Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
PI Controller: Design01:24

PI Controller: Design

Proportional Integral (PI) controllers are a fundamental component in modern control systems, widely used to enhance performance and mitigate steady-state errors. They are particularly effective in applications such as automatic brightness adjustment on smartphones, where they excel at mitigating steady-state errors for step-function inputs. Unlike PD controllers, which require time-varying errors to function optimally, PI controllers leverage their integral component to address residual...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Development of a new valid and reliable microsurgical skill assessment scale for ophthalmology residents.

BMC ophthalmology·2018
Same author

Transcriptome Profiling of Neovascularized Corneas Reveals miR-204 as a Multi-target Biotherapy Deliverable by rAAVs.

Molecular therapy. Nucleic acids·2018
Same author

Different patterns of myopia prevalence and progression between internal migrant and local resident school children in Shanghai, China: a 2-year cohort study.

BMC ophthalmology·2018
Same author

Defect Sites-Rich Porous Carbon with Pseudocapacitive Behaviors as an Ultrafast and Long-Term Cycling Anode for Sodium-Ion Batteries.

ACS applied materials & interfaces·2018
Same author

Distribution of Anterior Chamber Parameters in Normal Chinese Children and the Associated Factors.

Journal of glaucoma·2018
Same author

Bph6 encodes an exocyst-localized protein and confers broad resistance to planthoppers in rice.

Nature genetics·2018

Related Experiment Video

Updated: Jul 8, 2026

Utilizing vmTracking to Improve the Accuracy of Multi-Animal Pose Estimation in Rodent Social Behavior Studies
07:34

Utilizing vmTracking to Improve the Accuracy of Multi-Animal Pose Estimation in Rodent Social Behavior Studies

Published on: November 7, 2025

Research on robot path tracking method based on IDDPG-MPC.

Haicheng Shen1,2, Xun Xu3, Zhiqiang Miao1

  • 1School of Yonyou Digital and Intelligence, Nantong Institute of Technology, Nantong, Jiangsu, China.

Plos One
|July 6, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a hybrid control system for unmanned surface vessels (USVs) using deep reinforcement learning and model predictive control. The novel approach enhances path-following accuracy in complex marine environments, significantly reducing errors.

Related Experiment Videos

Last Updated: Jul 8, 2026

Utilizing vmTracking to Improve the Accuracy of Multi-Animal Pose Estimation in Rodent Social Behavior Studies
07:34

Utilizing vmTracking to Improve the Accuracy of Multi-Animal Pose Estimation in Rodent Social Behavior Studies

Published on: November 7, 2025

Area of Science:

  • Marine robotics
  • Control systems engineering
  • Artificial intelligence in navigation

Background:

  • Unmanned Surface Vessels (USVs) face challenges in path-following control due to environmental disturbances and system nonlinearities.
  • Traditional control methods like Line-of-Sight/Proportional-Integral-Derivative (LOS/PID) lack robustness and adaptability in complex marine settings.
  • Underactuated systems and dynamic nonlinearities further complicate precise navigation for USVs.

Purpose of the Study:

  • To develop a hybrid control architecture for enhanced path-following control of USVs in complex marine environments.
  • To overcome the limitations of traditional control strategies by combining deep reinforcement learning and model predictive control.
  • To achieve high-precision path tracking for autonomous surface vehicle navigation.

Main Methods:

  • Proposed a hybrid control architecture combining Improved Deep Deterministic Policy Gradient (IDDPG) and Model Predictive Control (MPC).
  • IDDPG as the upper-level module uses deep reinforcement learning to determine optimal heading angle increments based on environmental states.
  • MPC as the lower-level module optimizes thrust and rudder angle using a USV dynamics model via rolling optimization.
  • Implemented a closed-loop 'perception-decision-execution-learning' paradigm with gradient pruning and a custom reward function for stable training and optimal decisions.

Main Results:

  • The hybrid IDDPG-MPC approach demonstrated effective adaptability in complex environments, surpassing traditional methods.
  • Achieved a 37% reduction in average lateral deviation and a 21% reduction in heading angle error compared to the ALOS-PID method.
  • Validated high-precision path tracking control for unmanned surface vessels through simulation.

Conclusions:

  • The proposed hybrid IDDPG-MPC control strategy offers a robust and adaptive solution for USV path-following in challenging marine conditions.
  • This approach provides a novel method for autonomous surface vehicle navigation, improving accuracy and reliability.
  • The study highlights the potential of integrating deep reinforcement learning and MPC for advanced marine robotic control.