1. Introduction
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.
The below video shows the overview of the CRROS algorithm.
2. Matlab Code for Simulation
We provide all simulation codes written in MATLAB to help reader's understanding of the core principle of CRROS. The zip file includes six demo files. The first four demos are for the first experiment set shown in the paper (with homogeneous noise). The second two demos are for the second experiment set (with heteroscedastic noise).
3. Experiment Results with Flex-finger
-Experiment results of the CRROS with a standard GPR
