Novel morphing wing actuator control-based Particle Swarm Optimisation

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.

A New Aerodynamic Optimization Method with the Consideration of Dynamic Stability

“…The particle swarm optimization (PSO) algorithm [25] completes the whole search process by iteration. In each iteration step, the velocity and position update formula of the ith particle in the population of N in the n-dimensional search space is as follows:…”

Aerodynamic Design Optimization of a Morphing Leading Edge and Trailing Edge Airfoil–Application on the UAS-S45

“…Therefore, a PSO algorithm is used, based on a simplified social behavior closely related to the swarming theory, where the solutions are represented by a set of particles that heuristically navigate through a design space [79]. The efficiency of PSO algorithms over genetic algorithms is due to their independence from parameters, such as crossovers and mutations; instead, the solution is updated by sharing its information amongst its populations of particles.…”

Optimization and Design of a Flexible Droop Nose Leading Edge Morphing Wing Based on a Novel Black Widow Optimization (B.W.O.) Algorithm—Part II

“…"Morphing" may be mainly used on uncrewed aerial vehicles due to their smaller scale and lesser complexity in terms of wing design structure and energy consumption, especially in terms of actuation power (UAVs) [25][26][27][28][29][30]. A telescoping spar and rib system was used to build an Unmanned Aerial Vehicle (UAV) morphing wing capable of modifying its wing span and altering its chord [31].…”