Revision and Comparative Study with Experimental Validation of Sliding Mode Control Approaches Using Artificial Neural Networks for Positioning Piezoelectric Actuator

Piezoelectric actuators are commonly used in high precision, micro-displacement applications. However, nonlinear phenomena, like hysteresis, may reduce their performance. This article compares several control approaches—based on the combination of sliding mode control and artificial neural networks—for coping with these nonlinearities and improving actuator positioning accuracy and robustness. In particular, it discusses the application of diverse order sliding mode control techniques, such as conventional, twisting algorithms, super-twisting algorithms, and the prescribed convergence law, in combination with artificial neural networks. Moreover, it validates experimentally, with a commercial piezoelectric actuator, the application of these control structures using a dSPACE 1104 controller board. Finally, it evaluates the computational time consumption for the control strategies presented. This work intends to guide the designers of PEA commercial applications to select the best control algorithm and identify the hardware requirements.