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13th IFAC Symposium on Dynamics and control of Process Systems, including Biosystems (DYCOPS)

作者： Sbarciog, Mihaela Huet, Antoine Bogaerts, Philippe Univ Libre Bruxelles 3BIO BioControl CP 165-6150 Av FD Roosevelt B-1050 Brussels Belgium

Trehalose is a reserve carbohydrate, which occurs naturally in yeast and acts as a stress protectant for cells. Due to its features, trehalose is used in many industries (food, biopharmaceuticals, cosmetics). Its production/accumulation may be triggered and enhanced by the culture operating conditions, among which the feed flow rate and the start of the starvation period play the most important role. This paper presents a simulation study based on a novel model validated with experimental data, highlighting the interplay between trehalose maximization and biomass maximization. Several single-objective and multi-objective constrained optimization problems are solved using the control vector parameterization approach. It is shown that maximizing either the trehalose concentration or the biomass amount leads to solutions which are not appealing in practice. The best solution is obtained by maximizing trehalose concentration with a lower bound on the biomass amount. A comparison of the optimal solution to experimental data reveals that, although approximately the same amount of biomass can be obtained with different feeding profiles and starvation times, the maximization of trehalose concentration requires feeding most of the culture medium in the first part of the experiment while starting the nitrogen starvation at an early stage. Copyright (C) 2022 The Authors.

关键词： Saccharomyces cerevisiae trehalose dynamical model process optimization control vector parameterization stress protectant

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Minimum attention stochastic control with homotopy optimization

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INTERNATIONAL JOURNAL OF DYNAMICS AND control 2021年第1期9卷 266-274页

作者： Varanasi, Santhosh Kumar Jampana, Phanindra Vyasarayani, C. P. Indian Inst Technol Hyderabad Dept Chem Engn Kandi 502285 Telangana India Indian Inst Technol Hyderabad Dept Mech & Aerosp Engn Kandi 502285 Telangana India

A method of designing control inputs for tracking problems when models considered in the form of stochastic differential equations is proposed. Using the ideas of control vector parameterization, the control input identification problem is formulated as a parameter identification problem, which is solved using homotopy optimization. To further obtain a control with the least number of switchings, i.e., minimum attention control, a sparse recovery framework is developed. The accuracy of the proposed methods is demonstrated with the help of a linear second-order system and a non-linear quadruple tank system.

关键词： Stochastic differential equations Homotopy optimization control vector parameterization Sparse optimization

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Dynamic optimization of low-pressure carburizing furnaces 23

Dynamic optimization of low-pressure carburizing furnaces

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23rd International Conference on Process control (PC)

作者： Marin, Fatima Matamoros Latifi, Abderrazak M. Glaude, Pierre-Alexandre Bounaceur, Roda Monnier, Hubert Univ Lorraine Lab React & Genie Proc CNRS Nancy France Inst Natl Rech Sci Vandoeuvre Les Nancy France

ISBN: (纸本)9781665403306

This paper presents the modelling and dynamic optimization of low pressure carburizing of steel. A cyclic hybrid model alternating two modes, i.e. the two stages of the process, boost and diffusion stages, is proposed. Each mode is characterized by a set of ordinary differential equations and its corresponding transition conditions and transition functions. The subsequent dynamic optimization problem is formulated and it aims to minimize the production of toxic compounds. The decision variables selected are the operating conditions, i.e. the lengths of boost and diffusion stages as well as the inlet flowrate of carburizing gas. Equality and inequality constraints are added on the state variables to ensure the quality of the treated steel parts. The resulting optimization problem of infinite dimension is transformed into a finite-dimension problem by means of the control vector parameterization approach and solved using a gradient-based method, i.e. sequential quadratic programming method. The solution algorithm is tested on an example and the optimal profiles yield satisfactory results.

关键词： Modelling Optimization control vector parameterization Low pressure carburizing

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A Simulation Study on Model-Based Optimization of Intracellular Trehalose Accumulation in Saccharomyces cerevisiae Fed-Batch Cultures

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IFAC-PapersOnLine 2022年第7期55卷 762-767页

作者： Mihaela Sbarciog Antoine Huet Philippe Bogaerts

关键词： Saccharomyces cerevisiae trehalose dynamical model process optimization control vector parameterization stress protectant

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Optimal Standoff Distance of Subsonic Unpowered Gliding Vehicle

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RESULTS IN control AND OPTIMIZATION 2023年 12卷

作者： Mahmood, Ahmad Rehman, Fazal Ur Capital Univ Sci & Technol Elect Engn Dept Islamabad Pakistan

The inverted Y-tail joint standoff weapon is adopted as the Subsonic Unpowered Gliding Vehicle (SUGV) in this article to gain maximum standoff distance. To obtain the optimal standoff distance of SUGV, a direct approach i.e., homogeneous control vector parameterization (CVP), is employed. To achieve the maximum standoff distance, the SUGV requires to spend a maximum amount of time in the air to conduct a gliding flight, resulting in an unspecified final time optimal problem. To deal with this problem, a time scaling approach is adopted in conjunction with homogeneous CVP to achieve the maximum standoff distance. In accordance with the homogeneous CVP technique, the standoff distance of the SUGV is comparatively long in comparison to the maximum step input, and the SUGV reaches a standoff distance of 121 km with an increment of 1.77%. The optimal simulation outcomes indicate that homogeneous CVP delivers a smooth gliding flight as compared to the maximum step input, and this smoother flight results in increased standoff distance. Furthermore, to evaluate the efficiency between the homogeneous CVP approach and maximum step input, the burden on the elevators in the form of integral square value and time scaling parameter is chosen as a criterion for quantitative analysis, and this analysis reveals that the homogeneous CVP approach has less burden on the elevators than the maximum step input.

关键词： Subsonic Unpowered gliding vehicle Time scaling Homogeneous control vector parameterization Standoff distance DS Simulia Isight Maximum elevator

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Multi-objective optimal control of Docosahexaenoic Acid (DHA) production in fed-batch fermentation by Schizochytrium sp.

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BIOCATALYSIS AND AGRICULTURAL BIOTECHNOLOGY 2022年第0期45卷

作者： Rohman, F. S. Roslan, M. F. Muhammad, D. Shoparwe, N. F. Hamid, A. A. Univ Sains Malaysia Sch Chem Engn Engn CampusSeri Ampangan 14300 George Town 14300 Malaysia Univ Malaysia Kelantan Fac Bioengn & Technol Jeli Campus Jeli Kelantan 17600 Malaysia Univ Kebangsaan Malaysia Dept Biol Sci & Biotechnol Bangi 43600 Selangor Malaysia

Docosahexaenoic Acid (DHA) production from fermentation process in a fed-batch reactor is suited solved using optimal control to obtain optimal temperature and feed flowrate trajectories. This fermentation process, which involves numerous conflicting objectives, necessitates the so-lution of a multi-objective optimal control (MOOC). MOOC results, which include a variety of ideal solutions, are configured as Pareto Front (PF). The epsilon-constraint with hybrid strategy (HS), and elitist non-dominated sorting genetic algorithm (NSGA-II) have been implemented to tackle conflict bi-objectives: minimisation final time and maximizing DHA production. By computing performance measurements such as space (SP), hypervolume (HV), and pure diversity (PD), these MOOC techniques were compared to the characteristic of the Pareto solution. Due to the most precise, diverse, and desirable spread points along the PF, the epsilon-constraint technique is the most effective. Each Pareto solution point comprises a unique combination of optimal feed flowrate trajectories, resulting in a unique amount of final time and DHA concentration. These solutions provide a variety of options for evaluating trade-offs and establishing the best operating strategy.

关键词： Docosahexaenoic acid Bioreactor Fermentation Optimal control Hybrid strategy control vector parameterization Multi-objective optimization

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An adaptive sequential wavelet-based algorithm developed for dynamic optimization problems

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COMPUTERS & CHEMICAL ENGINEERING 2019年 121卷 465-482页

作者： Santos, Lizandro de Sousa Secchi, Argimiro Resende Prata, Diego Martinez Biscaia Jr, Evaristo Chalbaud Univ Fed Fluminense Sch Engn Dept Chem Engn & Petr Rua Passo Patria 156 BR-24210240 Niteroi RJ Brazil Univ Fed Rio de Janeiro PEQ COPPE Chem Engn Program Ctr Tecnol Av Horcicio Macedo 2030Bloco GSala G-116 BR-21941914 Rio De Janeiro RJ Brazil

In this paper we present an adaptive wavelet algorithm (WAA) tailored for dynamic optimization problems (DOP). The main feature of the WAA is the automatic computation of time-domain discretization, generating a self-adapting control parameterization, which depends on the nonlinear characteristics of the mathematical model. For this, the control variables are analyzed and treated at different wavelet levels. First, we have demonstrated the advantages of WAA over heuristic adaptive procedures, proposed in the last years. Second, the results of the proposed strategy are illustrated through the solution of ten case studies. According to the results, the computation cost could be reduced by about 56% on average. Besides, the average NLP size reduction was approximately 49.94%, showing that one of the most considerable advantages of the algorithm is the adaptive discretization without prior information of the control profile. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Dynamic optimization Nonlinear programming Wavelets Thresholding control vector parameterization

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Optimal control of the self-bound dipolar droplet formation process

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COMPUTER PHYSICS COMMUNICATIONS 2019年 244卷 205-216页

作者： Mennemann, J-F Langen, T. Exl, L. Mauser, N. J. Fac Math Wolfgang Pauli Inst Oskar Morgenstern Pl 1 A-1090 Vienna Austria Univ Stuttgart Phys Inst 5 Pfaffenwaldring 57 D-70550 Stuttgart Germany Univ Stuttgart Ctr Integrated Quantum Sci & Technol IQST Pfaffenwaldring 57 D-70550 Stuttgart Germany

Dipolar Bose-Einstein condensates have recently attracted much attention in the world of quantum many body experiments. While the theoretical principles behind these experiments are typically supported by numerical simulations, the application of optimal control algorithms could potentially open up entirely new possibilities. As a proof of concept, we demonstrate that the formation process of a single dipolar droplet state could be dramatically accelerated using advanced concepts of optimal control. More specifically, our optimization is based on a multilevel B-spline method reducing the number of required cost function evaluations and hence significantly reducing the numerical effort. Moreover, our strategy allows to consider box constraints on the control inputs in a concise and efficient way. To further improve the overall efficiency, we show how to evaluate the dipolar interaction potential in the generalized Gross-Pitaevskii equation without sacrificing the spectral convergence rate of the underlying time-splitting spectral method. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Quantum control Multilevel B-spline method control vector parameterization Generalized Gross-Pitaevskii equation Dipole-dipole interaction GPU computing

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Dynamic optimization of chemical processes using symbiotic organisms search algorithm

Dynamic optimization of chemical processes using symbiotic o...

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Chinese Automation Congress (CAC)

作者： Tian, Peng Chen, Xu Zhao, Wenxiang Du, Wenli Jiangsu Univ Sch Elect & Informat Engn Zhenjiang 212013 Jiangsu Peoples R China East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China

ISBN: (纸本)9781728140940

Chemical process optimization problems are often modeled as dynamic optimization problems (DOPs). Due to the nonlinear, multimodal and multi-dimensional nature of DOPs, efficient solution of DOPs is a very challenging task. In this paper, a new intelligent search algorithm called symbiotic organisms search (SOS) is proposed for tackling the chemical DOPs. SOS mainly mimics three symbiotic relationships in ecosystem, namely mutualism, commensalism and parasitism, to perform global search. In addition, compared with previous intelligent search algorithms, SOS has the advantages of no tuning parameters, easy to implement and high performance. Combined with control vector parameterization, the proposed SOS is applied to solve five chemical DOPs with different levels of complexity. Simulation results show that SOS can achieve solutions with accuracy comparable to those methods in the literature, and thus can be regarded as an effective tool for the chemical DOPs.

关键词： Chemical dynamic optimization Symbiotic organisms search Intelligent search algorithm control vector parameterization

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A moving time node method for solving robot optimal control problem 38

A moving time node method for solving robot optimal control ...

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38th Chinese control Conference (CCC)

作者： Jiang, Enhui Xu, Weifeng Jiang, Aipeng Gao, Hanhan Wang, Jian Xiao, Yangjian Hangzhou Dianzi Univ Sch Automat Hangzhou 310018 Peoples R China

ISBN: (纸本)9789881563972

Many motion control problems of robots are typical Bang-Bang optimal control problems with multi-control variables. In order to solve the contradiction between discrete approximation accuracy and computational time, and the inaccurate location of time-switching points caused by fixed time grids, a method of moving time nodes is proposed for more reasonably dividing the discrete-time grids of optimal control problems. In this method. each component of the control variable is taken as the parameter to be optimized, and the time grid distribution of each control variable is obtained. Unlike the Time-Scaling method, this method not only can effectively move the time grid nodes and obtain good time grid distribution. but also has stronger approximation ability and more flexibility. The effectiveness of the proposed method is verified by a robot optimal control example, and compared with the traditional control vector parameterization method and Time-Scaling method. The results show that the proposed method has low computational cost, accurate time-switching point location and high accuracy.

关键词： Optimal control Bang-Bang control control vector parameterization Non-uniform grid Time-Scaling Robot

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