Sequential Quadratic Programming-based Iterative Learning Control for Nonlinear Systems

TL;DR

This paper introduces a sequential quadratic programming-based iterative learning control algorithm tailored for nonlinear systems, enabling optimal input learning through repeated quadratic subproblem solutions, demonstrated on a precision motion system.

Contribution

It presents a novel iterative learning control method for nonlinear systems using sequential quadratic programming, extending existing linear-focused approaches to more complex dynamics.

Findings

Effective in trajectory optimization for nonlinear systems

Demonstrates improved performance over linear models

Validated through simulations on a precision motion system

Abstract

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.