Adaptive neuro-fuzzy inference system-based controllers for smart material actuator modelling

Abstract An intelligent approach for smart material actuator modelling of the actuation lines in a morphing wing system is presented, based on adaptive neuro-fuzzy inference systems. Four independent neuro-fuzzy controllers are created from the experimental data using a hybrid method — a combination of back propagation and least-mean-square methods — to train the fuzzy inference systems. The controllers' objective is to correlate each set of forces and electrical currents applied on the smart material actuator to the actuator's elongation. The actuator experi-mental testing is performed for five force cases, using a variable electrical current. An integrated controller is created from four neuro-fuzzy controllers, developed with Matlab/Simulink software for electrical current increases, constant electrical current, electrical current decreases, and for null electrical current in the cooling phase of the actuator, and is then validated by comparison with the experimentally obtained data.