Consensus Tracking for High-Order Uncertain Nonlinear MASs via Adaptive Backstepping Approach.

Area of Science:

• Control Theory
• Artificial Intelligence
• Systems Engineering

Background:

• Consensus control in nonlinear uncertain multiagent systems (MASs) is challenged by unknown state delays and external disturbances.
• Existing methods often struggle to address the combined effects of nonlinearity, delays, and uncertainties simultaneously.
• Adaptive function approximation is crucial for handling unknown system dynamics in MASs.

Purpose of the Study:

• To develop a robust consensus control strategy for nonlinear uncertain MASs with unknown state delays and external disturbances.
• To design adaptive control protocols that can compensate for system uncertainties and state delays.
• To ensure the synchronization of all agents in the MAS to a common reference signal.

Main Methods:

• Utilized adaptive radial basis function neural networks (RBFNNs) as nonlinear function approximators for system uncertainties.
• Employed Lyapunov-Krasovskii functionals (LKFs) to develop a compensation control strategy for state delays.
• Integrated RBFNNs, LKFs, and backstepping techniques to create an adaptive output-feedback control approach.

Main Results:

• Successfully designed consensus tracking control protocols and adaptive laws for nonlinear uncertain MASs.
• The proposed scheme effectively steers the MAS to synchronize with a predefined reference signal.
• Simulation results validated the theoretical approach, demonstrating its effectiveness in achieving consensus.

Conclusions:

• The developed adaptive output-feedback control strategy effectively addresses consensus control problems in nonlinear uncertain MASs.
• The combination of RBFNNs, LKFs, and backstepping provides a robust solution for systems with unknown delays and disturbances.
• The approach ensures reliable synchronization of multiagent systems, applicable in various complex dynamic environments.