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nonlinear model predictive control of a Distillation Column Using Wavenet Based Hammerstein model

ENGINEERING LETTERS 2012年第4期20卷 330-335页

作者： Ramesh, K. Hisyam, A. Aziz, N. Shukor, S. R. Abd Univ Malaysia Pahang Fac Chem & Nat Resources Engn Proc Syst Engn Grp Kuantan 26300 Pahang Malaysia Univ Sains Malaysia Seri Ampangan Sch Chem Engn Engn Campus Nibong Tebal 14300 Pulau Pinang Malaysia

Distillation columns are fairly complex multivariable systems and needs to be controlled close to optimum operating conditions because of economic incentives. nonlinear model predictive control (NMPC) scheme is one of the best options to be explored for proper control of distillation columns. In the present work, a new wavenet based Hammerstein model NMPC has been developed to control distillation column. An experimentally validated equilibrium model was used as plant model in nonlinear system identification and in NMPC. Two multiple-input-single-output (MISO) wavenet based Hammerstein models are developed to model the dynamics of the distillation column. The nonlinear model parameters were estimated using iterative predictionerror minimization method. The Unscented Kalman Filter (UKF) was used to estimate the state variables in NMPC and the NLP problem was solved using sequential quadratic programming (SQP) method. The closed loop control studies have indicated that the performance of developed NMPC scheme was good in controlling the distillation column.

关键词： Distillation column nonlinear model predictive control Sequential quadratic programming Wavenet based Hammerstein model

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nonlinear model predictive control of an Oil Well with Echo State Networks

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IFAC-PapersOnLine 2018年第8期51卷 13-18页

作者： Jordanou, Jean P. Camponogara, Eduardo Antonelo, Eric Aislan Schmitz Aguiar, Marco Aurélio Federal University of Santa Catarina FlorianópolisSC88040 Brazil Interdisciplinary Centre for Security Reliability and Trust University of Luxembourg Luxembourg Norwegian University of Science and Technology TrondheimNO-7491 Brazil

In oil production platforms, processes are nonlinear and prone to modeling errors, as the flow regime and components are not entirely known and can bring about structural uncertainties, making the design of predictive control algorithms a challenge. In this work, an efficient data-driven framework for model predictive control (MPC) using Echo State Networks (ESN) as the prediction model is proposed. Unlike previous works, the ESN model for MPC is only linearized partially: while the free response of the system is kept fully nonlinear, only the forced response is linearized. This MPC framework is known in the literature as the Practical nonlinear model predictive controller (PNMPC). In this work, by using the analytically computed gradient from the ESN model, a finite difference method is not needed to compute derivatives as in PNMPC. The proposed method, called PNMPC-ESN, is applied to control a simplified model of a gas-lifted oil well, managing to successfully control the plant, obeying the established constraints while maintaining setpoint tracking. © 2018

关键词： model predictive control Finite difference method Gallium Identification (control systems) Linearization nonlinear systems Oil wells predictive control systems Production platforms Uncertainty analysis Echo state networks Gas lifted oil wells Non linear model nonlinear model predictive control Oil production platforms predictive control algorithm Set point tracking Structural uncertainty

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nonlinear model predictive control of the Czochralski Process

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IFAC-PapersOnLine 2016年第20期49卷 120-125页

作者： Rahmanpour, Parsa Sælid, Steinar Hovd, Morten Grønning, Oddvar Jomaa, Moez TrondheimNO-7491 Norway Prediktor AS Fredrikstad Norway Norway Sintef Oslo Norway

Commercially, the Czochralski process is the most commonly applied method for production of monocrystalline silicon for semiconductor and solar cell applications. The process has traditionally been controlled using nested single-loop controllers. Due to the complex and nonlinear nature of the Cz process, this paper considers model-based control of the process with application of nonlinear model predictive controllers. © 2016

关键词： model predictive control controllers Monocrystalline silicon nonlinear systems predictive control systems Czochralski process model based controls Non linear model nonlinear model predictive control nonlinear nature Single loop controllers Solar cell applications

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nonlinear model predictive control of a pusher-type slab reheating furnace based on a continuous-time state-space model.

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ELEKTROTECHNIK UND INFORMATIONSTECHNIK 2012年第1期129卷 3-10页

作者： Steinboeck, A. Kugi, A. TU Wien Inst Automatisierungs & Regelungstech Vienna Austria TU Wien Komplexe Dynam Syst Vienna Austria

A nonlinear model predictive controller is developed as part of a cascade temperature control system for a continuous furnace that reheats steel slabs. Using a continuous-time state-space model based on first principles, a constrained dynamic optimization problem is formulated. It is converted into an unconstrained optimization problem by means of an input transformation and additional penalty terms in the cost functional. With the help of the quasi-Newton method, the optimization problem is recurrently solved for finite time horizons. The measured furnace temperatures as well as the slab temperatures, which are estimated by an extended Kalman filter, are used as feedback. Results from the application of the control system in a slab furnace of a rolling mill demonstrate the high accuracy of the slab reheating process and significant energy savings.

关键词： nonlinear model predictive control switched nonlinear system constrained dynamic optimization slab reheating furnace

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nonlinear model predictive control of an uninhabited surface vehicle

Nonlinear model predictive control of an uninhabited surface...

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9th Asian control Conference (ASCC)

作者： Sutton, Robert Sharma, Sanjay K. Univ Plymouth Sch Marine Sci & Engn Marine & Ind Dynam Anal Grp Plymouth PL4 8AA Devon England

ISBN: (纸本)9781467357692;9781467357678

This paper presents a novel nonlinear autopilot design based on a nonlinear model predictive control (NMPC) approach that is compared against a linear quadratic Gaussian control scheme. The autopilot systems are used to control the nonlinear yaw dynamics of an uninhabited surface vehicle named Springer. The yaw dynamics of the vehicle being modelled using a multi-layer perceptron neural network. Simulation results are presented and the performances of the autopilots are evaluated and compared using standard system performance criteria and indices. The autopilot based on the NMPC method is deemed the more apt of the two types examined for Springer in terms of control activity expenditure, power consumption and mission duration length.

关键词： nonlinear model predictive control linear quadratic Gaussian control genetic algoriths neural networks uninhabited surface vehicle

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nonlinear model predictive control for Tracking of Underactuated Vessels under Input Constraints 9

Nonlinear Model Predictive Control for Tracking of Underactu...

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UKSim-AMSS 9th IEEE European modelling Symposium on Computer modelling and Simulation (EMS)

作者： Abdelaal, Mohamed Franzle, Martin Hahn, Axel Carl von Ossietzky Univ Oldenburg Dept Comp Sci Oldenburg Germany

ISBN: (纸本)9781509002061

In this paper, a nonlinear model predictive control (NMPC) is presented for position and velocity tracking of underactuated surface vessel with input constraints. A three degree-of-freedom (3-DOF) dynamic model is used with only two control variables: namely, surge force and yaw moment. Without frame transformation, a nonlinear, but convex, optimization problem is formulated to minimize the deviation of the vessel states from a time varying reference generated over a finite horizon by a virtual vessel with the same dynamics. A real-time C-code is generated, using ACADO toolkit and qpOASES solver, with multiple shooting technique for discretization and Gauss-Newton iteration algorithm, which is computationally efficient, thus enabling real-time implementation of proposed technique. MATLAB simulations is used to assess the validity of the proposed technique.

关键词： nonlinear model predictive control Underactuated vessel Path following Tracking

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nonlinear model predictive control based on multi-scale models: is it worth the complexity? 9

Nonlinear Model Predictive Control based on multi-scale mode...

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9th IFAC Conference on Foundations of Systems Biology in Engineering (FOSBE)

作者： Bhonsale, Satyajeet Mores, Wannes Van Impe, Jan Katholieke Univ Leuven Dept Chem Engn BioTeC Technol Campus Ghent B-9000 Ghent Belgium

ISBN: (纸本)9781713867890

nonlinear model predictive control (NMPC) has been used to control a variety of bioprocesses. As NMPC requires full state information, a nonlinear moving horizon estimator (NMHE) is used to reconstruct the states from the available measurements. In context of bioprocesses, both NMPC and NMHE typically use macroscopic mass balance type models. These models ignore all intracellular information that is available via, e.g., Genome-Scale Metabolic networks. However, the use of these models adds significantly to the computational complexity of the controller. In this paper, we aim to assess whether incorporating metabolic networks in an NMPC framework leads to improvement in the tracking performance of a continuous bioreactor. To avoid complex bilevel optimisation problems, our NMPC scheme utilises the dynamic Metabolic Flux Analysis framework. In this framework, the intracellular fluxes are estimated from the measurements of extracellular metabolites. This is achieved by an NMHE scheme. The tracking performance of this metabolic network-based NMPC-NMHE is compared to a simple macroscopic model based NMPC in which all the states, i.e., the extracellular metabolites are assumed to be measured. In our case study, the tracking performance of the metabolic network-based NMPC is marginally better than the performance of the macroscopic model based NMPC. The added advantage of metabolic network based NMPC comes from the insight they offer from the estimated fluxes. Moreover, the performance and the insights obtained depend on the complexity of the metabolic network used. 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： nonlinear model predictive control nonlinear moving horizon estimation metabolic flux analysis

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nonlinear model predictive control of Hammerstein and Wiener models using Genetic Algorithms

Nonlinear Model Predictive Control of Hammerstein and Wiener...

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IEEE International Conference on control Applications

作者： Al-Duwaish, H Naeem, W King Fahd Univ Petr & Minerals Dept Elect Engn Dhahran 31261 Saudi Arabia

ISBN: (纸本)0780367332

model predictive control or MPC can provide robust control for processes with variable gain and dynamics, multivariable interaction, measured loads and unmeasured disturbances. In this paper a novel approach for the implementation of nonlinear MPC is proposed using Genetic Algorithms (GAs). The proposed method formulates the MPC as an optimization problem and genetic algorithms is used in the optimization process. Application to two types of nonlinear models namely Hammerstein and Wiener models is studied and the simulation results are shown for the case of two chemical processes to demonstrate the performance of the proposed scheme.

关键词： nonlinear model predictive control Genetic Algorithm (GA) bockwurst Variable gain Robust control Simulation results

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nonlinear model predictive control Approximation: Applications to Truck-Trailer control System 24

Nonlinear Model Predictive Control Approximation: Applicatio...

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24th International Conference on control, Automation and Systems

作者： Park, Suyong Nguyen, Duc Giap Jin, Yongsik Park, Jinrak Kim, Dohee Eo, Jeong Soo Han, Kyoungseok Hanyang Univ Dept Automot Engn Seoul 04763 South Korea Kyungpook Natl Univ Dept Mech Engn Daegu 41566 South Korea Elect & Telecommun Res Inst ETRI Robot IT Convergence Res Sect Daegu 42994 South Korea Hyundai Motor Co Dept Electrified Syst Control Res Lab Hwaseong 18280 South Korea

ISBN: (纸本)9798331517939;9788993215380

In this work, we demonstrate the effectiveness of nonlinear model predictive control (NMPC) approximation based on deep neural network (DNN). MPC has been widely adopted in autonomous driving control problems to handle multiple objectives and constraints. We first design the implicit NMPC for the forward and backward motions of a truck-trailer (TT) system, which follows the reference path while maintaining safety between the head truck (HT) and the trailer (TR). However, the computational load in implicit MPC makes it a challenge for real-time implementations. To alleviate the computational burden in implicit NMPC online, an NMPC approximation approach based on DNN is adopted in this study to achieve a parametric function approximation. We conduct a comparative study on the proposed approach and a baseline controller for control performance analysis, and the computational load is evaluated on a hardware-in-the-loop (HIL) experimental system.

关键词： Deep neural network truck-trailer system nonlinear model predictive control hardware-in-the-loop systems control policy approximation

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nonlinear model predictive control of fed-batch cultures of micro-organisms exhibiting overflow metabolism

Nonlinear model predictive control of fed-batch cultures of ...

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IEEE International Conference on control Applications Part of 2010 IEEE Multi-Conference on Systems and control

作者： Santos, Lino O. Dewasme, Laurent Hantson, Anne-Lise Wouwer, Alain Vande Department of Chemical Engineering Faculty of Sciences and Technology University of Coimbra Portugal

ISBN: (纸本)9781424453634

Overflow metabolism characterizes cells strains that are likely to produce inhibiting metabolites resulting from an excess of substrate feeding and a saturated respiratory capacity. The critical substrate level separating the two different metabolic pathways is generally not well defined. This occurs for instance in Escherichia coli cultures with aerobic acetate formation. This paper considers the control problem of a lab-scale E. coli biomass production. A preliminary study is presented to access the application of a multivariable nonlinear model predictive control approach to maximize the biomass production. This strategy is tested by simulation and its performance to control the bioreactor system is evaluated with various objective cost functions, and in the presence of noise and dead-time on the acetate concentration measurement.

关键词： E. coli biomass production Escherichia coli cultures aerobic acetate formation bioreactor system bioreactors fed-batch cultures microorganisms microorganisms multivariable nonlinear model nonlinear control systems nonlinear model predictive control overflow metabolism predictive control

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