Multi-Model One Step Ahead Control for Nonlinear System

Two multi-model based one-step ahead predictive controller algorithms are proposed for nonlinear systems. The proposed design employ Constrained Kalman filter algorithm to interpolate the submodels/controllers and determine which model/controller pair to be used according to the operating region of the system. Thus, it maintain the performance of the controllers over a wide range of operating regions. The algorithms is independent of the submodels, hence can be applied regardless of the submodels development. Two benchmark system examples are studied to demonstrate the effectiveness of the proposed predictive controller design on tracking problems.

[1]  Sergey A. Kolyubin,et al.  Control of Nonlinear Systems Using Multiple Model Black-Box Identification , 2013, NOLCOS.

[2]  Thiago V. Costa,et al.  Experimental assessment and design of multiple model predictive control based on local model networks for industrial processes , 2015, Evol. Syst..

[3]  Wei Qian,et al.  Robust adaptive control for single input/single output discrete systems via multi-model switching , 2014, J. Syst. Control. Eng..

[4]  Ye Xudong.  Nonlinear adaptive control by switching linear controllers , 2012 .

[5]  Hannu T. Toivonen,et al.  Internal model control of nonlinear systems described by velocity-based linearizations , 2003 .

[6]  I. Mohammadzaman,et al.  Predictive Control of an Electromagnetic Suspension System via Modified Locally Linear Model Tree with Merging Ability , 2006, 2006 IEEE Conference on Cybernetics and Intelligent Systems.

[7]  Mohamed Benrejeb,et al.  A Multimodel Approach of Complex Systems Identification and Control Using Neural and Fuzzy Clustering Algorithms , 2010, 2010 Ninth International Conference on Machine Learning and Applications.

[8]  Jingjing Du,et al.  Multilinear model decomposition of MIMO nonlinear systems and its implication for multilinear model-based control , 2013 .

[9]  Wen Tan,et al.  Operating point selection in multimodel controller design , 2004, Proceedings of the 2004 American Control Conference.

[10]  Zhenkuang Xue,et al.  Multi-Model Modelling and Predictive Control Based on Local Model Networks , 2006, Control. Intell. Syst..

[11]  Jürgen Kurths,et al.  Modeling and identification of nonlinear systems , 2004 .

[12]  Jiong Shen,et al.  Offset-free fuzzy model predictive control of a boiler-turbine system based on genetic algorithm , 2012, Simul. Model. Pract. Theory.

[13]  Yu Lei,et al.  Multi-model switching control for SISO discrete systems , 2013, Proceedings of the 32nd Chinese Control Conference.

[14]  Petr Chalupa,et al.  Modelling and Predictive control of a Nonlinear System Using Local Model Network , 2011 .

[15]  K. Youcef-Toumi,et al.  On robust adaptive switched control , 2005, Proceedings of the 2005, American Control Conference, 2005..

[16]  Naresh N. Nandola,et al.  A multiple model approach for predictive control of nonlinear hybrid systems , 2008 .

[17]  K. Srinivasan,et al.  Design of multi-model-based controller design and implementation using microcontroller for blood glucose regulation of Type 1 diabetic system , 2011 .

[18]  Kumpati S. Narendra,et al.  Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.

[19]  Wen Tan,et al.  Multimodel analysis and controller design for nonlinear processes , 2004, Comput. Chem. Eng..

[20]  Rosario Toscano,et al.  Robust synthesis of a PID controller by uncertain multimodel approach , 2007, Inf. Sci..

[21]  Jingjing Du,et al.  Multimodel Control of Nonlinear Systems: An Integrated Design Procedure Based on Gap Metric and H∞ Loop Shaping , 2012 .

[22]  Didier Maquin,et al.  NON-LINEAR SYSTEM IDENTIFICATION USING UNCOUPLED STATE MULTIPLE-MODEL APPROACH , 2006 .

[23]  Patrick Lyonnet,et al.  Robustness analysis and synthesis of a multi-PID controller based on an uncertain multimodel representation , 2006, Comput. Chem. Eng..

[24]  Petr Chalupa,et al.  MIMO model predictive control with local linear models , 2011 .

[25]  L. Bin,et al.  Structural Robustness and Multi-Model Control in Gap Metric , 2013 .

[26]  Tong Heng Lee,et al.  Data-Based Identification and Control of Nonlinear Systems via Piecewise Affine Approximation , 2011, IEEE Transactions on Neural Networks.

[27]  Ahmet Palazoglu,et al.  Multimodel Scheduling Control of Nonlinear Systems Using Gap Metric , 2004 .

[28]  Eugene Coyle,et al.  Model Predictive Control of CSTR Based on Local Model Networks , 2002 .