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检索条件"任意字段=Symposium on Dynamics and Control of Process Systems"

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MPC with Machine Learning Applied to Resource Allocation Problem using Lambda Architecture 12

MPC with Machine Learning Applied to Resource Allocation Pro...

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12th International-Federation-of-Automatic-control (IFAC) symposium on dynamics and control of process systems including Biosystems (DYCOPS)

作者： Dal Pont, Mauricio P. Ferreira, Raul S. Teixeira, Wendell W. Lima, Daniel M. Normey-Rico, Julio E. Univ Fed Santa Catarina Automat & Syst Dept Florianopolis SC Brazil Radix Software & Engn Res & Dev Dept Rio De Janeiro RJ Brazil CPFL Energy Res & Dev Dept Campinas SP Brazil

The resource allocation problem is the process of allocating limited resources for a vast amount of tasks. Within this problem there are several important variants such as the stochastic time-variant resource allocation problem. This problem is relevant within environments where the distribution of resources varies with time, bringing difficulties to forecasting. Related research generally address the problem by using model predictive control (MPC) techniques or machine learning (ML) algorithms. However, both can be applied together in order to improve the tasks prioritization and forecasting. Therefore, this paper proposes a solution using the concept of lambda architecture in order to tackle the time-variant and the distinct input information. First results show that the integration between MPC and ML prioritizes the resource allocation and a Markov chain model is capable of forecasting tasks, optimizing a strategic binary control. We analyze a case study of a real problem and show how the proposal was built and its advantages over the traditional method. (C) 2019, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Predictive control machine learning Markov models stochastic control lambda architecture resource allocation binary decision systems energy distribution

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Open-source low-cost Hardware-in-the-loop simulation platform for testing control strategies for artificial pancreas research 12

Open-source low-cost Hardware-in-the-loop simulation platfor...

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12th International-Federation-of-Automatic-control (IFAC) symposium on dynamics and control of process systems including Biosystems (DYCOPS)

作者： Fernanda Quesada, Luisa Rojas, Jose David Arrieta, Orlando Vilanova, Ramon Univ Costa Rica Escuela Ingn Elect San Jose Costa Rica Univ Costa Rica Inst Invest Ingn San Jose Costa Rica Univ Autonoma Barcelona Dept Telecommun & Syst Engn Barcelona Spain

Artificial pancreas control is an important research area in the biomedical field. However, it is dangerous to test new control algorithms on humans in order to improve the performance of the control system. This paper presents the results of using an open-source low-cost hardware in the loop platform to test different control strategies for artificial pancreas research. An Arduino platform was selected as the main device to implement the real time differential equations solver needed for the HIL simulation. The platform was successfully tested with both a PID controller and an LQR controller. The code and schematics of the platform are available upon request. (C) 2019, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： controlled system insulin control Hardware in the loop PID control Optimal control

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Sequential l1 Quadratic Programming for Nonlinear Model Predictive control 12

Sequential l1 Quadratic Programming for Nonlinear Model Pred...

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12th International-Federation-of-Automatic-control (IFAC) symposium on dynamics and control of process systems including Biosystems (DYCOPS)

作者： Boiroux, Dimitri Jorgensen, John Bagterp Tech Univ Denmark Dept Appl Math & Comp Sci DK-2800 Lyngby Denmark

In this paper, we present and describe a computationally efficient sequential l(1) quadratic programming (Sl(1)QP) algorithm for Nonlinear Model Predictive control (NMPC). We use a tailored trust region sequential quadratic programming for the solution of the optimal control problem (OCP) involved in the NMPC algorithm. We use a multiple shooting approach for numerical integration and sensitivity computation. A second order correction ensures a faster convergence of the SQP algorithm. We exploit the structure of the OCP by using an efficient primal-dual interior point algorithm based on Riccati factorizations and a block diagonal BFGS update of the Hessian matrix. The complexity scales linearly with the prediction horizon length. We numerically evaluate and compare the performance of our algorithm on a numerical example. (C) 2019, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Nonlinear model predictive control trust region algorithm sequential quadratic programming

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Hybrid NMPC Applied to a Solar-powered Membrane Distillation System 12

Hybrid NMPC Applied to a Solar-powered Membrane Distillation...

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12th International-Federation-of-Automatic-control (IFAC) symposium on dynamics and control of process systems including Biosystems (DYCOPS)

作者： Gil, Juan D. Mendes, Paulo R. C. Andrade, G. A. Roca, Lidia Normey-Rico, Julio E. Berenguel, Manuel Univ Almeria Ctr Mixto CIESOL CeiA3 Ctra Sacramento S-N Almeria 04120 Spain CIEMAT Plataforma Solar Almeria Ctra Series S-N Tabernas 04200 Almeria Spain Univ Fed Santa Catarina Dept Automat & Syst Florianopolis SC Brazil

This work proposes a hybrid control strategy for the optimal operation of a solar membrane distillation facility. This kind of plants presents a hybrid nature allowing their operation in several modes, which must be properly selected according to the operating conditions. Thus, the control algorithm is based on a mixed logical dynamical characterization of the solar membrane distillation facility, combined with the application of a practical nonlinear model predictive control strategy for calculating the optimal control actions. The main objectives of the control system are to increase the operating temperature and the distillate production of the membrane distillation module, as well as the number of operational hours. For these goals, the control algorithm has to select the most appropriate operating mode and the optimal operating points in terms of water flow rate at each sampling time. Simulation results are presented for evidencing the benefits of the proposed control approach. (C) 2019, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Mixed Logical Dynamical systems Model Predictive control Solar Energy Desalination

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Fast Generalized Predictive control Based on Accelerated Dual Gradient Projection Method 12

Fast Generalized Predictive Control Based on Accelerated Dua...

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12th International-Federation-of-Automatic-control (IFAC) symposium on dynamics and control of process systems including Biosystems (DYCOPS)

作者： Peccin, Vinicius Berndsen Lima, Daniel Martins Costa Flesch, Rodolfo Cesar Normey-Rico, Julio Elias Univ Fed Santa Catarina Automat & Syst Dept Florianopolis SC Brazil Univ Fed Santa Catarina Engn Dept Blumenau SC Brazil

In general, model predictive control (MPC) requires the computation of a quadratic programming problem (QP) at each sampling instant. This computation can be considered costly from the computational point of view and become a limitation for the use of MPC in plants with fast sampling rates. In order to circumvent this limitation and allow it to be used on a larger variety of systems, special solvers which efficiently compute the control signal can be used and implemented using high-speed hardware. Several works were proposed for this type of solution, but most of them focus on state-space formulations for MPC, which are very popular in academia. This paper proposes a solution based on the accelerated dual gradient projection method, applied to generalized predictive control, which is a very popular formulation in industry. The method is firstly validated using MATLAB (R) and its results are compared with the ones presented by quadprog solver. A small size system is also evaluated in an FPGA with the QP computed in microseconds. (C) 2019, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： embedded optimal control FPGA GPAD model predictive control

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Practical Nonlinear Model Predictive control Algorithm for Long Short-Term Memory Networks 12

Practical Nonlinear Model Predictive Control Algorithm for L...

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12th International-Federation-of-Automatic-control (IFAC) symposium on dynamics and control of process systems including Biosystems (DYCOPS)

作者： Schwedersky, Bernardo B. Flesch, Rodolfo C. C. Dangui, Hiago A. S. Univ Fed Santa Catarina Programa Posgrad Automacao & Sistemas Florianopolis SC Brazil Univ Fed Santa Catarina Dept Automacao & Sistemas Florianopolis SC Brazil

This paper presents a practical nonlinear model predictive control algorithm using Long Short Term Memory (LSTM) artificial neural networks as process model. The controller is based on the Practical Nonlinear Predictive controller (PNMPC) algorithm, a general framework which can be used for the implementation of nonlinear model predictive controllers using nonlinear models of almost any class. LSTM has gained attention during the last years for its ability to build high fidelity models in data-driven problems, such as system identification. In the proposed control algorithm, the LSTM model is used to obtain on-line a nonlinear prediction of the system free response and a linearized version of the neural model is used to obtain a local approximation of the system step response, which is used to build the dynamic matrix of the system at each sampling instant. The proposed controller was tested in a benchmark neutralization reactor control problem. The process was identified using the LSTM approach and the identification and closed-loop results are compared with linear and Hammerstein nonlinear models. The LSTM approach outperformed the other alternatives in both identification and control problems. The results obtained in this study show that the proposed control algorithm is an option to use a state-of-the-art nonlinear MPC in a computationally affordable way. (C) 2019, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： practical nonlinear model predictive control long short-term memory nonlinear system identification recurrent neural networks

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A Novel Phenomenological Based Model of an Upstream Hydro-classifier for control Purposes 12

A Novel Phenomenological Based Model of an Upstream Hydro-cl...

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12th International-Federation-of-Automatic-control (IFAC) symposium on dynamics and control of process systems including Biosystems (DYCOPS)

作者： Munoz, Diego A. Serna, Rene Obando, Andres F. Alvarez, Hernan UPB OPTIMO Optimizac Matemat Proc Circular 1 Medellin 7001 Colombia Grp Corona Suministros Colombia SUMICOL Medellin Colombia Univ Nacl Colombia KALMAN Grp Invest Proc Dinam Cra 80 N 65-223 Medellin Colombia

Hydro-classification is one of the most important operation to separate particles according with their size and specific gravity. This operation involves many phenomena and its modeling is still not clear. Complex models demand high computational resources and are used to design the dimension of devices or predict the classification but off-line. However, for control proposes, it is necessary to get a suitable model with acceptable prediction level to be implemented online. This work presents a phenomenological based semi-physical model (PBSM) that describes the phenomena of the hydro-classification and forecasts its performance according to the operative conditions, summarizing all phenomena in the main physical properties of production. (C) 2019, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Hydro-classifier solid pulps solids separation forces balance phenomenological based semi-physical model

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NMPC integrated with optimization layer in offshore production 12

NMPC integrated with optimization layer in offshore producti...

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12th International-Federation-of-Automatic-control (IFAC) symposium on dynamics and control of process systems including Biosystems (DYCOPS)

作者： Ribeiro, Leonardo D. Secchi, Argimiro R. dAvila, Thiago Univ Fed Rio de Janeiro Chem Engn Program COPPE Ctr Tecnol Av Horcio Macedo 2030Bloco GSala G-115 BR-21941914 Rio De Janeiro RJ Brazil

In offshore oil production, the occurrence of oscillatory flow regime that causes instabilities is very common, resulting in production losses, occasioned by shutdown, and environmental fine due to the violation of quality requirements. In this work, we evaluate the capability of a nonlinear model predictive controller (NMPC) integrated with an optimization layer to maximize production, minimize energy consumption and mitigate impacts caused by slug flow regimes present in offshore processes. The objective function used in the optimization problem is formulated with the oil and gas production as profit and energy consumption as cost. The degree of freedom of the optimization problem are the topside pressure in the processing plant and the gas-lift injection flow rate in each well. The constraints are the BSW (Basic and Water sediments) and TOG (Grease and Oil content) specifications, capacity of the compression unit and the limits of gas-lift injection, in each well and total flow. The simulated offshore production system encompasses the subsea well, the flowline, the riser, and the topside platform. The subsea part, with four wells, was modeled in OLGA simulator and integrated with a simulated platform in EMSO simulator. The control problem was formulated with nine manipulated variable: gas lift injection and opening choke valve in each well, and interface level in the separator drum. The results show that the controller can reject the slug disturbances, keeping the BSW and TOG inside the limits and driving the process to the setpoints coming from the optimization layer. (C) 2019, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： NMPC Optimization Offshore control and Simulation

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Adaptive Flux Variability Analysis: A Tool To Deal With Uncertainties 12

Adaptive Flux Variability Analysis: A Tool To Deal With Unce...

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12th International-Federation-of-Automatic-control (IFAC) symposium on dynamics and control of process systems including Biosystems (DYCOPS)

作者： Abbate, T. Dewasme, L. Bogaerts, Ph Vande Wouwer, A. Univ Mons Automat Control Lab Blvd Dolez 31 B-7000 Mons Belgium Univ Libre Bruxelles Brussels Sch Engn BioControl 3BIO Av FD Roosevelt 50CP 165-61 B-1050 Brussels Belgium

Underdetermined metabolic networks are usually investigated using Flux Variability Analysis (FVA) under the pseudo-steady state assumption of the internal metabolites. When a dynamic overview of the flux map is sought, the time variation of the cell uptake and excretion rates is deduced from extracellular dynamic mass balances and smoothing of the measurement data of the time evolution of the extracellular concentrations. Nevertheless, the resulting system of equations does not always admit a feasible solution under these constraints. Indeed, measurement data is affected by noise, whose processing (smoothing, etc) always entails some subjective user choices, and the network itself might not be perfectly suited to explain the several culture phases. To alleviate these adverse effects, the constraints can be relaxed by introducing coefficients of variation of the external fluxes, and by considering an interval representation of the fluxes. This work presents a systematic method to determine these coefficients of variation, along the time course of the culture, leading to an adaptive scheme where the coefficients are set to the tightest bounds. The methodology is applied to experimental data of cultures of hybridoma in batch and perfusion modes and compared to previously published results. (C) 2019, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Metabolix Flux Analysis Metabolic Network Mathematical Modelling Flux Variability Analysis Underdetermined systems Biotechnology

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Modifier-Adaptation Schemes Employing Gaussian processes and Trust Regions for Real-Time Optimization 12

Modifier-Adaptation Schemes Employing Gaussian Processes and...

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12th International-Federation-of-Automatic-control (IFAC) symposium on dynamics and control of process systems including Biosystems (DYCOPS)

作者： Chanona, E. A. del Rio Alves Graciano, J. E. Bradford, E. Chachuat, B. Imperial Coll London Dept Chem Engn CPSE London England Univ Sao Paulo Escola Politecn Dept Engn Qum Sao Paulo Brazil Norwegian Univ Sci & Technol Engn Cybernet Trondheim Norway

This paper investigates modifier-adaptation schemes based on Gaussian processes to handle plant-model mismatch in real-time optimization of uncertain processes. Building upon the recent work by Ferreira et al. [European control Conference, 2018], we present two improved algorithms that rely on trust-region ideas in order to speed-up and robustify the approach. The first variant introduces a conventional trust region on the input variables, whose radius is adjusted based on the Gaussian process predictors' ability to capture the cost and constraint mismatch. The second variant exploits the variance estimates from the Gaussian processes to define multiple trust regions directly on the cost and constraint predictors. These algorithms are demonstrated and compared on a Williams-Otto reactor benchmark problem. (C) 2019, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： real-time optimization modifier adaptation Gaussian process trust region

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