Policy‐Regulated Model Predictive Control for Path Tracking by Quadcopter for Real‐Time Implementation

Quadcopters are four‐motor robots that rely on the thrust generated by their motors to move. Thanks to their maneuverability, vertical takeoff and landing, and hover capabilities, they have become increasingly popular. Due to this popularity, over the years, significant advancements have been made in quadcopter maneuvers from a control perspective. With the objective of real‐time implementation of a model predictive controller (MPC) by reducing the time required for generating control signals, this research investigates the design of a controller for quadcopters to track complicated paths and contributes to regulating the predictive controller's cost. To this end, two methods are tailored: data‐driven regulation based on empirical tests and physics‐based regulation by incorporating human intuition. The developed controller is put to the test with various disturbances and parameter uncertainties, and stability analysis is conducted. A regulated predictive controller achieves high levels of accuracy in real‐time tracking complicated paths, whereas a standard predictive controller fails while reducing energy consumption, as demonstrated in test results.