Rate Dependent Krasnoselskii-Pokrovskii Modeling and Inverse Compensation Control of Piezoceramic Actuated Stages

Abstract

The piezoceramic actuated stages have rate-dependent hysteresis nonlinearity, which is not simply related to the current and historical input, but also related to the frequency of the input signal, seriously affects its positioning accuracy. Consider the influence of frequency on hysteresis modeling, a rate-dependent hysteresis nonlinearity model that is based on Krasnoselskii–Pokrovskii (KP) operator is proposed in this paper. A hybrid optimization algorithm of improved particle swarm optimization and cuckoo search is employed in order to identify the density function of rate-dependent KP model, avoiding the blind search process caused by the high randomness of Levy’s flight in the cuckoo search algorithm, and improving the parameter identification performance. For the sake of eliminating the hysteresis characteristics, an inverse feed-forward compensation control that is based on recursive method is proposed without any additional conditions, and a feed-forward compensation controller is designed accordingly. The experimental results show that, under different frequency input signals, as compared with the classic KP model, the proposed rate-dependent KP model can accurately describe the rate-dependent hysteresis characteristics of the piezoceramic actuated stages, and the recursive inverse feed-forward compensation control method can effectively mitigate the hysteresis behaviors.

A rate-dependent KP modeling and direct compensation control technique for hysteresis in piezo-nanopositioning stages

“…As shown in Figures 7(c) and 8(c), hysteresis modeling error at 50 Hz begins to stabilize after 0.002 s with the RE of 1.85% and RMSE of 0.3502 m m. Some comparison experimental results are summarized in Table 1. It is noteworthy that proposed RKP model is completely superior to the modeling method proposed by Li et al (2020). In addition, the modeling errors are increased with the increase of input frequency.…”

High-Performance Tracking for Piezoelectric Actuators Using Super-Twisting Algorithm Based on Artificial Neural Networks

“…Typically, triangular and sine waves are managed for PEA actuators [71,72]. Certainly, the sine represents a soft signal which generates an ellipse kind hysteresis graph where the alternative option provides a sharp form due to the slope changes at the maximum driving voltage.…”