  Learning-based control methods for industrial processes leverage the
repetitive nature of the underlying process to learn optimal inputs for the
system. While many works focus on linear systems, real-world problems involve
nonlinear dynamics. In this work, we propose an algorithm for the nonlinear
iterative learning control problem based on sequential quadratic programming, a
well-studied method for nonconvex optimization. We repeatedly solve quadratic
subproblems built using approximate nonlinear models and process measurements,
to find an optimal input for the original system. We demonstrate our method in
a trajectory optimization problem for a precision motion system. We present
simulations to illustrate the performance of the proposed method for linear and
nonlinear dynamics models.
