Cooperative Adaptive Dynamic Surface Control for a Class of High-Order Stochastic Nonlinear Multiagent Systems

Area of Science:

• Control Systems Engineering
• Nonlinear Systems Analysis
• Stochastic Processes

Background:

• Consensus tracking is crucial for coordinated behavior in multiagent systems (MASs).
• High-order stochastic pure-feedback nonlinear MASs with dead zones present significant control challenges.
• Existing methods often struggle with the complexities of power-exponential control laws and dead zone nonlinearities.

Purpose of the Study:

• To develop a robust control strategy for consensus tracking in complex MASs.
• To address the challenges posed by dead zones and stochastic disturbances.
• To overcome the
• explosion of complexity
• inherent in high-order systems.

Main Methods:

• Employing the adding a power integrator technique to handle power-exponential functions and dead zones.
• Utilizing radial basis function neural networks for estimating unknown nonlinearities.
• Implementing tracking differentiators to mitigate computational complexity from virtual controller differentiation.

Main Results:

• Demonstrated semiglobal uniform ultimate boundedness in probability for all closed-loop system signals.
• Achieved convergence of tracking errors to a small neighborhood around the origin.
• Validated the proposed approach through simulation results.

Conclusions:

• The proposed control strategy effectively achieves consensus tracking in high-order stochastic pure-feedback nonlinear MASs with dead zones.
• The method provides a robust solution for complex systems with nonlinearities and uncertainties.
• Simulation results confirm the practical applicability and effectiveness of the developed control technique.