Rapid development of modular and sustainable nonlinear model predictive control solutions

Lucia, Sergio; Tătulea-Codrean, Alexandru; Schoppmeyer, Christian; Engell, Sebastian

doi:10.1016/j.conengprac.2016.12.009

Cited by 123 publications

(39 citation statements)

References 25 publications

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“…The problem was solved using the do-mpc software which utilizes orthogonal collocation on finite elements to discretize the system, which can then be solved using NLP optimizers (Lucia et al, 2017). One main challenge when operating this system is the non-continuous property of the inputs.…”

Section: Methodsmentioning

confidence: 99%

Optimal operation of parallel mini-bioreactors in bioprocess development using multi-stage MPC

Krausch

Kim

Lucia

et al. 2021

Preprint

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Bioprocess development is commonly characterized by long development times, especially in the early screening phase. After promising candidates have been pre-selected in screening campaigns, an optimal operating strategy has to be found and verified under conditions similar to production. Cultivating cells with pulse-based feeding and thus exposing them to oscillating feast and famine phases has shown to be a powerful approach to study microorganisms closer to industrial bioreactor conditions. In view of the large number of strains and the process conditions to be tested, high-throughput cultivation systems provide an essential tool to sample the large design space in short time. We have recently presented a comprehensive platform, consisting of two liquid handling stations coupled with a model-based experimental design and operation framework to increase the efficiency in High Throughput bioprocess development. Using calibrated macro-kinetic growth models, the platform has been successfully used for the development of scale-down fed-batch cultivations in parallel mini-bioreactor systems. However, it has also been shown that parametric uncertainties in the models can significantly affect the prediction accuracy and thus the reliability of optimized cultivation strategies. To tackle this issue, we implemented a multi-stage Model Predictive Control (MPC) strategy to fulfill the experimental objectives under tight constraints despite the uncertainty in the parameters and the measurements. Dealing with uncertainties in the parameters is of major importance, since constraint violation would easily occur otherwise, which in turn could have adverse effects on the quality of the heterologous protein produced. Multi-stage approaches build up scenario tree, based on the uncertainty that can be encountered and computing optimal inputs that satisfy the constrains despite of such uncertainties. Using the feedback information gained through the evolution along the tree, the control approach is significantly more robust than standard MPC approaches without being overly conservative. We show in this study that the application of multi-stage MPC can increase the number of successful experiments, by applying this methodology to a mini-bioreactor cultivation operated in parallel.

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Section: Methodsmentioning

confidence: 99%

Optimal operation of parallel mini-bioreactors in bioprocess development using multi-stage MPC

Krausch

Kim

Lucia

et al. 2021

Preprint

Self Cite

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“…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].…”

Section: Software Packages Using Casadimentioning

confidence: 99%

CasADi: a software framework for nonlinear optimization and optimal control

Andersson

Gillis

Horn³

et al. 2018

Math. Prog. Comp.

2,425

976

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We present CasADi, an open-source software framework for numerical optimization. CasADi is a general-purpose tool that can be used to model and solve optimization problems with a large degree of flexibility, larger than what is associated with popular algebraic modeling languages such as AMPL, GAMS, JuMP or Pyomo. Of special interest are problems constrained by differential equations, i.e. optimal control problems. CasADi is written in self-contained C++, but is most conveniently used via full-featured interfaces to Python, MATLAB or Octave. Since its inception in late 2009, it has been used successfully for academic teaching as well as in applications from multiple fields, including process control, robotics and aerospace. This article gives an up-to-date and accessible introduction to the CasADi framework, which has undergone numerous design improvements over the last seven years.

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“…We apply distributed model predictive control on the time interval [0, 100]s, using discrete time steps of length ∆t = 0.1s and a prediction horizon of five time steps. The local controllers are implemented with the do-mpc framework [42]. We use CasADi for automatic differentiation [43], in particular for computing the sensitivities, and Ipopt for solving the involved optimization problems [44].…”

Section: A Nonlinear Power System Modelmentioning

confidence: 99%

Attack Identification for Nonlinear Systems Based on Sparse Optimization

Braun

Albrecht

Lucia

2022

IEEE Trans. Automat. Contr.

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Adversarial attacks on controllers of dynamic systems have become a serious threat to many real-world systems, making methods for fast identification of attacks an indispensable part of autonomous systems. With the increasing use of model-based controllers, it is valid to exploit model knowledge also for attack identification as long as privacy of individual components is maintained. A scalable, model-based method to reveal generic attacks was introduced in our previous work and is further investigated here. It is designed for coupled systems with nonlinear dynamics and monitors certain coupling states. Based on the exchange of local sensitivity information, it approximates the propagation of an attack through the network and solves a sparse optimization problem to identify the attack. We provide a thorough derivation of the approach and analyze the involved approximation errors to prove rigorous guarantees for successful identification. In an extensive numerical case study with the IEEE 30 bus power system, we prove that not only the guarantees apply for a nonlinear, practically relevant example, but the method also identifies attacked buses with very high success rates.

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Rapid development of modular and sustainable nonlinear model predictive control solutions

Cited by 123 publications

References 25 publications

Optimal operation of parallel mini-bioreactors in bioprocess development using multi-stage MPC

Optimal operation of parallel mini-bioreactors in bioprocess development using multi-stage MPC

CasADi: a software framework for nonlinear optimization and optimal control

Attack Identification for Nonlinear Systems Based on Sparse Optimization

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