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Related Experiment Video

Updated: Nov 5, 2025

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

A Swarm-Based Distributed Model Predictive Control Scheme for Autonomous Vehicle Formations in Uncertain

Antonio Bono, Giuseppe Fedele, Giuseppe Franze

IEEE Transactions on Cybernetics

|May 13, 2021

Summary

A new distributed model predictive control (MPC) architecture coordinates multiple vehicles in uncertain environments. This approach reduces sensor needs for long missions by combining swarm and leader-follower strategies, proving effective in lab tests.

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Last Updated: Nov 5, 2025

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Area of Science:

Robotics and Control Systems
Artificial Intelligence
Distributed Systems

Background:

Coordinating multiple vehicles in uncertain environments presents significant challenges, particularly concerning sensor limitations for long-range operations.
Existing control architectures often require extensive onboard sensing, limiting mission duration and scope.
The need for robust, decentralized control strategies that minimize reliance on external infrastructure or excessive onboard sensors is critical.

Purpose of the Study:

To propose a novel distributed model predictive control (MPC) architecture for multivehicle formation control.
To reduce the reliance on onboard sensors by integrating multiagent swarm and leader-follower configurations.
To formally prove the feasibility and stability of the proposed control scheme.

Main Methods:

Development of a distributed MPC architecture tailored for multivehicle formations.
Joint exploitation of multiagent swarm modeling and leader-follower configurations within an ad hoc MPC framework.
Formal mathematical proofs for the feasibility and asymptotic closed-loop stability of the control system.

Main Results:

The proposed distributed MPC architecture effectively coordinates multivehicle formations in uncertain environments.
The integrated approach significantly reduces the requirement for onboard sensors, crucial for long-range missions.
Laboratory experiments validated the algorithm's effectiveness, demonstrating robust follower capabilities, including blind operation.

Conclusions:

The novel distributed MPC architecture offers a viable solution for coordinated multivehicle control with reduced sensor dependency.
The formal proofs confirm the reliability and stability of the proposed control strategy.
The demonstrated blind operation capability highlights the system's adaptability to challenging, sensor-limited scenarios.