Approximate infinite-horizon predictive control

TL;DR

This paper proposes a stabilizing predictive control method using a parametric terminal cost trained via approximate dynamic programming, ensuring stability and quantifiable performance even beyond the training domain.

Contribution

It introduces a novel stabilizing predictive controller with a terminal cost trained through approximate dynamic programming, linking performance to approximation errors.

Findings

Controller guarantees closed-loop asymptotic stability.

Performance bounds are directly related to cost approximation errors.

Ensures stability beyond the training domain.

Abstract

Predictive control is frequently used for control problems involving constraints. Being an optimization based technique utilizing a user specified so-called stage cost, performance properties, i.e., bounds on the infinite horizon accumulated stage cost, aside closed-loop stability are of interest. To achieve good performance and to influence the region of attraction associated with the prediction horizon, the terminal cost of the predictive controller's optimization objective is a key design factor. Approximate dynamic programming refers to one particular approximation paradigm that pursues iterative cost adaptation over a state domain. Troubled by approximation errors, the associated approximate optimal controller is, in general, not necessarily stabilizing nor is its performance quantifiable on the entire approximation domain. Using a parametric terminal cost trained via approximate dynamic programming, a stabilizing predictive controller is proposed whose performance can directly be related to cost approximation errors. The controller further ensures closed-loop asymptotic stability beyond the training domain of the approximate optimal controller associated to the terminal cost.