Cascaded Model Predictive Control of a Tandem-Rotor Helicopter

TL;DR

This paper proposes a cascaded MPC approach for tandem-rotor helicopters, splitting control into outer and inner loops to enhance performance and reduce computational load, validated through simulations.

Contribution

It introduces a novel cascaded MPC structure with separate loops for translational and rotational control, improving efficiency and robustness over traditional single MPC methods.

Findings

Significant improvements in position and velocity tracking.

Reduced computational resources compared to single MPC.

Robustness to model uncertainty and environmental disturbances.

Abstract

This letter considers cascaded model predictive control (MPC) as a computationally lightweight method for controlling a tandem-rotor helicopter. A traditional single MPC structure is split into separate outer and inner-loops. The outer-loop MPC uses an $SE_2(3)$ error to linearize the translational dynamics about a reference trajectory. The inner-loop MPC uses the optimal angular velocity sequence of the outer-loop MPC to linearize the rotational dynamics. The outer-loop MPC is run at a slower rate than the inner-loop allowing for longer prediction time and improved performance. Monte-Carlo simulations demonstrate robustness to model uncertainty and environmental disturbances. The proposed control structure is benchmarked against a single MPC algorithm where it shows significant improvements in position and velocity tracking while using significantly less computational resources.