ModelPredictiveControl.jl: advanced process control made easy in Julia

TL;DR

ModelPredictiveControl.jl is an open-source Julia package that simplifies the design of advanced model predictive controllers, supporting nonlinear control and estimation with easy visualization and benchmarking capabilities.

Contribution

It introduces a modular, user-friendly toolkit for model predictive control in Julia, emphasizing transparency, reproducibility, and integration with modern computational frameworks.

Findings

Efficient solving times benchmarked against MATLAB implementations.

Successfully applied to linear and nonlinear control case studies.

Provides advanced features like moving horizon estimation and nonlinear control.

Abstract

Proprietary closed-source software is still the norm in advanced process control. Transparency and reproducibility are key aspects of scientific research. Free and open-source toolkit can contribute to the development, sharing and advancement of new and efficient control approaches, and the industrial sector will certainly benefit from them. This paper presents ModelPredictiveControl.jl, an open-source software package for designing model predictive controllers in the Julia programming language. It is designed to be easy to use and modular, while providing advanced features like nonlinear control and moving horizon estimation. It relies on powerful control system, mathematical optimization and automatic differentiation frameworks to simplify the construction and testing of state estimators and predictive controllers. It also integrates with the standard plotting library to quickly visualize closed-loop data. The paper presents the main functionalities and illustrates them with two case studies in simulation. The first example is a continuously stirred tank reactor described by linear dynamics. The second one implements a nonlinear, an economic, and a successive linearization model predictive controllers for an inverted pendulum. The solving times are benchmarked against equivalent implementations in MATLAB to show the efficiency of the package.