Output-Feedback Model Predictive Control with Online Identification

TL;DR

This paper introduces an output-feedback model predictive control method with online system identification that avoids the need for an observer, using recursive least squares for real-time model updates and analyzing its performance through various numerical examples.

Contribution

The paper presents a novel OFMPCOI approach that integrates online identification with MPC without a separate observer, and investigates its effectiveness under different control challenges.

Findings

OFMPCOI effectively handles model uncertainties and sensor noise.

The method maintains control performance without separate perturbations.

Numerical results demonstrate robustness and adaptability of OFMPCOI.

Abstract

Model predictive control (MPC) is a widely used modern control technique with numerous successful application in diverse areas. Much of this success is due to the ability of MPC to enforce state and control constraints, which are crucial in many applications of control. In order to avoid the need for an observer, output-feedback model predictive control with online identification (OFMPCOI) uses the block observable canonical form whose state consists of past values of the control inputs and measured outputs. Online identification is performed using recursive least squares (RLS) with variable-rate forgetting. The article describes the algorithmic details of OFMPCOI and numerically investigates its performance through a collection of numerical examples that highlight various control challenges, such as model order uncertainty, sensor noise, prediction horizon, stabilization, magnitude and move-size saturation, and stabilization. The numerical examples are used to probe the performance of OFMPCOI in terms of persistency, consistency, and exigency. Since OFMPCOI does not employ a separate control perturbation to enhance persistency, the focus is on self-generated persistency during transient operation. For closed-loop identification using RLS, sensor noise gives rise to bias in the identified model, and the goal is to determine the effect of the lack of consistency. Finally, the numerical examples reveal exigency, which is the extent to which the online identification emphasizes model characteristics that are most relevant to meeting performance objectives.