A rapid-prototype MPC tool based on gPROMS platform

TL;DR

This paper introduces a rapid-prototype Model Predictive Control tool integrated with the gPROMS platform, enabling direct interaction with first-principle models, simplified control models, and efficient solution algorithms for advanced control applications.

Contribution

The gPROMS-MPC tool uniquely combines first-principle model interaction, flexible linearization, and efficient sparse MPC algorithms, enhancing control design and implementation.

Findings

Supports direct interaction with gPROMS models

Enables flexible linearization strategies

Implements efficient sparse MPC algorithms

Abstract

This paper presents a rapid-prototype Model Predictive Control (MPC) tool based on the gPROMS platform, with the support for the whole MPC design workflow. The gPROMS-MPC tool can not only directly interact with a first-principle-based gPROMS model for closed-loop simulations but also utilizes its mathematical information to derive simplified control-oriented models, basically via linearization techniques. It can inherit the interpretability of the first-principle-based gPROMS model, unlike the PAROC framework in which the control-oriented models are obtained from black-box system identification based on gPROMS simulation data. The gPROMS-MPC tool allows users to choose when to linearize such as at each sampling time (successive linearization) or some specific points to obtain one or multiple good linear models. The gPROMS-MPC tool implements our previous construction-free CDAL and the online parametric active-set qpOASES algorithms to solve sparse or condensed MPC problem formulations, respectively, for possible successive linearization or high state-dimension cases. Our CDAL algorithm is also matrix-free and library-free, thus supporting embedded C-code generation. After many example validations of the tool, here we only show one example to investigate the performance of different MPC schemes.