Real-time Algorithm for Self-Reflective Model Predictive Control

TL;DR

This paper introduces a real-time self-reflective model predictive control algorithm that enhances system learning and estimation accuracy by efficiently solving structured optimization problems with minimal computational overhead.

Contribution

The paper presents a tailored algorithm for self-reflective MPC that significantly speeds up online computation compared to generic solvers, enabling real-time application.

Findings

The algorithm effectively improves state and parameter estimation accuracy.

It operates in real-time with reasonable computational effort.

Application to nonlinear control demonstrates practical viability.

Abstract

This paper is about a real-time model predictive control (MPC) algorithm for a particular class of model based controllers, whose objective consists of a nominal tracking objective and an additional learning objective. Here, the construction of the learning term is based on economic optimal experiment design criteria. It is added to the MPC objective in order to excite the system from time-to-time on purpose in order to improve the accuracy of the state and parameter estimates in the presence of incomplete or noise affected measurements. A particular focus of this paper is on so-called self-reflective model predictive control schemes, which have the property that the additional learning term can be interpreted as the expected loss of optimality of the controller in the presence of random measurement errors. The main contribution of this paper is a formulation-tailored algorithm, which exploits the particular structure of self-reflective MPC problems in order to speed-up the online computation. It is shown that, in contrast to generic state-of-the-art optimal control problem solvers, the proposed algorithm can solve the self-reflective optimization problems with reasonable additional computational effort and in real-time. The advantages of the proposed real-time scheme are illustrated by applying the algorithm to a nonlinear process control problem in the presence of measurement errors and process noise.