Abstract
This paper presents an adaptive hysteresis compensation approach for a piezoelectric actuator (PEA) using single-neuron adaptive control. For a given desired trajectory, the control input to the PEA is dynamically adjusted by the error between the actual and desired trajectories using Hebb learning rules. A single neuron with self-learning and self-adaptive capabilities is a non-linear processing unit, which is ideal for time-variant systems. Based on the single-neuron control, the compensation of the PEA’s hysteresis can be regarded as a process of transmitting biological neuron information. Through the error information between the actual and desired trajectories, the control input is adjusted via the weight adjustment method of neuron learning. In addition, this paper also integrates the combination of Hebb learning rules and supervised learning as teacher signals, which can quickly respond to control signals. The weights of the single-neuron controller can be constantly adjusted online to improve the control performance of the system. Experimental results show that the proposed single-neuron adaptive hysteresis compensation method can track continuous and discontinuous trajectories well. The single-neuron adaptive controller has better adaptive and self-learning performance against the rate-dependence of the PEA’s hysteresis.
Highlights
As a sub-nanometer-resolution actuation device, piezoelectric actuators (PEAs) have been widely applied in various applications requiring nanometer-accurate motion [1,2,3,4]
The most significant characteristics of the PEA’s hysteresis are the rate-dependence and asymmetry [5,6,7], i.e., the hysteresis loop becomes thicker with the increment in the input rate and the hysteresis loop is not symmetric about the loop center
As the inversion of the classical PI model is analytically available, it has been widely utilized in much research to describe the hysteresis characteristics of the PEA
Summary
As a sub-nanometer-resolution actuation device, piezoelectric actuators (PEAs) have been widely applied in various applications requiring nanometer-accurate motion [1,2,3,4]. After the inversion model is obtained, it can be utilized as a feedforward hysteresis compensator This modeling and inversion approach is widely adopted, and many adaptive methods can be integrated [14,15,16,17]. A linearization control method with feedforward hysteresis compensation and proportional-integral-derivative (PID) feedback has been proposed [22]. An inversion-free predictive controller was proposed based on a dynamic linearized multilayer feedforward neural network model [27]. A single-neuron adaptive hysteresis compensation method is proposed in this paper. The experimental results show that the proposed method has excellent robustness and adaptability against the rate-dependence of the PEA’s hysteresis, and the hysteresis can be successfully compensated.
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