Intelligent active force control of a helicopter seat suspension using iterative learning algorithm

Abstract

The high level of noise and vibrations in helicopters is not preventable and happens through flight operations. This high level of vibrations can produce uneasiness and may affect aircrew performance and their health. Correspondingly, their concentration on flight operation and decision making is strongly depended to comfort ability. Therefore, vibration attenuation can improve flight control, and aircrews feel better conditions. In this study, the helicopter structure was modeled in ANSYS software and natural frequencies have been obtained. The seat suspension and pilot body were modeled by Lumped modeling method. The active force control (AFC) scheme hybridized by Iterative learning (IL) to determine the estimated mass called AFCIL was used in helicopter seat suspension system to reduce the vibrations transmitted to the pilot body. The simulation was performed with sinusoidal and random disturbance signals and results demonstrated in both the time and frequency domains. Attained results were compared with the passive system, PID controller and AFCANN schemes. The AFCIL scheme had superior performance in pilot head displacement reduction compared to the classical PID controller. The results of the AFCIL and the AFCANN were similar together while AFCIL results were marginally superior to AFCANN.