Practical Real-Time Implementation of a Disturbance Rejection Control Scheme for a Twin-Rotor Helicopter System Using Intelligent Active Force Control

This paper centers around an experimental investigation into the effectiveness of an innovative hybrid control approach based on an intelligent active force control (IAFC) strategy to stabilize a twin-rotor helicopter model and improve its ability to reject external disturbances efficiently. The intelligent algorithm was based on an iterative learning (IL) method integrated into the main control loop to estimate control parameters automatically while on-line. A mechatronic test rig with the IAFC-based control algorithm was incorporated into a Quanser Aero twin-rotor model in a laboratory setting as a verification platform to evaluate the applicability and efficacy of the proposed control algorithm via a practical real-time implementation. The hybrid IAFC-based control design was rigorously examined to test its feasibility and durability in countering various forms of external disturbances while executing the trajectory tracking tasks. Notably, the efficiency of the IAFC-based control unit was mainly studied and compared with other control plans under different operating conditions for benchmarking. The experimental results show the ability of the controller based on the IAFC strategy to effectively improve the disturbance rejection capability compared to the other control schemes considered in the study. About 27% improvement of the system performance in terms of lowering the root mean square error (RMSE) was observed compared to the other control systems counterparts.

Automated summary

This paper presents an experimental investigation on the effectiveness of an innovative hybrid control approach utilizing an intelligent active force control (IAFC) strategy to stabilize a twin-rotor helicopter model and improve its disturbance rejection capability. The IAFC-based control design was rigorously tested and showed a 27% improvement in system performance in terms of lowering the root mean square error (RMSE) compared to other control schemes.