Control of a MIMO Coupled Plant Using a Neuro-Fuzzy Adaptive System Based on Boolean Relations

This document describes the implementation of a neuro-fuzzy adaptive system MIMO (Multiple Input Multiple Output), using two neuro-fuzzy MIMO systems: one for control and the other for identifying the plant. Under this approach, the controller is optimized, employing the model obtained during the identification of the plant that utilizes data generated from the controller’s operation. In this way, the plant identification and the controller optimization is performed iteratively. The application case consists of controlling a MIMO non-linear hydraulic system fed by a pump and a three-way valve. In order to observe the controller performance various experimental configurations are considered.

[1]  Sabry F. Saraya,et al.  An adaptive neuro-fuzzy sliding mode controller for MIMO systems with disturbance , 2017 .

[2]  Tsung-Chih Lin,et al.  Adaptive fuzzy sliding mode control for synchronization of uncertain fractional order chaotic systems , 2011 .

[3]  Nazmul Siddique,et al.  Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing , 2013 .

[4]  Z. Rahmani,et al.  Adaptive predictive control based on adaptive neuro-fuzzy inference system for a class of nonlinear industrial processes , 2016 .

[5]  Manoj Datta,et al.  An Adaptive Power Oscillation Damping Controller for a Hybrid AC/DC Microgrid , 2020, IEEE Access.

[6]  Hung T. Nguyen,et al.  A First Course in Fuzzy and Neural Control , 2002 .

[7]  Rogelio Lozano,et al.  Introduction to Adaptive Control , 2011 .

[8]  Ruiyun Qi,et al.  Fuzzy System Identification and Adaptive Control , 2019, Communications and Control Engineering.

[9]  Hung T. Nguyen,et al.  A First Course in Fuzzy Logic , 1996 .

[10]  Abbas Erfanian,et al.  Adaptive fuzzy terminal sliding mode control for a class of MIMO uncertain nonlinear systems , 2011, Fuzzy Sets Syst..

[11]  Xiaona Song,et al.  Adaptive neuro-fuzzy backstepping dynamic surface control for uncertain fractional-order nonlinear systems , 2019, Neurocomputing.

[12]  Helbert E. Espitia,et al.  Fuzzy controller design using concretion based on boolean relations (CBR) , 2012, 2012 12th UK Workshop on Computational Intelligence (UKCI).

[13]  Mehran Mehrandezh,et al.  Adaptive neuro-fuzzy inference system (ANFIS) – based model predictive control (MPC) for carbon dioxide reforming of methane (CDRM) in a plug flow tubular reactor for hydrogen production , 2019, Thermal Science and Engineering Progress.

[14]  Iván Machón González,et al.  Proposal of an Adaptive Neurofuzzy System to Control Flow Power in Distributed Generation Systems , 2019, Complex..

[15]  Tsung-Chih Lin,et al.  Adaptive fuzzy H∞ tracking design of SISO uncertain nonlinear fractional order time-delay systems , 2012, Nonlinear Dynamics.

[16]  Pierluigi Siano,et al.  An Optimal Home Energy Management Paradigm With an Adaptive Neuro-Fuzzy Regulation , 2020, IEEE Access.

[17]  Peter L. Balise,et al.  Boolean Algebra and Its Applications , 1995 .

[18]  Hung T. Nguyen,et al.  A first course in fuzzy logic (3. ed.) , 2005 .

[19]  Nader Jalili,et al.  An Adaptive Multi-critic Neuro-fuzzy Control Framework for Intravenous Anesthesia Administration , 2019, IFAC-PapersOnLine.

[20]  Paulo Gil,et al.  Adaptive Neuro–Fuzzy Control for Discrete-Time Nonaffine Nonlinear Systems , 2019, IEEE Transactions on Fuzzy Systems.

[21]  R. Sternberg.  Adaptive Intelligence , 2021 .

[22]  Ramin Vatankhah,et al.  Adaptive critic-based quaternion neuro-fuzzy controller design with application to chaos control , 2018, Appl. Soft Comput..

[23]  Mojtaba Ahmadieh Khanesar,et al.  Adaptive sliding-mode type-2 neuro-fuzzy control of an induction motor , 2015, Expert Syst. Appl..

[24]  Iván Machón,et al.  Design Methodology for the Implementation of Fuzzy Inference Systems Based on Boolean Relations , 2019, Electronics.

[25]  Iván Machón González,et al.  Dynamics identification and control of nonlinear MIMO coupled plant using supervised neural gas and comparison with recurrent neural controller , 2020, Neural Computing and Applications.

[26]  Ibrahim A. Hameed,et al.  A GA-Based Adaptive Neuro-Fuzzy Controller for Greenhouse Climate Control System , 2015, Alexandria Engineering Journal.

[27]  Bin Jiang,et al.  Adaptive compensation of persistent actuator failures of nonlinear systems , 2020, International Journal of Adaptive Control and Signal Processing.

[28]  Iván Machón,et al.  Compact Fuzzy Systems Based on Boolean Relations , 2021, Applied Sciences.

[29]  Erdal Kayacan,et al.  Adaptive Neuro-Fuzzy Control of a Spherical Rolling Robot Using Sliding-Mode-Control-Theory-Based Online Learning Algorithm , 2013, IEEE Transactions on Cybernetics.

[30]  Amira Y. Haikal,et al.  Adaptive neuro-fuzzy control of an induction motor , 2010 .

[31]  S. Sastry,et al.  Adaptive Control: Stability, Convergence and Robustness , 1989 .

[32]  Ligang Wu,et al.  Adaptive Fuzzy Control for Nonlinear Networked Control Systems , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[33]  Rogelio Lozano,et al.  Adaptive Control: Algorithms, Analysis and Applications , 2011 .

[34]  Abeer Alshejari,et al.  Neuro-fuzzy based intelligent approaches to nonlinear system identification and forecasting , 2018 .

[35]  D. H. Shah,et al.  Design of sliding mode control for quadruple-tank MIMO process with time delay compensation , 2019, Journal of Process Control.

[36]  George J. Klir,et al.  Fuzzy Logic and Mathematics: A Historical Perspective , 2017 .

[37]  Vlad Muresan,et al.  Wake Management in Wind Farms: An Adaptive Control Approach , 2019 .

[38]  Luciana R. Barroso,et al.  Response attenuation of cable-stayed bridge subjected to central US earthquakes using neuro-fuzzy and simple adaptive control , 2020 .

[39]  Gang Tao,et al.  Higher Order Tracking Properties of Model Reference Adaptive Control Systems , 2018, IEEE Transactions on Automatic Control.

[40]  Hartono Ibbi.  Optimization of Tsukamoto Fuzzy Inference System using Fuzzy Grid Partition , 2016 .

[41]  Lounis Adouane,et al.  Hierarchical and Adaptive Neuro-Fuzzy Control for Intelligent Energy Management in Hybrid Electric Vehicles , 2017 .

[42]  Yongkun Lu,et al.  Adaptive-Fuzzy Control Compensation Design for Direct Adaptive Fuzzy Control , 2018, IEEE Transactions on Fuzzy Systems.

[43]  Girish V. Lakhekar,et al.  Robust Diving Motion Control of an Autonomous Underwater Vehicle Using Adaptive Neuro-Fuzzy Sliding Mode Technique , 2020, IEEE Access.

[44]  Ricardo H. C. Takahashi,et al.  Online Neuro-Fuzzy Controller: Design for Robust Stability , 2020, IEEE Access.

[45]  Mykel J. Kochenderfer,et al.  Algorithms for Optimization , 2019 .

[46]  Gang Tao,et al.  A matrix decomposition based adaptive control scheme for a class of MIMO non-canonical approximation systems , 2019, Autom..

[47]  Sergio Salazar,et al.  Takagi–Sugeno Dynamic Neuro-Fuzzy Controller of Uncertain Nonlinear Systems , 2017, IEEE Transactions on Fuzzy Systems.

[48]  George A. Rovithakis,et al.  An adaptive neuro-fuzzy tracking control for multi-input nonlinear dynamic systems , 2008, Autom..

[49]  Kevin M. Passino,et al.  Biomimicry for Optimization, Control and Automation , 2004, IEEE Transactions on Automatic Control.

[50]  Srinivasan Alavandar,et al.  New hybrid adaptive neuro-fuzzy algorithms for manipulator control with uncertainties- comparative study. , 2009, ISA transactions.

[51]  Anuradha M. Annaswamy,et al.  Robust Adaptive Control , 1984, 1984 American Control Conference.

[52]  Aria Alasty,et al.  Adaptive optimal multi-critic based neuro-fuzzy control of MIMO human musculoskeletal arm model , 2016, Neurocomputing.

[53]  Jianwei Xia,et al.  Adaptive Backstepping Hybrid Fuzzy Sliding Mode Control for Uncertain Fractional-Order Nonlinear Systems Based on Finite-Time Scheme , 2020, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[54]  Doaa M. Atia,et al.  Analysis and design of greenhouse temperature control using adaptive neuro-fuzzy inference system , 2017 .

[55]  Muhammad Mujtaba Asad,et al.  Hybridized classification algorithms for data classification applications: A review , 2020, Egyptian Informatics Journal.

[56]  Mohammad Abdelrahman,et al.  Spacecraft attitude control via a combined state-dependent Riccati equation and adaptive neuro-fuzzy approach , 2013 .

[57]  Libor Pekař,et al.  Control of a Multivariable System Using Optimal Control Pairs: A Quadruple-Tank Process , 2020, IEEE Access.