Browsing by Subject "acoustic levitation"

Acoustic levitation refers to the levitation of particles using sound waves. It can be performed on a phased array of transducers (levitator) where the transducers create the sound waves. The levitator device can be controlled by altering the values of the control parameters of the transducers. In this thesis, we present an automatic approach for finding the control parameter values using a branch of machine learning called reinforcement learning. The main goal is to make specifying the control parameter values for complex levitation tasks easier. We first build a simulation environment for the learning task, and then perform several experiments in the environment and compare two model-based reinforcement learning algorithms: Covariance Matrix Adaptation Evolution Strategy and a baseline strategy based on random actions. The experiments are related to optimizing the hyperparameters of reinforcement learning, testing the algorithm with limitations that using a real levitator would bring, and solving different levitation tasks. The results of the experiments show that the simulation environment enables controlling levitators with model-based algorithms. Furthermore, both of the algorithms that were used were able to solve various control problems, such as lifting a particle and moving a particle in circles.