Yasuhiro Sawamoto

Two-Axial Piezoelectric Actuator Controller Using a Multi-Layer Artificial Neural Network Featuring Feedback Connection for Tactile Displays

Although some compensation method is required when using a piezoelectric actuator because of hysteresis, a sensor feedback method is not suitable for an actuator array. In this study, we design a controller using a neural network to apply it to a tactile display composed of two-axial miniature actuators. This paper describes the two-axial miniature actuator, which is composed of two bimorph piezoelectric elements and two small links connected by three joints. A control system for the two-axial miniature actuator is designed based on a multi-layered artificial neural network to compensate for the hysteresis of piezoelectric elements. The output neuron emits the time derivative of voltage, a two-bit signal expressing increment or decrement condition is generated by two input neurons, and two input neurons calculate current values of voltage and displacement, respectively. The neural network is outfitted with a feedback loop including an integral element to reduce the number of neurons. In the experiment, if the result of the left piezoelectric element is compared to that of the right element, the displacement amplitudes and the inclinations coincide on the right and left piezoelectric elements. Although precise hysteresis characteristics such as loop width are considerably different, the present neural system can follow the difference.

Parallel Type Two-axial Actuator Controlled by a Multi-layered Neural Network

We experimentally design a parallel typed two-axial micro actuator, which is utilized for the key part of the tactile display. The parallel typed two-axial actuator was composed of two bimorph piezoelectric elements and two small links connected by three joints. We formulated kinematics for the parallel typed two-axial actuator because the endpoint is controlled in the two-dimensional coordinate. Since relationship between applied voltage and displacement cause by the voltage shows a hysteresis loop in the bimorph piezoelectric element used as components of the two-axial actuator, we produce a control system for the two-axial actuator based on a multi-layered artificial neural network to compensate the hysteresis. The neural network is comprised of 4 neurons in the input layer, 10 neurons in the hidden layer and ones neuron in the output layer. The output neuron emits time derivative of voltage; two bits signal expressing increment or decrement condition is generated by two input neurons; one of the other two input neurons and the other calculate current values of voltage and displacement, respectively. The neural network is featured with a feedback loop including an integral element to reduce number of neurons. In the learning process, the network learns the hysteresis including a minor loop. In the verification test, the endpoint of the two-axial actuator traces the desired circular trajectory in the two-dimensional coordinate system.

A Two-axis Bimorph Piezoelectric Actuator for Pressure and Slippage Force Presentation

We are studying a two-axis micro-actuator to enhance the presentation reality of a tactile display that is capable of presenting pressure distribution and shearing force. We develop two types of actuators; Actuator A is composed of sequentially connected x- and y-directional actuators; each actuator is comprised of bimorph piezoelectric actuators. The x- and y-directional actuators are independently controlled by changing the applied voltage to position a probe that is attached to the tip of a two-axis actuator. The maximum displacement and force generated by the x-directional actuator are 1.1 mm and 0.03 N, respectively. Those generated by y-directional actuator are 1.0 mm and 0.06 N, respectively. Actuator B is composed of two bimorph actuators making an angle, two small links and three joints. At the present, we confirm that the actuator can move along x and y-axes of two-dimensional coordinate. Finally, since relationship between applied voltage and displacement caused by the voltage shows a hysteresis loop in the bimorph actuator used as components of the two-axis actuator, we produce a control system for the two-axial actuator based on a multi-layered artificial neural network to compensate the hysteresis

Improvement of a Parallel Type Two-axial Actuator Controlled by a Multi-layered Neural Network

Our parallel typed two-axial actuator was composed of two bimorph piezoelectric elements and two small links connected by three joints. We formulated kinematics for the parallel typed two-axial actuator because the endpoint is controlled in the two-dimensional coordinate. Since relationship between applied voltage and displacement cause by the voltage shows a hysteresis loop in the bimorph piezoelectric element used as components of the two-axial actuator, we produce a control system for the two-axial actuator based on a multi-layered artificial neural network to compensate the hysteresis. The neural network is comprised of 4 neurons in the input layer, 10 neurons in the hidden layer and ones neuron in the output layer. The output neuron emits time derivative of voltage; two bits signal expressing increment or decrement condition is generated by two input neurons; one of the other two input neurons and the other calculate current values of voltage and displacement, respectively. In the learning process, the network learns the hysteresis including minor loops. In the verification test, the endpoint of the two-axial actuator traces the desired circular trajectory in the two-dimensional coordinate system. After learning hysteresis loops including minor loops, the neural network simulates these hysteresis phenomena with very high accuracy.