Hysteresis Modelling and Feedforward Control of Piezoelectric Actuator Based on Simplified Interval Type-2 Fuzzy System

Design and characteristic analysis of multi-degree-of-freedom ultrasonic motor based on spherical stator

The multidimensional motion ultrasonic motor with a single spherical stator is studied in this paper. It has the characteristics of miniaturization and can be used in precision motion applications. By bonding six identical pieces of piezoelectric ceramic onto the stator and applying the voltage signal of high frequency, the deformation of the inverse piezoelectric effect is used to excite the stator yaw vibration mode. The orthogonal superposition of the modes of the spherical stator on the driving foot produces elliptical trajectory around X, Y, and Z directions by different excitation methods. According to the yaw vibration mode of the spherical stator, 12 driving feet are designed to drive the rotation of the spherical rotor. The structure and mechanical characteristics of the motor are simulated by using simulation software, and the transient response of the stator driving foot was obtained, which proved its feasibility. Finally, the output performance of the motor in actual operation is given through experiments, which provides a new reference scheme in the field of precise multi-degree-of-freedom motion. At a voltage of 100 V and a frequency of 26.7 kHz, the prototype has a no-load speed of 73, 70, and 114 rpm around X, Y, and Z axes, respectively.

Design and Locomotion Study of Stick-Slip Piezoelectric Actuator Using Two-Stage Flexible Hinge Structure

A novel piezoelectric actuator using a two-stage flexure hinge structure is proposed in this paper, which is used in a compact and high-precision electromechanical field. The two-stage flexure hinge structure is used to provide horizontal thrust and vertical clamping force to the driving feet, which solves the problems of unstable clamping force and insufficient load capacity in traditional stick-slip piezoelectric actuators. Firstly, the main structure of the driver and the working process under the triangular wave excitation voltage are briefly introduced. Secondly, after many simulation tests, the structure of the actuator is optimized and the stability of the structure in providing clamping force is verified. Finally, through the research of the operating performance, when the amplitude is 150 V and the frequency is 3.25 kHz as the excitation source, the maximum speed can reach 338 mm/s and can bear about 3 kg load. It can be seen from the analysis that the two-stage flexure hinge structure can improve the displacement trajectory.

Modeling and Inverse Compensation of Cross-Coupling Hysteresis in Piezoceramics under Multi-Input

In the fast tool servo (FTS) system for microstructure surface cutting, the dynamic voltage hysteresis of piezoelectric actuators (PEAs) and the cutting force produced in the manufacturing affect the driving accuracy and the cutting performance. For a multi-input-single-output (MISO) cutting system, in this paper, a dynamic hysteresis model based on a rate-dependent Prandtl–Ishlinskii model is proposed. A backpropagation neural network (BPNN) is established to describe the cross-coupling effect between the applied voltage and external load. An inverse dynamic model is developed to compensate the nonlinearity of PEAs. The accuracy of the model and its inverse is discussed and the performance of the inverse feedforward compensator is validated through experiments.

Advances in Tracking Control for Piezoelectric Actuators Using Fuzzy Logic and Hammerstein-Wiener Compensation

Piezoelectric actuators (PEA) are devices that are used for nano- microdisplacement due to their high precision, but one of the major issues is the non-linearity phenomena caused by the hysteresis effect, which diminishes the positioning performance. This study presents a novel control structure in order to reduce the hysteresis effect and increase the PEA performance by using a fuzzy logic control (FLC) combined with a Hammerstein–Wiener (HW) black-box mapping as a feedforward (FF) compensation. In this research, a proportional-integral-derivative (PID) was contrasted with an FLC. From this comparison, the most accurate was taken and tested with a complex structure with HW-FF to verify the accuracy with the increment of complexity. All of the structures were implemented in a dSpace platform to control a commercial Thorlabs PEA. The tests have shown that an FLC combined with HW was the most accurate, since the FF compensate the hysteresis and the FLC reduced the errors; the integral of the absolute error (IAE), the root-mean-square error (RMSE), and relative root-mean-square-error (RRMSE) for this case were reduced by several magnitude orders when compared to the feedback structures. As a conclusion, a complex structure with a novel combination of FLC and HW-FF provided an increment in the accuracy for a high-precision PEA.

A Proposal of an Adaptive Neuro-Fuzzy Inference System for Modeling Experimental Data in Manufacturing Engineering

In Manufacturing Engineering there is a need to be able to model the behavior of technological variables versus input parameters in order to predict their behavior in advance, so that it is possible to determine the levels of variation that lead to optimal values of the response variables to be obtained. In recent years, it has been a common practice to rely on regression techniques to carry out the above-mentioned task. However, such models are sometimes not accurate enough to predict the behavior of these response variables, especially when they have significant non-linearities. In this present study a comparative analysis between the precision of different techniques based on conventional regression and soft computing is initially carried out. Specifically, regression techniques, based on the response surface model, as well as the use of artificial neural networks and fuzzy inference systems along with adaptive neuro-fuzzy inference systems will be employed to predict the behavior of the aforementioned technological variables. It will be shown that when there are difficulties in predicting the response parameters by using regression models, soft computing models are highly effective, being much more efficient than conventional regression models. In addition, a new method is proposed in this study that consists of using an iterative process to obtain a fuzzy inference system from a design of experiments and then using an adaptive neuro-fuzzy inference system for tuning the constants of the membership functions. As will be shown, with this method it is possible to obtain improved results in the validation metrics. The means of selecting the membership functions to develop this model from the design of experiments is discussed in this present study in order to obtain an initial solution, which will be then tuned by using an adaptive neuro-fuzzy inference system, to predict the behavior of the response variables. Moreover, the obtained results will also be compared.