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检索条件"主题词=Model Predictive and Optimization-based Control"

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Distributed Estimation and control of Process Networks using Adaptive Community Detection 12

Distributed Estimation and Control of Process Networks using...

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12th IFAC Symposium on Advanced control of Chemical Processes (ADCHEM)

作者： Ebrahimi, Amirmohammad Pourkargar, Davood B. Kansas State Univ Tim Taylor Dept Chem Engn Manhattan KS 66506 USA

An integrated distributed moving horizon estimation (DMHE) and model predictive control (DMPC) approach is developed for complex process networks using an adaptive spectral community detection-based decomposition. The proposed approach employs the weighted graph representation of the process network model to identify optimal communities for distributed estimation and control architectures. The resulting decomposition dynamically adapts as the network transitions across different operating conditions. Consequently, adjustments are made to the integrated DMHE and DMPC architecture to optimize closed-loop performance and enhance robustness. A benchmark benzene alkylation process under various operating conditions is employed to substantiate the proposed methodology's efficacy. Simulation results demonstrate the effectiveness of the proposed method, showing improved closed-loop performance and computational efficiency compared to traditional unweighted hierarchical community detection-based decompositions. Copyright (c) 2024 The Authors.

关键词： Nonlinear process control model predictive and optimization-based control distributed control and estimation graph-based methods for networked systems process control

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Supervisory multi-model control for supply chains with large uncertainty on the perishability rate 18

Supervisory multi-model control for supply chains with large...

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18th IFAC Symposium on Information control Problems in Manufacturing (INCOM)

作者： Ietto, Beatrice Orsini, Valentina Weizenbaum Inst Berlin Germany Univ Politecn Marche Dept Informat Engn Ancona Italy

Defining an efficient inventory management of Perishable Supply Chains (PSCs) becomes a very difficult problem when the "a priori" information on the perishability rate is affected by a large degree of uncertainty. In this paper we face this problem using a multi-model based supervisor and a reconfigurable min-max Robust model predictive control (RMPC). The twofold task of the supervisor is: 1) identify the most likely uncertainty scenario, 2) drive towards it the current min-max MPC law. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： supply chain management inventory control control and optimization of supply chains model predictive and optimization-based control uncertain systems

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Minimum-Variance model predictive control for Dual Fluidized Bed Circulation control 22

Minimum-Variance Model Predictive Control for Dual Fluidized...

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22nd World Congress of the International Federation of Automatic control (IFAC)

作者： Stanger, Lukas Schirrer, Alexander Bartik, Alexander Kozek, Martin TU Wien Inst Mech & Mechatron Getreidemarkt 9 A-1060 Vienna Austria TU Wien Inst Chem Environm & Biosci Engn Getreidemarkt 9 A-1060 Vienna Austria

ISBN: (纸本)9781713872344

Dual fluidized bed steam gasification enables the production of gaseous energy carriers from woody biomass or biogenic residues. The circulation of bed material in dual fluidized bed gasifiers strongly affects the process behavior. Therefore, precise control of the bed material circulation is desired. This paper presents a control algorithm addressing two aspects of the given problem setting: On the one hand, redundant control actuators are available. Typically, there are several air streams to the reactors influencing the bed material circulation. On the other hand, only black box models with uncertainties in their model parameters are available for model-based control design. The presented control algorithm uses a model predictive controller considering known uncertainties in the model parameters and drives the process in a region with the lowest model uncertainties. This results in an improvement of the closed-loop performance when the actual plant deviates from the internal model used for the model predictive control predictions. Simulations show 66 % less offset from the design trajectory with the presented algorithm when compared to a standard model predictive controller. Copyright (c) 2023 The Authors.

关键词： model predictive and optimization-based control Process control applications model uncertainties control allocation Dual fluidized bed gasification

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Anderson Accelerated Feasible Sequential Linear Programming 22

Anderson Accelerated Feasible Sequential Linear Programming

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22nd World Congress of the International Federation of Automatic control (IFAC)

作者： Kiessling, David Pas, Pieter Astudillo, Alejandro Patrinos, Panagiotis Swevers, Jan Katholieke Univ Leuven Flanders Make KU Leuven Dept Mech Engn MECO Res Team B-3001 Leuven Belgium Katholieke Univ Leuven Dept Elect Engn ESAT STADIUS Ctr Dynam Syst Signal Proc & Data Analyt Leuven Belgium

ISBN: (纸本)9781713872344

This paper proposes an accelerated version of Feasible Sequential Linear Programming (FSLP): the AA(d)-FSLP algorithm. FSLP preserves feasibility in all intermediate iterates by means of an iterative update strategy based on repeated evaluation of zero-order information. This technique was successfully applied to techniques such as model predictive control and Moving Horizon Estimation, but it can exhibit slow convergence. Moreover, keeping all iterates feasible in FSLP entails a large number of additional constraint evaluations. In this paper, Anderson Acceleration (AA(d)) is applied to the zero-order update strategy improving the convergence rate and therefore decreasing the number of constraint evaluations in the inner iterative procedure of the FSLP algorithm. AA(d) achieves an improved contraction rate in the inner iterations, with proven local linear convergence. In addition, it is observed that due to the improved zero-order update strategy, AA(d)-FSLP takes larger steps to find an optimal solution, yielding faster overall convergence. The performance of AA(d)-FSLP is examined for a time-optimal point-to-point motion problem of a parallel SCARA robot. The reduction of the number of constraint evaluations and overall iterations compared to FSLP is demonstrated. Copyright (c) 2023 The Authors.

关键词： Numerical methods for optimal control model predictive and optimization-based control predictive control

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Optimal privacy protection of mobility data: a predictive approach 22

Optimal privacy protection of mobility data: a predictive ap...

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22nd World Congress of the International Federation of Automatic control (IFAC)

作者： Molina, Emilio Fiacchini, Mirko Cerf, Sophie Robu, Bogdan Univ Grenoble Alpes CNRS Grenoble INP GIPSA Lab F-38000 Grenoble France Univ Lille Inria CNRS Cent LilleUMR 9189CRIStAL F-59000 Lille France

ISBN: (纸本)9781713872344

Location data are extensively used to provide geo-personalized contents to mobile devices users. Sharing such personal data is a major threat to privacy, with risks of re-identification or inference of sensitive information. Location data broadcasted to services can be sanitized, i.e., by adding noise to spatial coordinates. Such protection mechanisms from the literature are widely generic, e.g., not specific to a user and mobility properties. In this work, we advocate that taking into account the specificities of location data (temporal correlation, human mobility patterns, etc.) enables to gain in the privacy-utility trade-off. Specifically, using future mobility prediction greatly improves privacy. We present a novel protection mechanism, based on model predictive control (MPC). The sanitized location is optimally computed so that it maximizes privacy while guaranteeing a utility loss constraint, for present and future locations. Our formulation explicitly takes into account non-constant sampling time, due to moments when no location data is broadcasted. We evaluate experimentally our control on real mobility dataset and compare to the state of the art. Results show that with knowledge of user's future mobility over a few of minutes, we can gain up to 10% of privacy compared to state of the art, while preserving data utility. Copyright (C) 2023 The Authors.

关键词： Security and privacy model predictive and optimization-based control predictive control

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Trajectory-dependent control Lyapunov Functions for Discrete-time Switched Systems 22

Trajectory-dependent Control Lyapunov Functions for Discrete...

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22nd World Congress of the International Federation of Automatic control (IFAC)

作者： Becerra, Gerardo Minh Tu Pham Patino, Diego Lin-Shi, Xuefang UNAD Bogota Colombia Univ Claude Bernard Lyon 1 INSA Lyon Univ Lyon Ecole Cent LyonCNRSAmpereUMR5005 F-69621 Villeurbanne France Pontificia Univ Javeriana Dept Elect Engn Bogota Colombia

ISBN: (纸本)9781713872344

A method for control of discrete-time switched systems based on parameterized Lyapunov functions is presented. A polynomial representation of the switched system is formulated, and the method of moments is applied to obtain a relaxed model. The control problem is recast as an online receding horizon optimization problem where the Lyapunov function parameters and moment sequences are the decision variables. A novel procedure to synthesize the switching control signal from the recovered measure is described. The method is illustrated in simulation for the case of a pulse-width modulated multicellular converter, showing good reference tracking results.

关键词： control of switched systems Lyapunov methods model predictive and optimization-based control polynomial methods modeling and simulation of power systems

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model-predictive control of irrigation - a case study in India 22

Model-predictive control of irrigation - a case study in Ind...

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22nd World Congress of the International Federation of Automatic control (IFAC)

作者： Singh, Dilpreet Subramanian, Sankaranarayanan Engell, Sebastian TU Dortmund Dept Biochem & Chem Engn Proc Dynam & Operat Grp Dortmund Germany

ISBN: (纸本)9781713872344

Agriculture is a major consumer of freshwater, in particular in countries like India. From the perspectives of crop yield and water conservation, the development of advanced model-based irrigation scheduling methods is desirable. Most of the farming in India is done by small-scale farmers and to serve their needs, we are developing a simple model-based system that can be applied semi-manually and does not need expensive sensors and costly data. In this article, an optimal irrigation system, based on model predictive control (MPC), which uses a physics-based model to compute an optimal amount of irrigation for the next day, is developed. The robustness of the developed system is tested by comparing the effect of errors in a key model parameter as well as in the weather forecast. Copyright (c) 2023 The Authors. This is an open access article under the CC BY-NC- ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： modeling and control of agriculture model predictive and optimization-based control precision agriculture

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A Review of Parameterised MPC Algorithms 22

A Review of Parameterised MPC Algorithms

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22nd World Congress of the International Federation of Automatic control (IFAC)

作者： Rossiter, John A. Gonzalez-Villareal, Oscar J. Sarbini, Mohammad A. M. Univ Sheffield Dept Automat Control & Syst Engn Sheffield S1 3JD S Yorkshire England Univ Teknol Brunei Elect & Elect Engn Programme Area Bandar Seri Begawan Brunei

ISBN: (纸本)9781713872344

This paper provides an evaluation and comparison of popular parameterised model predictive control approaches that have been proposed in the literature in recent years. Using the Generalised predictive control (GPC) algorithm as the baseline algorithm, the paper sets out a number of performance criteria to compare and contrast with several other MPC approaches. Numerical examples use 100 random samples of 2, 3, and 4-state models and the approaches are compared using the selected performance criteria.

关键词： predictive control model predictive and optimization-based control Monte Carlo methods

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MPC for Artificial Muscles Using FRIT based Optimized Pseudo Linearization model 22

MPC for Artificial Muscles Using FRIT based Optimized Pseudo...

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22nd World Congress of the International Federation of Automatic control (IFAC)

作者： Sekine, Mikiya Tsuruhara, Satoshi Ito, Kazuhisa Shibaura Inst Technol Grad Sch Engn & Sci Mech Engn 307 Fukasaku Saitama 3378570 Japan Shibaura Inst Technol Dept Machinery & Control Syst 307 Fukasaku Saitama 3378570 Japan

ISBN: (纸本)9781713872344

A McKibben-type artificial muscle exhibits strong asymmetric hysteresis characteristics. In a previous study, fictitious reference iterative tuning (FRIT) was applied to a 1-DOF rotational actuator applying these muscles because it was able to determine the controller parameters using only a pair of single closed-loop data. However, it is generally difficult to design a reference model properly without information on the plant, which is strongly related to the control performance, and sometimes this leads to performance degradation. To solve this problem, in this study, we propose a method combining FRIT and model predictive control (MPC). First, we define the pseudo linearization (PL) model, which is adopted as a predictor in MPC, and is used to optimization between the model and design a control system by matching the closed-loop system to the PL model using FRIT. Additionally, it can consider input constraints by combined MPC. Copyright (c) 2023 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Data-driven control model predictive and optimization-based control Motion control systems Artificial muscle controller parameter tuning Pseudo Linearization

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Safety-Critical control for Ensemble Systems 22

Safety-Critical Control for Ensemble Systems

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22nd World Congress of the International Federation of Automatic control (IFAC)

作者： Guo, Yang Petzke, Felix Rumschinski, Philipp Streif, Stefan Tech Univ Chemnitz Automat Control & Syst Dynam Lab Chemnitz Germany Hsch Furtwangen Fac Mech & Med Engn Furtwangen Germany

ISBN: (纸本)9781713872344

In this paper, we derive set constraints for a reduced order model and augment them into a model predictive control (MPC) scheme to ensure safe operation of the largescale ensemble system. For the control feedback, only the aggregated information of the whole system is required. For the constraint satisfaction, we consider an adaptive tube formulation to characterize the deviation between the reduced order model and the ensemble system. Employing the robust control invariant set, we ensure recursive feasibility and initial feasibility under an easily verifiable condition.

关键词： robust control model predictive and optimization-based control constrained control linear systems feasibility ensemble systems

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