion states and wave height estimation to the A-MPC, which calculates the control efforts based on the receding horizon approach. The scheme is tested through numerical simulations on the depth control of the Mandi II-ROV at the diving plane. The results are compared to those of a standard Proportional-Integral-Derivative (PID) controller. To perform realistic simulations, a detailed mathematical model is presented, considering the rigid body-motion, hydrodynamic and hydrostatic forces, thrusters dynamics, and onboard sensor measurements. The simulations are performed for different scenarios, considering the dynamic positioning in still waters and the station-keeping under wave disturbances for significant heights of 1 and 2 m. The controller robustness to parameter variation is assessed by using Monte-Carlo simulation. Results have shown improved performance for the A-MPC in terms of positioning errors, motion oscillations, and drift attenuation in comparison to a PID controller. The Monte-Carlo simulation reveals enhanced robustness to the A-MPC, which is associated with the greater variance for the control forces. Also, the filter performed very well in all the tests, producing an accurate estimate of the wave-induced height and providing an off-set free control.

show abstract

Section: Introductionmentioning

confidence: 99%

Station-keeping of a ROV under wave disturbance: Modeling and control design

Oliveira

Donha

Fleury

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part M:

View full text Add to dashboard Cite

show abstract

“…To confront intelligent jammers with environmental sense and learning capabilities, genetic algorithms [7], particle swarm algorithms [8], and artificial bee colony algorithms [9] have been used for making frequency, power, or coding action. However, such algorithms need different degrees of prior information, which limits their application in real scenarios.…”

Section: Introductionmentioning

confidence: 99%

A hidden anti-jamming method based on deep reinforcement learning

2021

KSII TIIS

View full text Add to dashboard Cite

In the field of anti-jamming based on dynamic spectrum, most methods try to improve the ability to avoid jamming and seldom consider whether the jammer would perceive the user's signal. Although these existing methods work in some anti-jamming scenarios, their long-term performance may be depressed when intelligent jammers can learn user's waveform or decision information from user's historical activities. Hence, we proposed a hidden anti-jamming method to address this problem by reducing the jammer's sense probability. In the proposed method, the action correlation between the user and the jammer is used to evaluate the hiding effect of the user's actions. And a deep reinforcement learning framework, including specific action correlation calculation and iteration learning algorithm, is designed to maximize the hiding and communication performance of the user synchronously. The simulation result shows that the algorithm proposed reduces the jammer's sense probability significantly and improves the user's anti-jamming performance slightly compared to the existing algorithms based on jamming avoidance.

show abstract