Model Predictive Control for Helicopter Flight Control

Abstract

Model predictive control is an optimal, model-based control method that has the powerful capability of directly including input and output constraints. Next to this, it is known that helicopters are hard to fly with its complex, unstable and highly coupled dynamics. With the introduction of the concept of handling qualities, guidelines for helicopter and flight control system design were set in the ADS-33 document to improve the ease of controlling rotorcraft. In order to improve helicopter handling qualities, this paper investigates whether linear and nonlinear MPC are suitable for online application to helicopters to reduce cross-coupling effects. This was investigated by evaluating its performance on the cross-coupling requirements of the ADS-33 handling quality document. It was found that both linear and nonlinear MPC are very effective to reduce cross-coupling effects even when disturbances or prediction model errors are present. The model predictive controller could reduce the off-axis coupling response by around 99% compared to the uncontrolled helicopter. Furthermore, it performed 90% to 99% better than a PID controller in most coupling cases.