Development of Model Based Pilot Controller for Automated Testing of Failure Scenarios in Automatic Flight Control System

This paper examines the Handling Quality Rating (HQR) of the Model-Based Pilot Controller (MBPC) in failure scenarios within the Automatic Flight Control System (AFCS). The MBPC aims to automate the testing of malfunctions in the AFCS of the T625 Gökbey platform. It is constructed using optimal control and estimation theory, with the cost function representing human characteristics determined by weighting matrices. The optimal values of weighting matrices that minimize the cost function are achieved via Genetic Algorithm. This algorithm utilized to systematically minimize user-defined cost functions tailored to optimize performance for selected maneuvers within the scope of ADS33E-PRF, considering user-defined constraints. Time-domain metric performance is provided for two maneuvers: vertical maneuver and hovering turn. The HQRs of the MBPC evaluated according to Power Frequency and Inceptor Peak Power-Phase (IPPP) metrics. The MBPC satisfies the ADS33 desired performance criteria in case of a failure scenario in AFCS throughout the maneuver, achieving satisfactory handling quality results.