Model Predictive Control Based Washout Algorithm Design for Flight Simulator Upset Prevention and Recovery Training

Abstract

To migrate Loss of Control In-flight, the number one cause of aviation fatalities, pilots need to undergo upset prevention and recovery training with flight simulators. The fidelity of a moving base flight simulator is greatly dependent on the washout algorithm of the Stewart platform, which may reach the workspace limits when simulating the aircraft recovery from upset conditions. In this paper, a washout algorithm optimal design method based on the model predictive control technique is proposed for flight simulator upset prevention and recovery training. The parameters of the washout algorithm are calculated directly based on the platform model, and the system limits are explicitly taken into account. The human perception model is incorporated into the optimization problem, for which the objective is to minimize the pilot’s perceived motion mismatch between the real flight and the simulator training. Simulations are conducted and compared with the classical filter-based washout algorithm. Responses of the flight simulator model show that the proposed method can improve the motion cueing effect when the aircraft is in upset conditions.