System and method for polytopic policy optimization for robust feedback control during learning

Abstract

A controller is provided for generating a policy controlling a system by learning a dynamics of the system. The controller is configured to perform steps of acquiring measurement data from sensors arranged on the system, providing, to the memory, a non-linear system model represented by known part of the dynamics of the system and unknown part of the dynamics of the system, collecting states of the system by measuring the dynamics of the system using the sensors of the system based on a nominal policy and a noise term with respect to the states, estimating a sequence of sets of states of the system and sets of control inputs by collecting data of the system, wherein the data includes a collection of system states, applied control inputs and change in system states, wherein each of the control input is computed by the nominal policy and the additional noise term, learning a polytopic system by use of the collected data of the system for approximating the unknown part of the dynamics of the system using a linear probabilistic regression model, estimating an attractor basin of a terminal controller by sampling initial states in a neighborhood of a terminal state and estimating the attractor basin by supervised learning, and generating a polytopic policy using the estimated polytopic system to drive the system to the attractor basin of the terminal controller from an initial state.