An environment for the efficient testing and implementation of robust NMPC

Abstract

In the last years many research studies have presented simulation or experimental results using Nonlinear Model Predictive Control (NMPC). The computation times needed for the solution of the resulting nonlinear optimization problems are in many cases no longer an obstacle due to the advances in algorithms and computational power. However, NMPC is not yet an industrial reality as its linear counterpart is. Two reasons for this are the lack of good tool support for the development of NMPC solutions and the fact that it is difficult to ensure the robustness of NMPC to plant-model mismatch. In this paper, we address both these issues. The main contribution is the development of an environment for the efficient implementation and testing of NMPC solutions, offering flexibility to test different algorithms and formulations without the need to re-encode the model or the algorithm. In addition, we present and discuss the approach of multi-stage robust NMPC to systematically deal with the robustness issue. The benefits of our approach are illustrated by experimental results on a laboratory plant.

CasADi: a software framework for nonlinear optimization and optimal control

“…Software packages that rely on CasADi for algorithmic differentiation and optimization include the JModelica.org package for simulation and optimization [16,98], the Greybox tool for constructing thermal building models [35], the do-mpc environment for efficient testing and implementation of robust nonlinear MPC [93,94], mpc-tools-casadi for nonlinear MPC [7], the casiopeia toolbox for parameter estimation and optimum experimental design [2], the RTC-Tools 2 package for control of hydraulic networks, the omgtools package for real-time motion planning in the presence of moving obstacles, the Pomodoro toolbox for multi-objective optimal control [29], the spline toolbox for robust optimal control [127], and a MATLAB optimal control toolbox [83].…”

Dynamic Performance Optimization of a Pilot-Scale Reactive Distillation Process by Economics Optimizing Control

“…The NMPC algorithm used in this work is a modified version of the algorithm used in the software package do-mpc , , which is implemented in the Python/C++ programming language. The software has interfaces to the automatic differentiation tool CasADi , and the interior-point optimizer Ipopt …”

“…In this work, the realization of economics optimizing control at a pilot-plant RD process with two target products is reported. The implementation is based on the Python/C++ based software tool do-mpc, 22 which utilizes efficient algorithms that drastically reduce the computation times of NMPC applications. The software consists of three main modules: A process simulator for performing simulation studies, a state estimator, and the NMPC algorithm.…”