Control in the Reliable Region of a Statistical Model

We present a novel statistical model based control algorithm, called Control in the Reliable Region of a Statistical model (CRROS). First, CRROS builds a statistical model with Gaussian process regression, which provides a prediction function and uncertainty of the prediction. Then, CRROS avoids high-uncertainty regions of the statistical model by regulating the null space of the pseudo inverse solution. The simulation results demonstrate that CRROS drives the states toward high-density and low-noise regions of training data, ensuring high reliability of the model. The experiments with a robotic finger, called Flex-finger, show the potential of CRROS to control robotic systems that are difficult to model, contain constrained inputs, and exhibit heteroscedastic noise output.