Consensus Maneuvering for a Class of Nonlinear Multivehicle Systems in Strict-Feedback Form

Area of Science:

• Control Systems Engineering
• Robotics
• Nonlinear Systems Theory

Background:

• Investigates consensus maneuvering for nonlinear multivehicle systems.
• Addresses challenges in strict-feedback systems with uncertain nonlinearities.
• Defines consensus maneuvering through geometric (path convergence) and dynamic (desired assignment) tasks.

Purpose of the Study:

• Develop a modular consensus maneuvering controller for nonlinear multivehicle systems.
• Ensure follower vehicles converge to a parameterized path and satisfy desired dynamics.
• Analyze system stability using input-to-state and cascade theories.

Main Methods:

• Employs a modular design approach: estimator module (echo state network), controller module (modified dynamic surface control with nonlinear tracking differentiator), and path update law.
• Utilizes distributed maneuvering error feedback and a filtering scheme.
• Leverages input-to-state stability and cascade theory for stability analysis.

Main Results:

• A distributed consensus maneuvering controller is developed for uncertain nonlinear strict-feedback systems.
• The controller effectively estimates uncertain nonlinearities and ensures path convergence and dynamic assignment.
• Simulation results validate the controller's efficacy.

Conclusions:

• The proposed modular controller enables effective consensus maneuvering in complex nonlinear systems.
• The distributed nature of the controller enhances scalability and practicality.
• The stability analysis confirms the robustness of the proposed control strategy.