The main purpose of the presented paper is to evaluate the performance of an automatic flight control system for an unmanned helicopter, using model predictive control (MPC) enhanced with recursive least squares (RLS) model parameter estimation.
The proposed control system integrates a stability augmentation system in the inner loop and an MPC-based system in the outer loop, which iteratively solves a constrained optimization problem through quadratic programming. Simulation tests are conducted using the unmanned helicopter mathematical model built in the Flightlab environment, fully integrated with the Matlab/Simulink platform.
Simulation tests compare the performance of the MPC with and without RLS parameter estimation during a level change maneuver, position changes along the X and Y axes, or flight over a predefined trajectory. The results provide insights into the limitations of the proposed algorithms and suggest potential improvements for the automatic flight control system.
The test results can guide the implementation of MPC algorithms for future unmanned helicopter control systems. In addition, the research highlights the impact of model disturbances within the MPC-based control loop on helicopter dynamics.
This paper introduces an integrated control system and discusses the potential advantages of using RLS algorithm in predictive control systems.
