Observer-Based Event-Triggered Adaptive Control for Nonlinear Multiagent Systems With Unknown States and...

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Networked Systems

Background:

• Consensus tracking in nonlinear semi-strict-feedback multiagent systems is challenging due to unknown states and disturbances.
• Existing control methods often require frequent controller updates, consuming significant communication resources.
• Event-triggered control strategies are needed to optimize communication in such systems.

Purpose of the Study:

• To develop an adaptive event-triggered control scheme for nonlinear semi-strict-feedback multiagent systems.
• To address the challenges of unknown states and external disturbances.
• To reduce controller update frequency and conserve communication resources.

Main Methods:

• Utilized radial basis function neural networks (RBF NNs) and backstepping techniques.
• Developed a joint construction of state observer, disturbance observer, and a first-order filter.
• Implemented an adaptive event-triggered control strategy.

Main Results:

• Achieved uniform tracking of the leader's reference signal by all followers' output signals.
• Ensured all error signals remain uniformly bounded.
• Demonstrated reduced controller update frequency through the event-triggered mechanism.

Conclusions:

• The proposed adaptive event-triggered control scheme effectively solves the consensus tracking problem for the considered multiagent systems.
• The method conserves communication resources by minimizing controller updates.
• Simulation results validate the control scheme's effectiveness in handling unknown states and disturbances.