Observer-based prescribed-time optimal neural consensus control for six-rotor UAVs: A novel actor-critic...

Area of Science:

• Robotics and Control Systems
• Artificial Intelligence
• Aerospace Engineering

Background:

• Six-rotor unmanned aerial vehicles (UAVs) face challenges in resource allocation within dynamic environments.
• Achieving optimal consensus control with unified prescribed performance remains a significant hurdle.

Purpose of the Study:

• To address the prescribed-time observer-based optimal consensus control problem for six-rotor UAVs.
• To develop an efficient resource allocation strategy with unified prescribed performance.

Main Methods:

• A prescribed-time optimal control scheme combining prescribed-time control with a simplified reinforcement learning framework.
• Novel updating laws for actor and critic neural networks using a prescribed-time adjustment function.
• An improved distributed prescribed-time observer for leader information estimation.
• Nonlinear transformations and mappings for unified performance requirements.
• An adaptive prescribed-time filter to manage complexity.

Main Results:

• Six-rotor UAVs achieve a desired steady state within a prescribed time.
• Follower UAVs accurately estimate leader's velocity and position within a prescribed time.
• The unified control framework simplifies performance requirement satisfaction and implementation.
• Global performance requirement simplifies constraint verification.
• Filter error converges within the prescribed time.

Conclusions:

• The proposed method effectively solves the optimal consensus control problem for six-rotor UAVs under unified prescribed performance.
• The approach enhances resource allocation efficiency and control system user-friendliness.
• Simulation results validate the effectiveness and robustness of the designed control scheme.