Fuzzy Model Predictive Control for Unmanned Helicopter

Unmanned helicopters, due to their agility and strong dependence on environmental conditions, require using advanced control techniques in order to ensure precise trajectory tracking in various states of flight. The following paper presents a methodology for the design of an unmanned helicopter flight controller. The proposed solution involves the use of the Model Predictive Control framework enhanced with the Takagi–Sugeno inference algorithm. The designed system uses a Parallel Distributed Compensation architecture and utilizes multiple linear dynamics models to precisely model the helicopter’s response in transitioning from hovering to forward flight. The proposed control system was developed for the ARCHER unmanned rotorcraft, which was designed at Warsaw University of Technology. In order to evaluate control efficiency, simulation tests were conducted using the helicopter mathematical model built in the FLIGHTLAB environment, fully integrated with the Matlab/Simulink platform. The control system test results, including system step responses and performance during flight over a predefined path, highlight the differences between the conventional Model Predictive Control regulator and its fuzzy-enhanced variant.

Abstract licence: CC BY

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