Neurofuzzy model based predictive control for thermal batch processes

Escaño, Juan Manuel; Bordons, Carlos; Vilas, Carlos; García, Míriam R.; Alonso, Antonio A.

doi:10.1016/j.jprocont.2009.07.016

Cited by 29 publications

(5 citation statements)

References 25 publications

(30 reference statements)

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“…ANFIS is a multi-inputs and single-output model. Based on experience and knowledge, a neural network was employed to optimize the premise and consequence parameters to adaptively establish a fuzzy inference system [15,[26][27][28][29].…”

Section: Design Of Anfimentioning

confidence: 99%

Path Tracking Control of an Omni-Directional Service Robot Based on Model Predictive Control of Adaptive Neural-Fuzzy Inference System

Pang

Luo

et al. 2021

Applied Sciences

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In this paper, model predictive control (MPC) based on an adaptive neural-fuzzy inference system (ANFIS) is proposed to realize control of an omni-directional service robot in path tracking. The weight of the cost function in a traditional MPC needs to be manually adjusted, and it is difficult to adjust to a satisfactory value. In order to improve the performance and the control accuracy of MPC, a fuzzy system trained by ANFIS is used to adaptively adjust the weight of MPC’s cost function to reduce the error in the process of path tracking. The different simulation experiments are conducted to verify the performance of the proposed algorithm. The experimental results show that the distance error of MPC based on ANFIS is reduced by more than 50% under different paths compared with a traditional MPC, and the angle error is reduced by more than 70%. Meanwhile, the stability is increased by around 60%. The results show the feasibility and superiority of MPC based on ANFIS.

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Section: Design Of Anfimentioning

confidence: 99%

Path Tracking Control of an Omni-Directional Service Robot Based on Model Predictive Control of Adaptive Neural-Fuzzy Inference System

Pang

Luo

et al. 2021

Applied Sciences

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“…The searching for sterilization processes of canned food models attracted many scientists and researchers of the late twentieth century [1][2][3][4][5][6][7][8][9][10][11][12]. One of the most famous researchers in this field is Giulio R. Banga, who proposed methods for improving food processing using modern optimization methods [13].…”

Section: Brdem-2019mentioning

confidence: 99%

Development of an Autoclave Thermal Processes Model for the Simulator of Canned Food Sterilization Process

Kaychenov

Vlasov

Maslov

et al. 2020

KLS

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The article describes an autoclave thermal processes model, which is used for the simulator of canned food sterilization process. The simulator is based on a simulation model that adequately describes the reaction of the autoclave to the actions of the control system and the operator of the sterilization unit. The model’s parameters were obtained by means of experimental data processing. The computer program ”autoclave Model” for simulating sterilization process in the steam and water environment is described. The examples of the canned food’s manual control sterilization modeling are shown. The results of numerical mathematical modeling of canned food sterilization processes in the autoclave showed a high degree of the implemented process models quality of approximation. The calculation schemes done as a result of the mathematical models creation were used to develop a hardwaresoftware complex of the sterilization process simulator. The increase of training level on carrying out process of canned goods sterilization will be provided as a result of designing the simulator of sterilization process in educational process. Consequently reducing defects in production and improving the quality of canned products are expected.

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“…Moreover, fuzzy models permit explicit solutions of the optimization (without restrictions) (Marusak and Tatjewski 2009;Escaño et al 2009;Lu and Tsai 2007), with a low computational cost and getting better performance than linear MPC schemes. It should be remarked that a NMPC procedure based on Fuzzy models could be implemented on small hardware platforms like PLCs.…”

Section: Complexity Reduction For Fuzzy Predictive Controlmentioning

confidence: 99%

Fuzzy Modeling and Control: Theory and Applications

Mata¹,

Marichal²,

Jimnez³

2014

Atlantis Computational Intelligence Systems

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Aims and scope of the seriesThe series 'Atlantis Computational Intelligence Systems' aims at covering state-of-the-art research and development in all fields where computational intelligence is investigated and applied. The series seeks to publish monographs and edited volumes on foundations and new developments in the field of computational intelligence, including fundamental and applied research as well as work describing new, emerging technologies originating from computational intelligence research. Applied CI research may range from CI applications in the industry to research projects in the life sciences, including research in biology, physics, chemistry and the neurosciences.All books in this series are co-published with Springer.For more information on this series and our other book series, please visit our website at: www.atlantis-press.com/publications/books. This book is published under the Creative Commons Attribution-Non-commercial license, meaning that copying, distribution, transmitting and adapting the book is permitted, provided that this is done for non-commercial purposes and that the book is attributed. This book, or any parts thereof, may not be reproduced for commercial purposes in any form or by any means, electronic or mechanical, including photocopying, recording or any information storage and retrieval system known or to be invented, without prior permission from the Publisher.Printed on acid-free paper ForewordAlmost half a century has passed since the publication of the seminal paper by Prof. Lotfi A. Zadeh. Fifty years that have witnessed an impressive growth of fuzzy systems from both the theoretical and applied points of view. Impressive growth and at the same time following an unforeseen way, since as Prof. Zadeh has said many times, his expectations when writing his first paper on fuzzy sets in 1965 were that fuzzy sets would find its main applications in the realm of humanistic systems (economics, linguistics, psychology, etc.). Now the reality is that most of the success of fuzzy systems research (at least in its initial decades) was related to its application to mechanistic instead of humanistic systems. This book concentrates on modeling and control of mechanistic systems, as can be seen when reading the Table of Contents, first considering important theoretical aspects and then showing its application to quite interesting real-world problems.Any book on fuzzy modeling and control is somehow a tribute to those who paved the way for the topic. In this case, we need to consider three key persons and three related milestones in the history of fuzzy sets. First, the already mentioned Prof. Zadeh and his 1973 paper ''Outline of a New Approach to the Analysis of Complex Systems and Decision Processes,'' IEEE Transactions on Systems, Man, and Cybernetics 3: 28-44 (1973), the paper where concepts as linguistic variable and fuzzy if-then rules took form, and at the same time, the paper that triggered the second hint in the road to build up the theory of fuzzy modeling and control. Ab...

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Neurofuzzy model based predictive control for thermal batch processes

Cited by 29 publications

References 25 publications

Path Tracking Control of an Omni-Directional Service Robot Based on Model Predictive Control of Adaptive Neural-Fuzzy Inference System

Path Tracking Control of an Omni-Directional Service Robot Based on Model Predictive Control of Adaptive Neural-Fuzzy Inference System

Development of an Autoclave Thermal Processes Model for the Simulator of Canned Food Sterilization Process

Fuzzy Modeling and Control: Theory and Applications

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