Prescribed Performance Cooperative Control for Multiagent Systems With Input Quantization.

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Networked Systems

Background:

• Multiagent systems (MAS) face challenges in cooperative control due to unknown system parameters and communication constraints.
• Prescribed performance control aims to guarantee that system states remain within specified bounds in finite time.

Purpose of the Study:

• To develop a quantized cooperative control strategy for multiagent systems with unknown gains and prescribed performance.
• To address the limitations of finite-time control by introducing a speed function for faster error convergence.

Main Methods:

• Design of a speed function to achieve finite-time convergence of tracking errors to a prescribed set.
• Utilization of a lemma and Nussbaum function to handle unknown gains and input quantization in cooperative control.
• Application of fuzzy logic systems (FLS) for approximating unknown nonlinear functions.
• Construction of a distributed controller and adaptive laws using Lyapunov stability theory and the backstepping method.

Main Results:

• The proposed distributed controller ensures that tracking errors converge to a prescribed compact set within a finite time.
• The control strategy effectively handles unknown gains and input quantization inherent in multiagent systems.
• Numerical simulations validate the effectiveness and robustness of the developed quantized cooperative control approach.

Conclusions:

• The study presents a novel and effective quantized cooperative control method for multiagent systems with unknown gains.
• The proposed approach achieves prescribed performance, demonstrating faster convergence than traditional finite-time control methods.
• The integration of fuzzy logic systems and adaptive control provides a robust solution for complex networked systems.