Neural network based adaptive control for a piezoelectric actuator with model uncertainty and unknown external disturbance

Abstract To lessen the positioning error of the piezoelectric actuator (PEA) caused by hysteresis nonlinearity and unknown external disturbance, a neural network based adaptive controller is designed to realize the accurate trajectory tracking of the PEA. Specifically, a more universal model, consisting of a hysteresis submodel and a dynamics submodel, is first built for the PEA without the requirement of parameter identification. On this basis, a sliding mode adaptive controller capable of handling unknown parameters of the dynamics submodel is designed to weaken the damage of external disturbance to the system stability. Furthermore, to deal with the hysteresis submodel with unknown structure and parameters, a neural network based self‐tuning control scheme is developed to enable the PEA to accurately track the desired trajectory. Moreover, Lyapunov stability analysis is performed to strictly prove that the tracking error of the system can asymptotically converge to zero. Finally, the performance of the designed controller is verified via sufficient comparative simulations and experiments.