Direct adaptive control based on LS-SVM inverse model for nonlinear systems

This article proposes an inverse model control scheme for unknown nonlinear systems. The proposed scheme structures the inverse dynamics of the controlled system offline. This is done using a least squares support vector machine (LS-SVM) network that based on the Fuzzy C-Means (FCM) clustering technique. Then the proposed network is updated online to determine the appropriate control action. A linear feedback compensator is added to ensure the closed loop stability. Simulation results of a continuous stirred tank reactor (CSTR) demonstrate the efficiency of the proposed approach.

[1]  Aun-Neow Poo,et al.  Support vector regression model predictive control on a HVAC plant , 2007 .

[2]  Sung Jin Yoo,et al.  Stable Predictive Control of Chaotic Systems Using Self-Recurrent Wavelet Neural Network , 2005 .

[3]  Lamiaa M. Elshenawy,et al.  Recursive Fault Detection and Isolation Approaches of Time-Varying Processes , 2012 .

[4]  Weifeng Liu,et al.  Extended Kernel Recursive Least Squares Algorithm , 2009, IEEE Transactions on Signal Processing.

[5]  B. Bequette.  Nonlinear control of chemical processes: a review , 1991 .

[6]  Serdar Iplikci,et al.  Support vector machines based neuro-fuzzy control of nonlinear systems , 2010, Neurocomputing.

[7]  Tarek A. Mahmoud,et al.  Multi resolution wavelet least squares support vector machine network for nonlinear system modeling , 2010, 2010 15th International Conference on Methods and Models in Automation and Robotics.

[8]  J. Suykens.  Nonlinear modelling and support vector machines , 2001, IMTC 2001. Proceedings of the 18th IEEE Instrumentation and Measurement Technology Conference. Rediscovering Measurement in the Age of Informatics (Cat. No.01CH 37188).

[9]  Han-Xiong Li,et al.  A novel neural internal model control for multi-input multi-output nonlinear discrete-time processes , 2009 .

[10]  Serdar Iplikci,et al.  Support vector machines‐based generalized predictive control , 2006 .

[11]  Miguel Lázaro-Gredilla,et al.  Estimation of the forgetting factor in kernel recursive least squares , 2012, 2012 IEEE International Workshop on Machine Learning for Signal Processing.

[12]  H. Su,et al.  Generalized Predictive Control with Online Least Squares Support Vector Machines , 2007, ACTA AUTOMATICA SINICA.

[13]  Youxian Sun,et al.  Online SVM regression algorithm-based adaptive inverse control , 2007, Neurocomputing.

[14]  Selami Beyhan,et al.  Stable modeling based control methods using a new RBF network. , 2010, ISA transactions.

[15]  Benjamin Schrauwen,et al.  Feedback Control by Online Learning an Inverse Model , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[16]  Jin Hyun Park,et al.  Fault detection and identification of nonlinear processes based on kernel PCA , 2005 .

[17]  Seongkyu Yoon,et al.  Fault diagnosis with multivariate statistical models part I: using steady state fault signatures , 2001 .

[18]  Richard D. Braatz,et al.  Fault Detection and Diagnosis in Industrial Systems , 2001 .

[19]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[20]  Lamiaa M. Elshenawy,et al.  Efficient Recursive Principal Component Analysis Algorithms for Process Monitoring , 2010 .

[21]  Serdar Iplikci,et al.  A support vector machine based control application to the experimental three-tank system. , 2010, ISA transactions.

[22]  Yaonan Wang,et al.  SVM Approximate-based Internal Model Control Strategy , 2009 .

[23]  C. Yoo,et al.  Nonlinear process monitoring using kernel principal component analysis , 2004 .

[24]  Miguel Lázaro-Gredilla,et al.  Kernel Recursive Least-Squares Tracker for Time-Varying Regression , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[25]  C. W. Laufer,et al.  Kernel Recursive Least Squares for the CMAC Neural Network , 2013 .

[26]  Tarek A. Mahmoud,et al.  Adaptive control scheme based on the least squares support vector machine network , 2011, Int. J. Appl. Math. Comput. Sci..

[27]  Johan A. K. Suykens,et al.  Optimal control by least squares support vector machines , 2001, Neural Networks.

[28]  Wei Wu,et al.  Adaptive feedforward and feedback control of non-linear time-varying uncertain systems , 1999 .

[29]  Madan Gopal,et al.  Reinforcement Learning with Kernel Recursive Least-Squares Support Vector Machine , 2012 .

[30]  Mohd Azlan Hussain,et al.  Implementation of Neural-Network-Based Inverse-Model Control Strategies on an Exothermic Reactor , 2001 .

[31]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[32]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[33]  Mian M. Awais,et al.  Application of internal model control methods to industrial combustion , 2005, Appl. Soft Comput..

[34]  D. C. Psichogios,et al.  Direct and indirect model based control using artificial neural networks , 1991 .

[35]  Kwang Y. Lee,et al.  Diagonal recurrent neural networks for dynamic systems control , 1995, IEEE Trans. Neural Networks.