二軸マイクロ・ピエゾアクチュエータのニューラル制御

抄録

In the previous paper, we experimentally designed serial and parallel typed two-axial micro actuators, which were utilized for key parts of advanced tactile displays. The serial typed two-axial actuator comprises bimorph piezoelectric actuators connected in series. The parallel typed two-axial actuator was composed of two bimorph piezoelectric actuators 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 caused by the voltage shows a hysteresis loop in the bimorph piezoelectric actuator used as components of the two-axis actuators, we produce a control system for these two-axial actuators 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 one neuron in the output layer. The output neuron emitts time derivative of voltage; two bits signal expressing loading or unloading 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 unit 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.