Adaptive control over non­linear objects using the robust neural network FCMAC

The paper explores issues related to the application of artificial neural networks (ANN) when solving the problems on identification and control of nonlinear dynamic systems. We have investigated characteristics of the network, which is a result of the application of the apparatus of fuzzy logic in a classical СМАС neural network, which is titled FCMAC ‒ Fuzzy Cerebral Model Arithmetic Computer. We studied influence of the form of receptive fields of associative neurons on the accuracy of identification and control; various information hashing algorithms that make it possible to reduce the amount of memory required for the implementation of a network; robust learning algorithms are proposed allowing the use of a network in systems with strong perturbations. It is shown that the FСМАС network, when selecting appropriate membership functions, can be applied in order to synthesize indirect control systems with and without a reference model; it is more efficient to use it in control systems with the reference model. This sharply reduces the quantity of training pairs and simplifies the coding due to the narrower range of the applied values of input signals. The results obtained are confirmed by simulation modeling of the processes of identification of and control over nonlinear dynamical systems

[1]  L. Wang,et al.  Fuzzy systems are universal approximators , 1992, [1992 Proceedings] IEEE International Conference on Fuzzy Systems.

[2]  Sarangapani Jagannathan.  Discrete-time CMAC NN control of feedback linearizable nonlinear systems under a persistence of excitation , 1999, IEEE Trans. Neural Networks.

[3]  Hongjun Lu,et al.  Fuzzy system and CMAC network with B-spline membership/basis functions are smooth approximators , 2003, Soft Comput..

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

[5]  Chih-Min Lin,et al.  An Efficient Interval Type-2 Fuzzy CMAC for Chaos Time-Series Prediction and Synchronization , 2014, IEEE Transactions on Cybernetics.

[6]  Yuanwei Jing,et al.  Research and Application of the Pellet Grate Thickness Control System Base on Improved CMAC Neural Network Algorithm , 2016 .

[7]  Jing Zhao,et al.  A General Fuzzy Cerebellar Model Neural Network Multidimensional Classifier Using Intuitionistic Fuzzy Sets for Medical Identification , 2016, Comput. Intell. Neurosci..

[8]  Donald E. Knuth,et al.  Sorting and Searching , 1973 .

[9]  Ya-Fu Peng,et al.  Applications of Prediction and Identification Using Adaptive DCMAC Neural Networks , 2011 .

[10]  J. L. Schiano,et al.  Hash-coding in CMAC neural networks , 1996, Proceedings of International Conference on Neural Networks (ICNN'96).

[11]  Frank L. Lewis,et al.  CMAC neural networks for control of nonlinear dynamical systems: Structure, stability and passivity , 1997, Autom..

[12]  Mohammad Teshnehlab,et al.  Fuzzy CMAC structures , 2009, 2009 IEEE International Conference on Fuzzy Systems.

[13]  Richard D. Braatz,et al.  On the "Identification and control of dynamical systems using neural networks" , 1997, IEEE Trans. Neural Networks.

[14]  D.A. Handelman,et al.  Theory and development of higher-order CMAC neural networks , 1992, IEEE Control Systems.

[15]  Chih-Min Lin,et al.  Fuzzy Brain Emotional Cerebellar Model Articulation Control System Design for Multi-Input Multi-Output Nonlinear , 2015 .

[16]  Lin Chun-Shin,et al.  CMAC with General Basis Functions. , 1996, Neural networks : the official journal of the International Neural Network Society.

[17]  James S. Albus,et al.  Data Storage in the Cerebellar Model Articulation Controller (CMAC) , 1975 .

[18]  Chih-Min Lin,et al.  Adaptive CMAC-based supervisory control for uncertain nonlinear systems , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[19]  Frank L. Lewis,et al.  Discrete-time CMAC neural networks for control applications , 1995, Proceedings of 1995 34th IEEE Conference on Decision and Control.

[20]  Derek A. Linkens,et al.  FCMAC: A fuzzified cerebellar model articulation controller with self-organizing capacity , 1994, Autom..

[21]  Hui Cheng.  The Fuzzy CMAC Based on RLS Algorithm , 2013 .

[22]  Jing Zhao,et al.  Medical sample classifier design using fuzzy cerebellar model neural networks , 2016 .

[23]  Ching-Hung Lee,et al.  A Novel Wavelet-based-CMAC Neural Network Controller for Nonlinear Systems , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[24]  D. Marr.  A theory of cerebellar cortex , 1969, The Journal of physiology.

[25]  Chi-Cheng Jou,et al.  A fuzzy cerebellar model articulation controller , 1992, [1992 Proceedings] IEEE International Conference on Fuzzy Systems.