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

Second Order systems II01:18

Second Order systems II

173
In an underdamped second-order system, where the damping ratio ζ is between 0 and 1, a unit-step input results in a transfer function that, when transformed using the inverse Laplace method, reveals the output response. The output exhibits a damped sinusoidal oscillation, and the difference between the input and output is termed the error signal. This error signal also demonstrates damped oscillatory behavior. Eventually, as the system reaches a steady state, the error diminishes to zero.
173
Second Order systems I01:20

Second Order systems I

237
A servo system exemplifies a second-order system, featuring a proportional controller and load elements that ensure the output position aligns with the input position. The relationship between these components is described by a second-order differential equation. Applying the Laplace transform under zero initial conditions yields the transfer function, showing how inputs are converted to outputs in the system.
By reinterpreting the system, one can derive the closed-loop transfer function, which...
237
One-Degree-of-Freedom System01:24

One-Degree-of-Freedom System

559
In mechanical engineering, one-degree-of-freedom systems form the basis of a wide range of electrical and mechanical components. Using these models, engineers can predict the behavior of various parts in a larger system, which gives them insight into how different forces interact with each other.
A one-degree-of-freedom system is defined by an independent variable that determines its state and behavior. One example of a one-degree-of-freedom system is a simple harmonic oscillator, such as a...
559
Two-Dimensional Force System: Problem Solving01:29

Two-Dimensional Force System: Problem Solving

679
Solving problems related to two-dimensional force systems is an essential aspect of mechanics and engineering. By applying the principles of vector analysis and force equilibrium, one can determine the effect of multiple forces acting on an object in a two-dimensional space.
The first step to solving a two-dimensional force system problem is to draw a free-body diagram of the object under consideration. This diagram helps identify all the external forces acting on the object, including their...
679
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

451
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...
451
Kinematic Equations: Problem Solving01:15

Kinematic Equations: Problem Solving

14.8K
When analyzing one-dimensional motion with constant acceleration, the problem-solving strategy involves identifying the known quantities and choosing the appropriate kinematic equations to solve for the unknowns. Either one or two kinematic equations are needed to solve for the unknowns, depending on the known and unknown quantities. Generally, the number of equations required is the same as the number of unknown quantities in the given example. Two-body pursuit problems always require two...
14.8K

You might also read

Related Articles

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

Sort by
Same author

Collision-Free Coordination of Heterogeneous Multiagent Systems Under Independent DoS Attacks.

IEEE transactions on cybernetics·2026
Same author

DeepGene: An Efficient Foundation Model for Genomics Based on Pan-Genome Graph Transformer.

IEEE transactions on computational biology and bioinformatics·2025
Same author

CF-DTI: coarse-to-fine feature extraction for enhanced drug-target interaction prediction.

Health information science and systems·2025
Same author

Obstacle Avoidance Control for Multiagent Systems Based on Model-Free Adaptive Control.

IEEE transactions on cybernetics·2025
Same author

CTFNet: Long-Sequence Time-Series Forecasting Based on Convolution and Time-Frequency Analysis.

IEEE transactions on neural networks and learning systems·2023
Same author

Small RNA Sequencing Analysis of STZ-Injured Pancreas Reveals Novel MicroRNA and Transfer RNA-Derived RNA with Biomarker Potential for Diabetes Mellitus.

International journal of molecular sciences·2023
Same journal

Hybrid vehicle state estimation using closed-form liquid neural networks and nonlinear Kalman filtering.

ISA transactions·2026
Same journal

Cross-coupled synchronization control strategy for rebar binding robots based on impedance control.

ISA transactions·2026
Same journal

Gas flow tracking for electronic pressure control system in gas chromatography under state constraints and hysteresis：An innovative fuzzy adaptive control approach.

ISA transactions·2026
Same journal

Stackelberg differential game-based fuzzy adaptive hierarchical optimal control for a nonlinear system with unknown dynamics.

ISA transactions·2026
Same journal

Composite fault-tolerant predictive control strategy for PMSM demagnetization faults.

ISA transactions·2026
Same journal

Bias-compensated Q-learning for optimal tracking control under denial-of-service attacks.

ISA transactions·2026
See all related articles

Related Experiment Video

Updated: Sep 15, 2025

Operation of the Collaborative Composite Manufacturing CCM System
10:09

Operation of the Collaborative Composite Manufacturing CCM System

Published on: October 1, 2019

6.7K

Collision-free formation trajectory tracking control for second-order multi-agent systems by PPC method.

Liqiu Zhu1, Yining Qian2, An-Yang Lu3

  • 1College of Information Science and Engineering, Northeastern University, Shenyang 110819, China.

ISA Transactions
|July 17, 2025
PubMed
Summary
This summary is machine-generated.

This study ensures multi-agent systems safely track formation trajectories and maintain communication. It prevents collisions between agents and with obstacles using prescribed performance control and potential functions.

Keywords:
Collision avoidanceFormation trajectory trackingMulti-agent systemsPrescribed performance control

More Related Videos

A Protocol for Real-time 3D Single Particle Tracking
10:16

A Protocol for Real-time 3D Single Particle Tracking

Published on: January 3, 2018

15.0K
Image-based Lagrangian Particle Tracking in Bed-load Experiments
10:32

Image-based Lagrangian Particle Tracking in Bed-load Experiments

Published on: July 20, 2017

9.1K

Related Experiment Videos

Last Updated: Sep 15, 2025

Operation of the Collaborative Composite Manufacturing CCM System
10:09

Operation of the Collaborative Composite Manufacturing CCM System

Published on: October 1, 2019

6.7K
A Protocol for Real-time 3D Single Particle Tracking
10:16

A Protocol for Real-time 3D Single Particle Tracking

Published on: January 3, 2018

15.0K
Image-based Lagrangian Particle Tracking in Bed-load Experiments
10:32

Image-based Lagrangian Particle Tracking in Bed-load Experiments

Published on: July 20, 2017

9.1K

Area of Science:

  • Robotics
  • Control Systems
  • Artificial Intelligence

Background:

  • Multi-agent systems require precise formation control and continuous communication.
  • Ensuring safety necessitates preventing collisions among agents and with environmental obstacles.

Purpose of the Study:

  • To develop a control strategy for multi-agent systems that achieves accurate formation trajectory tracking.
  • To maintain reliable communication links between agents.
  • To guarantee collision avoidance for both inter-agent and agent-obstacle scenarios.

Main Methods:

  • Prescribed Performance Control (PPC) method to design expected velocities for trajectory tracking and collision avoidance.
  • Introduction of a continuous potential function to generate an obstacle avoidance term within the expected velocity.
  • Development of a control algorithm for individual agents to adjust their velocities according to the designed expected velocity.

Main Results:

  • The proposed method enables agents to accurately follow a designated formation trajectory.
  • Effective prevention of collisions between agents.
  • Successful avoidance of obstacles in the operational environment.
  • Sustained communication links among the agents throughout the operation.

Conclusions:

  • The developed control strategy effectively addresses formation trajectory tracking and communication maintenance in multi-agent systems.
  • The integration of PPC and potential functions ensures comprehensive safety by mitigating collision risks.
  • Simulation results validate the robustness and efficacy of the proposed approach for practical applications.