Robust ADP-based solution of a class of nonlinear multi-agent systems with input saturation and collision avoidance...

Area of Science:

• Control Engineering
• Nonlinear Systems Analysis
• Artificial Intelligence in Control

Background:

• Investigating robust tracking control for nonlinear systems presents challenges due to external disturbances, input saturation, and collision avoidance requirements.
• Existing control methods may struggle to simultaneously address these complex constraints within a unified framework.

Purpose of the Study:

• To develop a robust tracking control strategy for nonlinear systems subject to input saturation and collision avoidance constraints under external disturbances.
• To propose an adaptive dynamic programming (ADP) based technique that estimates optimal control laws and disturbance bounds without prior system dynamics knowledge.

Main Methods:

• A novel adaptive dynamic programming (ADP) algorithm is presented, utilizing neural networks for function approximation.
• The method estimates optimal cost functions, control policies, and the worst-case disturbance scenario.
• Lyapunov theory is employed to rigorously analyze the convergence and stability of the ADP estimations.

Main Results:

• The developed ADP method successfully generates optimal control laws for nonlinear systems with complex constraints.
• Neural networks effectively estimate system states, optimal policies, and disturbance bounds.
• Simulation results validate the efficacy and robustness of the proposed adaptive dynamic programming approach.

Conclusions:

• The proposed ADP technique offers an effective solution for robust tracking control in challenging nonlinear systems.
• The method's ability to handle multiple constraints and unknown dynamics makes it broadly applicable.
• This work demonstrates the power of ADP combined with neural networks for advanced control problems.