A TOPSIS multi-criteria decision method-based intelligent recurrent wavelet CMAC control system design for MIMO uncertain nonlinear systems

The main goal of this paper is to design a more effective control algorithm for multiple-input multiple-output uncertain nonlinear systems. Major advantages of the proposed method include: (1) A dynamic deletion threshold developed to consider whether to retain or to delete the hypercubes automatically so as to achieve more efficient network structure; (2) automatically parameter adaptation to achieve favorable control performance; (3) combine various techniques such as sliding-mode control, adaptive control, cerebellar model articulation controller and TOPSIS multi-criteria decision method for getting the advantages of these techniques. Based on the advantages of above techniques, a new intelligent recurrent wavelet cerebellar model articulation control system is designed. The gradient descent method is utilized to online adjust the parameters of the controller, and a Lyapunov function is employed to guarantee the system stability. The effectiveness of the proposed control scheme is validated through the applications for an inverted double pendulum system and an unmanned aerial vehicle motion control.

[1]  Dieu Tien Bui,et al.  A novel hybrid artificial intelligent approach based on neural fuzzy inference model and particle swarm optimization for horizontal displacement modeling of hydropower dam , 2018, Neural Computing and Applications.

[2]  Chih-Min Lin,et al.  Self-evolving function-link interval type-2 fuzzy neural network for nonlinear system identification and control , 2017, Neurocomputing.

[3]  Marco Laumanns,et al.  PISA: A Platform and Programming Language Independent Interface for Search Algorithms , 2003, EMO.

[4]  Chih-Min Lin,et al.  Adaptive CMAC neural control of chaotic systems with a PI-type learning algorithm , 2009, Expert Syst. Appl..

[5]  Faa-Jeng Lin,et al.  Intelligent Sliding-Mode Position Control Using Recurrent Wavelet Fuzzy Neural Network for Electrical Power Steering System , 2017, Int. J. Fuzzy Syst..

[6]  James S. Albus,et al.  New Approach to Manipulator Control: The Cerebellar Model Articulation Controller (CMAC)1 , 1975 .

[7]  Chih-Min Lin,et al.  PSO-Self-Organizing Interval Type-2 Fuzzy Neural Network for Antilock Braking Systems , 2017, Int. J. Fuzzy Syst..

[8]  Jiayi Cao,et al.  Reinforcement learning-based real-time power management for hybrid energy storage system in the plug-in hybrid electric vehicle , 2018 .

[9]  Luke S. Zettlemoyer,et al.  Learning to Parse Natural Language Commands to a Robot Control System , 2012, ISER.

[10]  Chih-Hui Chiu,et al.  The Design and Implementation of a Wheeled Inverted Pendulum Using an Adaptive Output Recurrent Cerebellar Model Articulation Controller , 2010, IEEE Transactions on Industrial Electronics.

[11]  Zhongjiu Zheng,et al.  Global Asymptotic Model-Free Trajectory-Independent Tracking Control of an Uncertain Marine Vehicle: An Adaptive Universe-Based Fuzzy Control Approach , 2018, IEEE Transactions on Fuzzy Systems.

[12]  Mojtaba Ahmadieh Khanesar,et al.  Optimal parameters of an ELM-based interval type 2 fuzzy logic system: a hybrid learning algorithm , 2018, Neural Computing and Applications.

[13]  Linlin Liu,et al.  Sitcom-star-based clothing retrieval for video advertising: a deep learning framework , 2018, Neural Computing and Applications.

[14]  Núria Agell,et al.  Decision making under uncertainty using a qualitative TOPSIS method for selecting sustainable energy alternatives , 2016, International Journal of Environmental Science and Technology.

[15]  Durbadal Mandal,et al.  Optimal sizing of CMOS analog circuits using gravitational search algorithm with particle swarm optimization , 2015, International Journal of Machine Learning and Cybernetics.

[16]  Ligang Wu,et al.  Observer-based adaptive sliding mode control for nonlinear Markovian jump systems , 2016, Autom..

[17]  Zhiping Lu,et al.  Improving prediction performance of GPS satellite clock bias based on wavelet neural network , 2017, GPS Solutions.

[18]  Mohammed A. Omar,et al.  Eco-material selection using fuzzy TOPSIS method , 2016 .

[19]  R. Venkata Rao,et al.  Decision Making in Manufacturing Environment Using Graph Theory and Fuzzy Multiple Attribute Decision Making Methods , 2013 .

[20]  MengChu Zhou,et al.  Petri net synthesis for discrete event control of manufacturing systems , 1992, The Kluwer international series in engineering and computer science.

[21]  Hongyi Li,et al.  Neural network robust tracking control with adaptive critic framework for uncertain nonlinear systems , 2018, Neural Networks.

[22]  Ricardo J. Rodríguez,et al.  An Evaluation Framework for Comparative Analysis of Generalized Stochastic Petri Net Simulation Techniques , 2020, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[23]  Chun-Fei Hsu,et al.  Perturbation wavelet neural sliding mode position control for a voice coil motor driver , 2018, Neural Computing and Applications.

[24]  Chih-Min Lin,et al.  CMAC-based adaptive backstepping synchronization of uncertain chaotic systems , 2009 .

[25]  Panos M. Pardalos,et al.  A study on decision making of cutting stock with frustum of cone bars , 2017, Oper. Res..

[26]  Jau-Woei Perng,et al.  Design of robust PI control systems based on sensitivity analysis and genetic algorithms , 2018, Neural Computing and Applications.

[27]  Chih-Min Lin,et al.  Dynamic Petri Fuzzy Cerebellar Model Articulation Controller Design for a Magnetic Levitation System and a Two-Axis Linear Piezoelectric Ceramic Motor Drive System , 2015, IEEE Transactions on Control Systems Technology.

[28]  Mignon Park,et al.  Comments on " Tracking Design of Uncertain Nonlinear SISO Systems: Adaptive Fuzzy Approach" , 1998 .

[29]  Da Lin,et al.  Dynamic fuzzy neural networks modeling and adaptive backstepping tracking control of uncertain chaotic systems , 2010, Neurocomputing.

[30]  Dongmei Pan,et al.  Comparative studies on using RSM and TOPSIS methods to optimize residential air conditioning systems , 2018 .

[31]  Chih-Min Lin,et al.  Function-Link Fuzzy Cerebellar Model Articulation Controller Design for Nonlinear Chaotic Systems Using TOPSIS Multiple Attribute Decision-Making Method , 2018, Int. J. Fuzzy Syst..

[32]  Chih-Min Lin,et al.  Intelligent control system design for UAV using a recurrent wavelet neural network , 2012, Neural Computing and Applications.

[33]  Jonathan M. Garibaldi,et al.  Modeling and control of operator functional state in a unified framework of fuzzy inference petri nets , 2017, Comput. Methods Programs Biomed..

[34]  Shun-Feng Su,et al.  A Novel Fuzzy Modeling Structure-Decomposed Fuzzy System , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[35]  C. Hwang.  Multiple Objective Decision Making - Methods and Applications: A State-of-the-Art Survey , 1979 .

[36]  Chun-Fei Hsu,et al.  Emotional Fuzzy Sliding-Mode Control for Unknown Nonlinear Systems , 2016, International Journal of Fuzzy Systems.

[37]  Chih-Min Lin,et al.  TSK Fuzzy CMAC-Based Robust Adaptive Backstepping Control for Uncertain Nonlinear Systems , 2012, IEEE Transactions on Fuzzy Systems.

[38]  Mohammad-Bagher Naghibi-Sistani,et al.  Stable indirect adaptive interval type-2 fuzzy sliding-based control and synchronization of two different chaotic systems , 2017, Appl. Soft Comput..

[39]  Chih-Min Lin,et al.  Self-Organizing CMAC Control for a Class of MIMO Uncertain Nonlinear Systems , 2009, IEEE Transactions on Neural Networks.

[40]  Yu-Yen Ou,et al.  Classifying the molecular functions of Rab GTPases in membrane trafficking using deep convolutional neural networks. , 2018, Analytical biochemistry.

[41]  Yaonan Wang,et al.  Recurrent fuzzy wavelet neural networks based on robust adaptive sliding mode control for industrial robot manipulators , 2019, Neural Computing and Applications.

[42]  Chih-Min Lin,et al.  A Functional-link-based Fuzzy Brain Emotional Learning Network for Breast Tumor Classification and Chaotic System Synchronization , 2018, Int. J. Fuzzy Syst..

[43]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[44]  Jamal Ouenniche,et al.  An out-of-sample framework for TOPSIS-based classifiers with application in bankruptcy prediction , 2017, Technological Forecasting and Social Change.

[45]  Ching-Lai Hwang,et al.  Multiple Attribute Decision Making: Methods and Applications - A State-of-the-Art Survey , 1981, Lecture Notes in Economics and Mathematical Systems.

[46]  Bor-Sen Chen,et al.  H∞ tracking design of uncertain nonlinear SISO systems: adaptive fuzzy approach , 1996, IEEE Trans. Fuzzy Syst..

[47]  ChenBor-Sen,et al.  H tracking design of uncertain nonlinear SISO systems , 1996 .

[48]  Chris J. B. Macnab.  Creating a CMAC with overlapping basis functions in order to prevent weight drift , 2017, Soft Comput..

[49]  Quanmin Zhu,et al.  Advances and Applications in Sliding Mode Control Systems , 2014, Advances and Applications in Sliding Mode Control Systems.

[50]  Harish Kumar,et al.  AuthCom: Authorship verification and compromised account detection in online social networks using AHP-TOPSIS embedded profiling based technique , 2018, Expert Syst. Appl..

[51]  Ricardo H. C. Takahashi,et al.  Multi-objective Decision in Machine Learning , 2017 .

[52]  Claude E. Shannon,et al.  The mathematical theory of communication , 1950 .

[53]  Marc Roubens,et al.  Multiple criteria decision making , 1994 .

[54]  C. J. B. Macnab.  Modifying CMAC adaptive control with weight smoothing in order to avoid overlearning and bursting , 2017, Neural Computing and Applications.