Distributed optimal consensus of nonlinear multi-agent systems under intermittent communication networks.

Area of Science:

• Control Systems Engineering
• Networked Systems
• Optimization Theory

Background:

• Achieving consensus in multi-agent systems is challenging, especially with intermittent communication and partial observability.
• High-order nonlinear systems require advanced control strategies for stable and optimal coordination.
• Distributed control architectures are crucial for scalability and robustness in complex networks.

Purpose of the Study:

• To develop distributed sliding mode controllers for optimal consensus in high-order nonlinear multi-agent systems.
• To address challenges posed by intermittent communication networks and partial observability constraints.
• To ensure tracking error convergence and stability within a cooperative optimization framework.

Main Methods:

• Design of distributed sliding mode controllers.
• Utilizing adaptive state observers for state estimation under partial observability.
• Employing a distributed optimization algorithm for cost function minimization.
• Application of Hermite interpolation to handle non-existent high-order derivatives.

Main Results:

• The proposed controllers ensure that each agent's tracking error converges to zero.
• Stability analysis confirms the boundedness of the closed-loop distributed cooperative optimization framework.
• Simulation results validate the effectiveness of the developed control strategy.

Conclusions:

• The developed distributed sliding mode controllers effectively solve the optimal consensus problem for high-order nonlinear multi-agent systems with intermittent communication.
• The integration of adaptive observers and distributed optimization provides a robust solution under challenging network conditions.
• The proposed framework demonstrates practical applicability and enhances cooperative optimization in networked systems.