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LSTM and GRU Neural Networks as Models of Dynamical Processes Used in Predictive Control: A Comparison of Models Developed for Two Chemical Reactors

Sensors (Basel, Switzerland), 2021-08, Vol.21 (16), p.5625

2021

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Input convex neural networks in nonlinear predictive control: A multi-model approach

Neurocomputing (Amsterdam), 2022-11, Vol.513, p.273-293

Ławryńczuk, Maciej

2022

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Nonlinear predictive control of a boiler-turbine unit: A state-space approach with successive on-line model linearisation and quadratic optimisation

ISA transactions, 2017-03, Vol.67, p.476-495

Ławryńczuk, Maciej

2017

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Modelling and predictive control of a neutralisation reactor using sparse support vector machine Wiener models

Neurocomputing (Amsterdam), 2016-09, Vol.205, p.311-328

Lawrynczuk, Maciej

2016

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Advanced predictive control for GRU and LSTM networks

Information sciences, 2022-11, Vol.616, p.229-254

2022

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Outliers in Control Engineering: Fractional Calculus Perspective

1, 2022

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Nonlinear predictive control of dynamic systems represented by Wiener–Hammerstein models

Nonlinear dynamics, 2016-10, Vol.86 (2), p.1193-1214

Lawrynczuk, Maciej

2016

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Constrained computationally efficient nonlinear predictive control of Solid Oxide Fuel Cell: Tuning, feasibility and performance

ISA transactions, 2020-04, Vol.99, p.270-289

Ławryńczuk, Maciej

2020

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Fast Real-Time Model Predictive Control for a Ball-on-Plate Process

Sensors (Basel, Switzerland), 2021-06, Vol.21 (12), p.3959

2021

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Practical nonlinear predictive control algorithms for neural Wiener models

Journal of process control, 2013-06, Vol.23 (5), p.696-714

Ławryńczuk, Maciej

2013

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Algorithms with state estimation in linear and nonlinear model predictive control

Computers & chemical engineering, 2020-12, Vol.143, p.107065, Article 107065

2020

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Identification of Wiener models for dynamic and steady‐state performance with application to solid oxide fuel cell

Asian journal of control, 2019-07, Vol.21 (4), p.1836-1846

Ławryńczuk, Maciej

2019

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Nonlinear Predictive Control Using Wiener Models: Computationally Efficient Approaches for Polynomial and Neural Structures

Ławryńczuk, Maciej

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Special Issue “Model Predictive Control: Algorithms and Applications”: Foreword by the Guest Editor

Algorithms, 2022-11, Vol.15 (12), p.452

Ławryńczuk, Maciej

2022

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Long Short-Term Memory Neural Networks for Modeling Dynamical Processes and Predictive Control: A Hybrid Physics-Informed Approach

Sensors (Basel, Switzerland), 2023-11, Vol.23 (21), p.8898

2023

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Optimization of control strategy for a low fuel consumption vehicle engine

Information sciences, 2019-08, Vol.493, p.192-216

2019

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Offset-free state-space nonlinear predictive control for Wiener systems

Information sciences, 2020-02, Vol.511, p.127-151

2020

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Fast Nonlinear Predictive Control Using Classical and Parallel Wiener Models: A Comparison for a Neutralization Reactor Process

Sensors (Basel, Switzerland), 2023-11, Vol.23 (23), p.9539

2023

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Make a difference, open the door: The energy-efficient multi-layer thermal comfort control system based on a graph airflow model with doors and windows

Information sciences, 2021-11, Vol.579, p.553-573

2021

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Nonlinear Predictive Control Using Wiener Models: Computationally Efficient Approaches for Polynomial and Neural Structures

Lawrynczuk, Maciej

1st Edition 2022, 2021

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Fast Model Predictive Control of PEM Fuel Cell System Using the L1 Norm

Energies (Basel), 2022-07, Vol.15 (14), p.5157

2022

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Nonlinear predictive control for Hammerstein–Wiener systems

ISA transactions, 2015-03, Vol.55, p.49-62

Lawrynczuk, Maciej

2015

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Computationally Efficient Nonlinear Model Predictive Control Using the L1 Cost-Function

Sensors (Basel, Switzerland), 2021-08, Vol.21 (17), p.5835

2021

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Fast Analytical Model Predictive Controllers and Their Implementation for STM32 ARM Microcontroller

IEEE transactions on industrial informatics, 2019-08, Vol.15 (8), p.4580-4590

2019

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Beyond the quadratic norm: Computationally efficient constrained nonlinear MPC using a custom cost function

ISA transactions, 2023-03, Vol.134, p.336-356

2023

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LSTM and GRU Neural Networks as Models of Dynamical Processes Used in Predictive Control: A Comparison of Models Developed for Two Chemical Reactors

Input convex neural networks in nonlinear predictive control: A multi-model approach

Nonlinear predictive control of a boiler-turbine unit: A state-space approach with successive on-line model linearisation and quadratic optimisation

Modelling and predictive control of a neutralisation reactor using sparse support vector machine Wiener models

Advanced predictive control for GRU and LSTM networks

Outliers in Control Engineering: Fractional Calculus Perspective

Nonlinear predictive control of dynamic systems represented by Wiener–Hammerstein models

Constrained computationally efficient nonlinear predictive control of Solid Oxide Fuel Cell: Tuning, feasibility and performance

Fast Real-Time Model Predictive Control for a Ball-on-Plate Process

Practical nonlinear predictive control algorithms for neural Wiener models

Algorithms with state estimation in linear and nonlinear model predictive control

Identification of Wiener models for dynamic and steady‐state performance with application to solid oxide fuel cell

Nonlinear Predictive Control Using Wiener Models: Computationally Efficient Approaches for Polynomial and Neural Structures

Special Issue “Model Predictive Control: Algorithms and Applications”: Foreword by the Guest Editor

Long Short-Term Memory Neural Networks for Modeling Dynamical Processes and Predictive Control: A Hybrid Physics-Informed Approach

Optimization of control strategy for a low fuel consumption vehicle engine

Offset-free state-space nonlinear predictive control for Wiener systems

Fast Nonlinear Predictive Control Using Classical and Parallel Wiener Models: A Comparison for a Neutralization Reactor Process

Make a difference, open the door: The energy-efficient multi-layer thermal comfort control system based on a graph airflow model with doors and windows

Nonlinear Predictive Control Using Wiener Models: Computationally Efficient Approaches for Polynomial and Neural Structures

Fast Model Predictive Control of PEM Fuel Cell System Using the L1 Norm

Nonlinear predictive control for Hammerstein–Wiener systems

Computationally Efficient Nonlinear Model Predictive Control Using the L1 Cost-Function

Fast Analytical Model Predictive Controllers and Their Implementation for STM32 ARM Microcontroller

Beyond the quadratic norm: Computationally efficient constrained nonlinear MPC using a custom cost function

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