Abstract
This paper proposes a feedforward and feedback combined hysteresis compensation method for a piezoelectric actuator (PEA) based on the multi-layer feedforward neural network (MFNN) inverse model. Under the scheme of direct inverse modeling, the MFNN is utilized as the feedforward hysteresis compensator, which can be directly identified from the measurements. The high modeling accuracy and high robustness of the MFNN help to increase the bandwidth of the closed-loop system. Experiments are conducted on a commercial PEA so as to verify the effectiveness of the proposed method. The superimposition of two sinusoidal signals is found to be efficient for the training of the MFNN. Closed-loop trajectory tracking experiments demonstrate that the bandwidth can be increased up to 1000 Hz and the maximum deviation can be maintained closed to the noise level. Meanwhile, there are only two parameters to be tuned in the proposed method, which guarantees ease of use for the inexperienced users. The proposed method successfully realizes high-precision hysteresis compensation performance across a wider frequency range.
Highlights
The nano positioning system using piezoelectric actuator (PEA) has the advantages of low energy consumption, fast response and negligible friction [1,2,3,4]. It is widely used in various fields, such as precision manufacturing and nano positioning
A lot of models have been used to describe the hysteresis of PEA, such as the Preisach model [5], Krasnosel’skii-Pokrovkii model [6,7], Prandtl–Ishlinskii (PI) model [8], and Duhem model [9]
When the modeling accuracy of the inverse hysteresis model is accurate the multi-layer feedforward neural network (MFNN) is a function of system output and input, which can be expressed as follows: enough, the PEA’s hysteresis can be well compensated
Summary
The nano positioning system using piezoelectric actuator (PEA) has the advantages of low energy consumption, fast response (millisecond level) and negligible friction [1,2,3,4]. Feedforward control can achieve satisfactory hysteresis compensation performance at low frequencies. The feedforward and feedback combined control is a candidate to realize high-precision and high-bandwidth hysteresis compensation of PEAs [22]. Based on this framework, many controllers were proposed to improve the control performance [23,24]. Many effective hysteresis compensation methods have been proposed, they often need to adjust many control parameters onsite This results in higher requirements on the experience and skills of the users. MFNN is used to directly obtain the PEA’s inverse hysteresis model under the scheme of DIM [10], where the conventional model-inversion process can be avoided.
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