Latest Articles for Control Systems

Examining formation control techniques for multiple agents using distance and bearing constraints.

Researchers develop a method for efficient robot control across different designs.

A new method allows autonomous vehicles to be controlled without real-world data.

New methods use graph theory to enhance structural controllability in switched systems.

A new approach enhances self-driving race car performance using real-time learning.

A new framework for improving safety and performance in hybrid robotic systems.

New Model Predictive Control approach enhances safety in robotic systems.

This article discusses how DPETS improves stability and control in reinforcement learning.

A look at control systems and the role of state estimation in technology management.

Learn how SMPC balances safety and performance in uncertain systems.

An overview of Distributionally Robust Model Predictive Control in uncertain environments.

A new method improves control for engine-driven ducted fan lift systems.

A method using deep reinforcement learning and reactive synthesis for controller design.

A new method enhances model training for better future predictions.

This article presents a method for optimizing PID controllers in balancing robots.

Exploring model-free control techniques under limited communication channels.

This paper presents a fresh approach to controlling large groups of systems.

New control methods improve stability for underactuated balance robots using Gaussian processes.

A look into challenges of making predictions in complex dynamical systems.

This research explores gain scheduling and neural operators for better control of nonlinear PDEs.

A study on controlling systems with Dirichlet boundary conditions for improved performance.

This article discusses strategies to control harmonic oscillators while minimizing energy use.

A new method enhances control of complex systems using data-driven techniques.

Exploring new methods to enhance Model Predictive Control with deep learning techniques.

A study on efficient reachability methods for autonomous systems.

A new method enhances robots' abilities to move efficiently on uneven surfaces.

Introducing a framework for safety filters that balances performance and safety in control systems.

Exploring two data-driven approaches to control systems.

This article introduces a new method to improve stability in real-time control systems.

Examining how agents coordinate without direct communication in decentralized systems.

Introducing innovative Koopman models designed for stability in system learning.

A method combining task prioritization and efficient computation for robotic control.

A look at how Wilcox and Fer expansions help solve differential equations.

Study proposes a benchmark for controlling autonomous vehicle groups effectively.

Using machine learning to enhance dynamic system control through Lie groups.

A new approach ensures tasks are completed within set time limits.

New RNN approach enhances motor speed response in electric vehicles and drones.

This study examines simulation's role in developing control policies for autonomous robots.

Discover how DDPMs enhance feedback control for nonlinear systems.

This method shows promise for applications in robotics and medicine.