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WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

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Centralized and distributed model predictive control for consensus of non-linear multi-agent systems with

Isa Ravanshadi¹, Elham Amini Boroujeni¹, Mahdi Pourgholi²

¹Department of Electrical and Computer Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran.

ISA Transactions

|July 9, 2022

Summary

This study presents new model predictive control (MPC) methods for multi-agent systems to achieve consensus, even with obstacles. These approaches simplify design and ensure stability and convergence for complex systems.

Keywords:

Consensus Contractive constraint Model predictive control Multi-agent Non-linear systems Obstacle avoidance

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Area of Science:

Robotics
Control Theory
Artificial Intelligence

Background:

Multi-agent systems require robust control for coordinated behavior.
Model Predictive Control (MPC) is effective but often complex to design, especially for nonlinear systems with constraints.
Achieving consensus in multi-agent systems with obstacles presents significant control challenges.

Purpose of the Study:

To develop and evaluate centralized and distributed Model Predictive Control (MPC) methods for nonlinear multi-agent systems.
To address the consensus problem in the presence of fixed and time-varying obstacles.
To simplify MPC design by eliminating the need for terminal ingredients, enhancing applicability to higher-order systems.

Main Methods:

Centralized and distributed Model Predictive Control (MPC) strategies were implemented.
The distributed MPC was executed using both serial and parallel approaches.
Contractive constraints were applied to guarantee system stability and agent convergence.
Obstacles were integrated as constraints within the optimization control problem.

Main Results:

The proposed MPC methods successfully achieved consensus in nonlinear multi-agent systems.
Stability and convergence of agents to a consensus point were guaranteed.
The necessity of designing complex terminal ingredients for MPC was eliminated.
Simulations demonstrated the effectiveness of both centralized and distributed methods for wheeled mobile robots.

Conclusions:

The developed centralized and distributed MPC methods offer a simplified yet effective approach to solving the consensus problem for nonlinear multi-agent systems.
These methods are robust to fixed and time-varying obstacles and are suitable for higher-order systems.
The elimination of terminal ingredients reduces design complexity, making MPC more accessible for advanced applications.