Event-Based Prescribed-Time Output Regulation of Uncertain Nonlinear Multiagent Systems

Area of Science:

• Control Theory
• Systems Engineering
• Artificial Intelligence

Background:

• Multiagent systems (MASs) face challenges in output regulation due to uncertainties and limited communication.
• Prescribed-time control offers finite-time convergence but is sensitive to communication constraints.
• Event-triggered control strategies aim to reduce communication load by transmitting data only when necessary.

Purpose of the Study:

• To develop an event-based distributed neuro-adaptive prescribed-time control scheme for uncertain nonlinear MASs.
• To address the challenges posed by limited communication resources in achieving prescribed-time output regulation.
• To ensure system states remain bounded and achieve output regulation within a user-defined time.

Main Methods:

• Construction of distributed prescribed-time observers for state estimation without prior knowledge of exosystem dynamics.
• Implementation of a dynamic event-triggered mechanism (DETM) to ensure positive lower bounds on interexecution intervals.
• Derivation of neuro-adaptive prescribed-time controllers for each agent to maintain system states within a compact set.

Main Results:

• Observation errors converge to a small neighborhood around zero within a user-determined time.
• The DETM effectively reduces communication demands while guaranteeing positive interexecution intervals.
• Regulated outputs converge to a user-tuned region within a prescribed time, with all signals remaining bounded.
• Zeno behavior is successfully eliminated.

Conclusions:

• The proposed event-based distributed neuro-adaptive prescribed-time control scheme effectively addresses the output regulation problem for uncertain nonlinear MASs under communication constraints.
• The method ensures finite-time convergence and conserves communication resources, validated by simulation examples.