Adaptive Neural Consensus Tracking Control for Nonlinear Multiagent Systems Using Integral Barrier Lyapunov...

Area of Science:

• Control Systems Engineering
• Nonlinear Dynamics
• Networked Systems

Background:

• Multiagent systems often face challenges with state constraints and communication errors.
• Existing barrier Lyapunov functions can be overly conservative, limiting their applicability.
• Adaptive control is crucial for handling uncertainties in nonlinear systems.

Purpose of the Study:

• To develop an adaptive tracking control scheme for nonlinear multiagent systems with state constraints.
• To introduce integral barrier Lyapunov functionals (iBLFs) to relax conservatism and handle state constraints.
• To ensure follower systems track the leader's trajectory while maintaining state variable bounds.

Main Methods:

• Utilized integral barrier Lyapunov functionals (iBLFs) to address state constraints and coupling errors.
• Designed an adaptive distributed controller using the backstepping method and iBLF differentiation via the integral mean value theorem.
• Employed neural networks to approximate unknown system terms and Lyapunov stability theory for system analysis.

Main Results:

• The proposed control scheme successfully ensures that all follower outputs track the leader's output trajectory.
• State variables of the agents remain within the predefined constraint bounds.
• All closed-loop signals within the multiagent system are proven to be bounded.

Conclusions:

• The novel adaptive tracking control scheme effectively manages nonlinear multiagent systems with state constraints.
• The integral barrier Lyapunov functional approach offers a less conservative and more feasible solution compared to traditional methods.
• The controller's efficiency is validated, demonstrating its practical applicability in complex networked systems.