Distributed Model-Free Sliding-Mode Predictive Control of Discrete-Time Second-Order Nonlinear Multiagent Systems...

Area of Science:

• Control Systems Engineering
• Robotics and Automation
• Networked Systems

Background:

• Multiagent systems (MASs) are crucial for complex tasks but face challenges in coordination and control.
• Networked systems introduce complexities like communication delays that degrade performance.
• Accurate system modeling is often difficult, necessitating model-free approaches.

Purpose of the Study:

• To develop a distributed model-free sliding-mode control algorithm for discrete-time second-order nonlinear MASs without communication delay.
• To propose a distributed model-free sliding-mode predictive control strategy for MASs with communication delay, incorporating time-delay compensation.
• To ensure system stability and consensus tracking performance in networked MASs under various conditions.

Main Methods:

• A novel distributed model-free sliding-mode control algorithm is proposed for systems without delay.
• A distributed model-free sliding-mode predictive control strategy is developed for systems with delay, utilizing time-delay compensation.
• Sliding-mode control principles are adapted for model-free applications in networked MASs.

Main Results:

• The proposed algorithm ensures rapid system convergence without requiring an accurate system model.
• The predictive control strategy effectively compensates for time delays, maintaining system stability.
• Both simulation and experimental results validate the superiority and effectiveness of the developed control methods.

Conclusions:

• The developed model-free control strategies are effective for networked discrete-time second-order nonlinear MASs.
• The methods provide robust consensus tracking performance, even in the presence of communication delays.
• The proposed approaches offer a practical solution for controlling complex multiagent systems where accurate models are unavailable.