Feedforward neural network position control of a piezoelectric actuator based on a BAT search algorithm

• Precise position control of PEA is problematic due to highly-nonlinear hysteresis behavior.
• A proposed positional control method improved the positional control of X-PEA mechanism model.
• The overshoot value and steady-state error were successfully minimized.
• Introduced error constant is modified through BAT search algorithm.
• BAT search algorithm is used to properly train the FNN and to optimize the FNN topology.

The precise positional controls of piezoelectric actuators (PEA) are problematic due to highly-nonlinear hysteresis behavior which is inherent in piezoelectric materials. In existing PEA positional control applications that are based only on neural networks, the obtained control response results are insufficient for practical usage. In this paper we apply a combined approach by using a feedforward neural network (FNN) jointly with a BAT search algorithm in order to improve the positional control of an X-PEA mechanism model by also taking into account the hysteresis behavior. The proposed positional controller was successfully implemented and it was capable of significantly improving the overall control response result of an X-PEA mechanism model by minimizing the overshoot value and steady-state error, and decreasing the settling time. In addition, the BAT search algorithm can also be used for training the FNN, optimizing the FNN topology and reducing the computational complexity. The presented simulation results confirmed that the proposed positional controller with combined approach provides better results compared to the classical FNN control approach.