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Novel morphing wing actuator control-based Particle Swarm Optimisation

  • S. Khan (a1), T. L. Grigorie (a1) (a2), R. M. Botez (a1), M. Mamou (a3) and Y. Mébarki (a3)...

Abstract

The paper presents the design and experimental testing of the control system used in a new morphing wing application with a full-scaled portion of a real wing. The morphing actuation system uses four similar miniature brushless DC (BLDC) motors placed inside the wing, which execute a direct actuation of the flexible upper surface of the wing made from composite materials. The control system of each actuator uses three control loops (current, speed and position) characterised by five control gains. To tune the control gains, the Particle Swarm Optimisation (PSO) method is used. The application of the PSO method supposed the development of a MATLAB/Simulink® software model for the controlled actuator, which worked together with a software sub-routine implementing the PSO algorithm to find the best values for the five control gains that minimise the cost function. Once the best values of the control gains are established, the software model of the controlled actuator is numerically simulated in order to evaluate the quality of the obtained control system. Finally, the designed control system is experimentally validated in bench tests and wind-tunnel tests for all four miniature actuators integrated in the morphing wing experimental model. The wind-tunnel testing treats the system as a whole and includes, besides the evaluation of the controlled actuation system, the testing of the integrated morphing wing experimental model and the evaluation of the aerodynamic benefits brought by the morphing technology on this project. From this last perspective, the airflow on the morphing upper surface of the experimental model is monitored by using various techniques based on pressure data collection with Kulite pressure sensors or on infrared thermography camera visualisations.

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Keywords

Novel morphing wing actuator control-based Particle Swarm Optimisation

  • S. Khan (a1), T. L. Grigorie (a1) (a2), R. M. Botez (a1), M. Mamou (a3) and Y. Mébarki (a3)...

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