Enhancement of Energy-Based Swing-Up Controller via Entropy Search
This work addresses a specific control challenge for mechanical systems like the Furuta pendulum, representing an incremental improvement in parameter optimization.
The paper tackled the problem of finding optimal parameters for an energy-based swing-up controller for a Furuta pendulum by applying Bayesian optimization (Entropy Search), resulting in improved performance over a nominal controller across various initial conditions in simulations and experiments.
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.