  An energy based approach for stabilizing a mechanical system has offered a
simple yet powerful control scheme. However, since it does not impose such
strong constraints on parameter space of the controller, finding appropriate
parameter values for an optimal controller is known to be hard. This paper
intends to generate an optimal energy-based controller for swinging up a rotary
inverted pendulum, also known as the Furuta pendulum, by applying the Bayesian
optimization called Entropy Search. Simulations and experiments show that the
optimal controller has an improved performance compared to a nominal controller
for various initial conditions.

Machine Learning;Systems and Control;Optimization and Control;Machine Learning