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

共 1794 条记录，以下是141-150 订阅

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Co-simulation of the Unreal Engine and MATLAB/Simulink for Automated Grain Offloading 10th

Co-simulation of the Unreal Engine and MATLAB/Simulink for A...

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10th IFAC symposium on Advances in Automotive control (AAC)

作者： Jiang, Chufan Dhamankar, Shveta Liu, Ziping Vinod, Gautham Shaver, Gregory Evans, John Puryk, Corwin Anderson, Eric DeLaurentis, Daniel Purdue Univ W Lafayette IN 47907 USA Deere Co Moline IL 61265 USA

This paper presents a generic simulation platform with two widely employed software, Simulink and Unreal, to simultaneously simulate the perception in virtual 3D scenarios and system dynamics of automated systems. The proposed CoSim framework improves the accuracy and reduces the development time of automation systems for agricultural crop harvesting and transfer. Strategies using either cameras or LiDAR are supported by the framework. To demonstrate the capability of the proposed CoSim tool, this paper simulates an automated offloading process conducted by a combine-tractor system with a closed-loop controller and a LiDAR-based perception system. The simulation results show that CoSim can be used for both system design and system evaluation. Copyright (c) 2022 The Authors. This is an open access article under the CC BY-NC-ND license

关键词： Perception simulation driver assistance system closed-loop control

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process Optimization under Parameter Uncertainty Conditions in CCU process 13th

Process Optimization under Parameter Uncertainty Conditions ...

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13th IFAC symposium on dynamics and control of process systems, including Biosystems (DYCOPS)

作者： Jeong, Dong Hwi Univ Ulsan Sch Chem Engn Ulsan 44610 South Korea

In order to produce methanol using CO2 and H-2, several processes about the related catalytic reaction has been suggested and simulated. There may be systematic uncertainties like parameters of reaction kinetics, so a modelling method considering the parametric uncertainty is proposed and can give more informative data compared to the conventional modelling methods. However, the resulting distributional model requires large computational burdens due to the iterative calculations for convergence. To solve the problem, generalized extreme value distribution (GEVD) and neural network (NN) modellings are utilized. The formation parameters of GEVD are fitted by NN and as a result distributional reactor model in an explicit formulation is proposed. Compared to the shallow structured neural network for learning the formulation parameters, the deep neural network shows improved performance especially adjacent to the boundary layers of process inputs. As its explicit and distributional formulation, the proposed model is expected to be utilized for real-time stochastic model based approaches in optimization and control with reduced computational load. Copyright (C) 2022 The Authors.

关键词： process optimization Methanol production Carbon capture and utilization Parametric uncertainty Neural network Generalized extreme value distribution

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Dynamic Modeling and Nonlinear Model Predictive control of a Moving Bed Chemical Looping Combustion Reactor 13th

Dynamic Modeling and Nonlinear Model Predictive Control of a...

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13th IFAC symposium on dynamics and control of process systems, including Biosystems (DYCOPS)

作者： Parker, Robert B. Biegler, Lorenz T. Carnegie Mellon Univ Pittsburgh PA 15232 USA

A partial differential-algebraic equation (PDAE) model of a dynamic moving bed chemical looping combustion reduction reactor is developed in the IDAES process modeling framework for nonlinear model predictive control (NMPC). Numerical stability of the PDAE discretization and the index-1 property of the DAE are validated. An NMPC case study is performed, which manipulates inlet flow rates to control the system to a steady-state setpoint over approximately 6,000 s of simulated time, while respecting a lower bound on outlet oxygen carrier conversion. The dynamic optimization problems are solved for each NMPC cycle with an average of 39 CPUs, indicating that this model has the potential for use in a real-time scenario. Copyright (c) 2022 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 Optimization Nonlinear control Reactor control Partial differential equations

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Anaerobic Digestion processes controller Tuning Using Fictitious Reference Iterative Method 13th

Anaerobic Digestion Processes Controller Tuning Using Fictit...

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13th IFAC symposium on dynamics and control of process systems, including Biosystems, DYCOPS 2022

作者： Condrachi, Larisa Barbu, Marian Automatic Control and Electrical Engineering Department Dunarea de JosUniversity of Galati Galati Romania

In this paper, a new data-based procedure, Fictitious Reference Iterative Tuning, is proposed to control the anaerobic digestion process. In the first phase, the proposed approach uses input-output data from the anaerobic digestion process obtained by using a controller with initial parameters that ensure loop stability. In the second phase, the situation in which the input-output data are obtained in a closed-loop was also analyzed. Therefore, the Fictitious Reference Iterative Tuning method was used to obtain: a PI controller, which was tuned on the basis of an iterative, convergent and monotonous process and a PID controller, which was tuned on the basis of a divergent iterative process. The results obtained confirm the validity of the proposed Fictitious Reference Iterative Tuning method for the control of the anaerobic digestion process. © 2022 Elsevier B.V.. All rights reserved.

关键词： Iterative methods

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Recurrent Neural Network-Based Joint Chance Constrained Stochastic Model Predictive control 13th

Recurrent Neural Network-Based Joint Chance Constrained Stoc...

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13th IFAC symposium on dynamics and control of process systems, including Biosystems (DYCOPS)

作者： Yang, Shu-Bo Li, Zukui Univ Alberta Dept Chem & Mat Engn Edmonton AB Canada

A novel recurrent neural network (RNN)-based approach is proposed in this work to handle joint chance-constrained stochastic model predictive control (SMPC) problem. In the proposed approach, the joint chance constraint (JCC) is first reformulated as a quantile-based inequality to reduce the complexity in approximation. Then, the quantile function (QF) in the quantile-based inequality is replaced by the empirical QF using sample average approximation (SAA). Afterwards, the empirical QF is approximated via an RNN-based surrogate model, which is embedded into the SMPC problem formulation to predict quantile values at different sampling instants. By employing the RNN-based approximation, the resulting deterministic optimization problem is finally solved through a nonlinear optimization solver. The proposed approach is applied to a hydrodesulphurisation process to demonstrate its efficiency in handling the SMPC problem. Copyright (C) 2022 The Authors.

关键词： Recurrent neural network Stochastic model predictive control Stochastic optimal control Joint chance constraint Sample average approximation

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Optimal control of renewable biohydrogen production: A switched system approach 13th

Optimal control of renewable biohydrogen production: A switc...

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13th IFAC symposium on dynamics and control of process systems, including Biosystems (DYCOPS)

作者： Andres-Martinez, Oswaldo Ricardez-Sandoval, Luis A. Univ Waterloo Dept Chem Engn Waterloo ON N2L 3G1 Canada

Biohydrogen produced from microorganisms such as cyanobacteria is a promising low cost, sustainable and environmentally friendly energy source. Recent studies have shown that high biohydrogen yield can be obtained from Cyanothece sp. ATCC 51142 in a fed-batch reactor. This system has been accurately described with a modified Droop model that can be used for optimization studies. Searching for the optimal operating conditions and the switching time from batch to fed-batch operation, such that the biohydrogen production is maximized, leads to a challenging singular optimal control problem. In this study, a novel reformulation based on the theory of switched systems and time-scaling transformation is proposed to address the switching of the operating modes and the optimal control structure. Solutions are found by solving an embedded optimal control problem that can be solved efficiently as a nonlinear programming problem. No mesh refinement is required to capture the switching times. Smooth optimal control profiles and clear switching structures that maximize the biohydrogen yield were found for two types of control parametrization. Copyright (c) 2022 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： Singular optimal control switched systems biohydrogen production fed-batch reactors

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Implementation of Extremum Seeking control in an Experimental Lab-Rig 13th

Implementation of Extremum Seeking Control in an Experimenta...

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13th IFAC symposium on dynamics and control of process systems, including Biosystems, DYCOPS 2022

作者： Matias, Jose Myrvang, Frida Bakken Jäschke, Johannes Chemical Engineering Department Norwegian University of Science and Technology Sem Saelandsvei 4 Kjemiblokk 5 Trondheim Norway

Extremum seeking control (ESC) is an adaptive control method that can be used for production optimization. In the standard ESC approach, the process optimum is found by slowly adapting the inputs based on plant gradient approximations. Since ESC "learns" the system characteristics online using only plant measurements, it requires an appropriate time-scale separation between the system response, the excitation signal used for gradient approximation, and the dynamics to be optimized. Krishnamoorthy et al. (2019) proposed a dynamic ESC that identifies a local linear dynamic model using transient measurements and applies this model to compute the plant gradients. Consequently, the time-scale separation between the system response and the excitation signal is not required and the scheme converges to the optimum faster than the standard ESC. Albeit attractive, some practical challenges have been identified, such as gradient computation and constraint handling. The main contribution of this paper is to investigate how to overcome these challenges in a more realistic setting, in which dynamic ESC is implemented on a lab-scale plant that emulates a subsea oil well network. We compare the performance of two gradient computation approaches and present a constraint handling strategy for the system of interest, where multiple units compete for limited resources. The results show that dynamic ESC is able to drive the system to its optimum without constraint violations. © 2022 Elsevier B.V.. All rights reserved.

关键词： control theory

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Hybrid Model Predictive control for Hybrid Electric Vehicle Energy Management Using an Efficient Mixed-Integer Formulation 13th

Hybrid Model Predictive Control for Hybrid Electric Vehicle ...

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13th IFAC symposium on dynamics and control of process systems, including Biosystems (DYCOPS)

作者： Jung, Hyein Oh, Tae Hoon Park, Hyun Min Lee, Heeyun Lee, Jong Min Seoul Natl Univ Chem & Biol Engn Dept Seoul South Korea Hyundai Motor Co Electrifcat Control Dev Team 1 Hwaseong Si 18280 South Korea

This paper proposes to use hybrid model predictive control (HMPC) for energy management in hybrid electric vehicle (HEV) using an efficient formulation. HEV has two sources of energy - electric motor and internal combustion engine (ICE) - allowing it an additional degree of freedom to optimize the ratio between the use of two energy sources. HEV energy management is crucial to exploit its potential to reduce fuel consumption and emissions. However, it is challenging to achieve the optimal solution along with the fast dynamics of HEVs. In this paper, instead of using a nonlinear, computationally expensive dynamic model of HEV, a piecewise afne (PWA) model is used to depict its behavior, implemented with multiple linear regression. The validation of the developed model is taken with standard drive cycles. Based on the PWA model, an optimal control problem of HMPC was formulated as mixed integer linear programming (MILP). Conventionally, mixed logical dynamical (MLD) system has been used in the process control field, including early studies of HEV control. This paper applies Big-M formulation which is efficient in its problem size and tight inequality relation for integer variables. The performance of the HMPC controller was examined in a simulated environment based on MATLAB/Simulink HEVP2 application. As a result, HMPC shows superior control performance than the equivalent consumption minimization strategy (ECMS). Copyright (c) 2022 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： Model predictive control Dynamic modelling andsimulation for control andoperation process control

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A Data-Driven Model-Reference Adaptive control Approach Based on Reinforcement Learning 14

A Data-Driven Model-Reference Adaptive Control Approach Base...

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IEEE International symposium on Robotic and Sensors Environments (ROSE)

作者： Abouheaf, Mohammed Gueaieb, Wail Spinello, Davide Al-Sharhan, Salah Bowling Green State Univ Coll Technol Architecture & Appl Engn Bowling Green OH 43402 USA Univ Ottawa Sch Elect Engn & Comp Sci Ottawa ON K1N 6N5 Canada Univ Ottawa Dept Mech Engn Ottawa ON K1N 6N5 Canada Machine Intelligence Res Labs Auburn WA 98071 USA

ISBN: (纸本)9780738113692

Model-reference adaptive systems refer to a consortium of techniques that guide plants to track desired reference trajectories. Approaches based on theories like Lyapunov, sliding surfaces, and backstepping are typically employed to advise adaptive control strategies. The resulting solutions are often challenged by the complexity of the reference model and those of the derived control strategies. Additionally, the explicit dependence of the control strategies on the process dynamics and reference dynamical models may contribute in degrading their efficiency in the face of uncertain or unknown dynamics. A model-reference adaptive solution is developed here for autonomous systems where it solves the Hamilton-Jacobi-Bellman equation of an error-based structure. The proposed approach describes the process with an integral temporal difference equation and solves it using an integral reinforcement learning mechanism. This is done in real-time without knowing or employing the dynamics of either the process or reference model in the control strategies. A class of aircraft is adopted to validate the proposed technique.

关键词： Model-Reference control Integral Bellman Equation Integral Reinforcement Learning Adaptive Critics

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Long-term dependency slow feature analysis for dynamic process monitoring 16

Long-term dependency slow feature analysis for dynamic proce...

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16th IFAC symposium on Advanced control of Chemical processes (ADCHEM)

作者： Gao, Xinrui Shardt, Yuri A. W. Tech Univ Ilmenau D-98684 Ilmenau Thuringia Germany

Industrial processes are large scale, highly complex systems. The complex flow of mass and energy, as well as the compensation effects of closed-loop control systems, cause significance cross correlation and autocorrelation between process variables. To operate the process systems stably and efficiently, it is crucial to uncover the inherent characteristics of both variance structure and dynamic relationship. Long-term dependency slow feature analysis (LTSFA) is proposed in this paper to overcome the Markov assumption of the original slow feature analysis to understand the long-term dynamics of processes, based on which a monitoring procedure is designed. A simulation example and the Tennessee Eastman process benchmark are studied to show the performance of LTSFA. The proposed method can better extract the system dynamics and monitor the process variations using fewer slow features. Copyright (C) 2021 The Authors.

关键词： process monitoring slow feature analysis fault diagnosis latent variable model

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