Comprehensive reconstructions and predictive control for quadrotor UAV information gathering tracking missions based...

Area of Science:

• Robotics
• Control Systems Engineering
• Aerospace Engineering

Background:

• Quadrotor unmanned aerial vehicles (UAVs) present complex control challenges due to under-actuation and hybrid constraints.
• Existing predictive control (PC) methods struggle with the inherent nonlinearities and interdependencies of quadrotor systems.
• Information-gathering missions require robust and efficient control strategies for UAVs.

Purpose of the Study:

• To develop a novel fully actuated system (FAS) predictive control (PC) framework for quadrotor UAVs.
• To integrate system model reconstruction, hybrid constraint handling, and performance index optimization into a unified control scheme.
• To enhance the real-time performance and stability of quadrotor UAVs during information-gathering missions.

Main Methods:

• Reconstructing the under-actuated quadrotor UAV model into a fully actuated system (FAS) model.
• Explicitly transforming and decoupling implicit hybrid constraints arising from model reconstruction.
• Developing a cascaded predictive algorithm that solves time-varying input constraints and decouples nonlinear optimization into linear convex problems.
• Ensuring closed-loop stability through careful selection of predictive parameters.

Main Results:

• Successfully injected full-actuation properties into the quadrotor UAV model.
• Significantly reduced computational demands by diminishing system complexities, nonlinearities, and interdependencies.
• Achieved satisfactory real-time performance through decoupled optimization problems.
• Validated the stability of the tracking error closed-loop system.

Conclusions:

• The proposed FAS-PC approach effectively addresses the control complexities of quadrotor UAVs for information gathering.
• The method demonstrates robust performance and stability, validated through simulations and practical flight missions.
• This novel framework offers a computationally efficient and reliable solution for advanced UAV control applications.