Model-Based Learning Controller Design for a Furuta Pendulum


We present a method for designing and tuning controllers for the problem of swing-up and stabilization of a Furuta pendulum. The method is based on suitable param- eterization of a family of controllers and the application of Bayesian optimization to their tuning with minimal interaction with the physical system. Unlike traditional controller design methodologies, the method does not require the derivation of an exact physical model of the controlled plant, thus saving significant design time and effort. Furthermore, the method has much more favorable sample complexity than most policy optimization methods proposed in the field of reinforcement learning.