An Iterative Data-Driven Linear Quadratic Method to Solve Nonlinear Discrete-Time Tracking Problems

The objective of this note is to introduce a novel data-driven iterative linear quadratic control method for solving a class of nonlinear optimal tracking problems. Specifically, an algorithm is proposed to approximate the Q-factors arising from linear quadratic stochastic optimal tracking problems. This algorithm is then coupled with iterative linear quadratic methods for determining local solutions to nonlinear optimal tracking problems in a purely data-driven setting. Simulation results highlight the potential of this method for field applications.