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.