Many processes operate repetitively, for example batch processes in biotechnology or chemical engineering. We propose a method for risk-aware run-to-run optimization and model predictive control of ...repetitive processes with uncertain models. The goal is to increase the performance as the number of runs increases by improving the model despite limited measurements while considering model uncertainty and avoiding uncertain areas. The method uses a gray-box model, i.e. a model formed by a first principle and a machine learning component, in this case an artificial neural network. The model uncertainty might be large, particularly in the first runs, where only a few measurements are available. We propose to quantify this uncertainty using Bayesian inference. This is in turn reflected by a risk measure entering an open-loop optimal control problem and a shrinking-horizon Model Predictive Controller as a constraint to limit control and exploitation in high risk areas. We show that using this risk measure we are able to efficiently reach high process performance. The proposed method is tested in simulations on two biotechnological fed-batch processes.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Controlling systems that are subject to state and input constraints using either neural networks-based or neural network-supported controllers is challenging. We focus on the neural network output ...layer to enforce constraint satisfaction and guarantees such as set invariance. In particular, we restrict the neural network output to a suitable set of control inputs that enables the flexible choice of guarantees depending on the requirements and the accessible model. The main contribution is the formal derivation of such a set for linear time-invariant systems. The result is a computationally efficient neural network output function that guarantees set inclusion without the requirement of adjusting a possibly existing baseline controller. Furthermore, we provide a numerical example to highlight the efficiency of the method as well as a detailed discussion of the results.
Achieving a new set point and maintaining it with a desired precision is a common control problem. In case the reference is not fixed, or a priori unknown disturbances are present, the problem is ...often referred to as offset-free control. We consider the problem of finding suitable controller parameters to obtain an offset-free control up to a certain degree. We tackle the problem in two steps: first, we find controller parameters to steer the controlled system in the neighborhood of the desired reference; second, we identify controller parameters such that the system is robustly controlled invariant with respect to the desired neighborhood of the reference value. For each subproblem we determine the set of parameters that guarantee the desired behavior despite bounded uncertainties. We employ a set-based feasibility formulation which is able to handle nonlinear systems with set constraints. The approach is illustrated with an example.
The main factor of bacteria pathogenicity is the ability to organize biofilm, which increases the resistance to antibacterial agents. Nanomaterials are promising substances with antimicrobial ...activity due to their unique physicochemical properties such as ultra-small sizes, large surface-area-to-mass ratio, and increased chemical reactivity1. The aim of this study was to evaluate the antibacterial efficacy of silver nanoparticles (AgNPs) against Enterococcus faecalis. AgNPs were prepared by the chemical reduction method with PVP as a capturing agent. Antibacterial properties were examined with the determination of the Minimum Inhibitory Concentration (MIC), and Minimum Bactericidal Concentration (MBC). The influence of AgNPs on biofilm formation was evaluated by detecting the biofilm mass inhibition (with gentian violet assay) and determining the AgNPs' effect on the biofilm ultrastructure with Scanning Electron Microscopy. AgNPs demonstrated appropriate antibacterial properties with inhibition of bacterial growth at concentrations \mathbf{5}\ \boldsymbol{\mu} \mathbf{g}/\mathbf{ml} (MIC) and killing bacteria at concentrations of \mathbf{10} \boldsymbol{\mu} \mathbf{g}/\mathbf{ml} (MBC). AgNPs do not affect bacteria attachment at these concentrations. Increasing concentrations up to \mathbf{40}\ \boldsymbol{\mu} \mathbf{g}/\mathbf{ml} lead to decreasing biomass quantity on 1- and 2-day biofilms compared to the control. We did not find the changes in biomass quantity of 5-day biofilm, but reviled the structural changes in the bacteria cell wall. We demonstrated that AgNPs could be used for effective treatment and prevention of infections caused by Enterococcus faecalis at concentrations varied from 10 to \mathbf{40} \boldsymbol{\mu} \mathbf{g}/\mathbf{ml} .
Certifying or enforcing performance and stability guarantees for controllers based on deep learning is challenging. This paper aims to provide conditions to verify nominal stability of a system ...controlled by a deep learning based controller. We focus on a special form of neural network as the controller, called non-autonomous deep networks. The training is performed using data from a baseline controller, which does not need to be known mathematically, e.g. it may be a human operating the system. We provide an explicit formula for computation of the number of hidden layers such that the resulting learning-based closed-loop system is stable. We furthermore outline how this condition can be integrated in the learning. The results are illustrated by a simulation study considering control of a continuously stirred tank reactor.
The application of nanotechnologies in the development of biomaterials has at present of potential interest for medical application. Among the polymers for this aim, chitosan (Ch) is one of the most ...promising due to its biocompatibility, biodegradability, and antibacterial modes. Electrospinning is an economy and highly reproducible procedure for the fabrication of polymeric nanofibrous membranes. This study applied the appropriate electrospinning parameters to manufacture membranes made of Ch solution in a combination of trifluoroacetic acid (TFA) and dichloromethane (DMC) as solvent systems. The ability of Ch nanofibers to influence bacterial adhesion and proliferation was enforced by the loading of silver nanoparticles (AgNPs). We demonstrated that enhancing the antibacterial potential of silver-contained Ch membranes depends on the amount of AgNPs. Otherwise, the viability and bacterial growth of the E. coli strain were lower during co-cultivation with Ch-Ag samples than S. aureus. The biofilm formation capacity was better in silver nanoparticle-free membranes. We confirmed that an increase of AgNPs content noticeable decreases the biofilm formation ability. These results demonstrate that nanofibrous Ch materials contained AgNPs in concentrations not exceeded minimum inhibitory concentrations (MIC) might be promising antibacterial material due to extensive bacterial suppression and biofilm inhibition effect.
Predictive control of uncertain nonlinear systems is challenging. Existing approaches often require to find a global minima of a nonconvex optimization problem, and often are conservative, as the ...worst case solution is considered. This paper presents a robust model predictive control scheme for Lur’e systems subject to constraints, which improves via learning over time and allows efficient implementation using Linear Matrix Inequalities. The approach utilizes Lipschitz continuity conditions for the unknown sector bounded nonlinearity. Based on reconstructions of the unknown function from past experiments and measurements, the bound on the uncertainty is improved - learned - in an set-based way. The system is controlled by a continuous time linear feedback law, where the feedback matrix used is updated in a sampled data fashion solving an infinite horizon robust control problem that guarantees constraint satisfaction and robust stability. To improve the performance, constraints based on the learned data are added to the LMI formulation, which allows to guarantee stability and satisfaction of input and state constraints. Due to convexity of the resulting LMI formulation its computational demand is low, allowing to implement the method on systems with limited computational capabilities. The effectiveness of the approach is illustrated by an example of a flexible link robotic arm.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Many control problems, such as charging of batteries or operation of (bio-)chemical processes, are repetitively operated finite-time processes. Such processes are often subject to disturbances and ...the controllers used depend on parameters for tuning. Guaranteeing safe and reliable operation that achieves the desired outcome in finite time despite uncertainties is a challenging task. We propose a set-based procedure based on a reformulation as a feasibility to identify admissible controller parameters, acceptable uncertainties and disturbances. The approach allows to directly consider bounded uncertainty, nonlinear system dynamics, as well as safety and performance requirements. It provides an outer approximation of the set of admissible controller parameters, for which the desired performance can be guaranteed. We illustrate the approach with a simulation example from battery management considering the problem of safe charging of an off-the shelf Li-ion battery.
Reliable, yet flexible operation of manufacturing systems is important for efficient and economically viable production. Model-based analysis and verification methods are becoming increasingly ...important to achieve such an operation. In this work, we outline a model-based approach to monitor and verify transportation systems commonly employed in discrete manufacturing. To provide guaranteed verification and monitoring results despite uncertainties and the event-driven nature of the considered transportation systems, we combine tailored first principle models with suitable set-based feasibility formulations. Simulation examples and results from an industrial test plant underline the performance and real-time capability of the presented approach.
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