Control synthesis of Roesser type discrete-time 2-D T-S fuzzy systems via a multi-instant fuzzy state-feedback control scheme

Abstract This paper is concerned with further relaxations for control synthesis of the Roesser type discrete-time 2-D T–S fuzzy systems. A novel multi-instant fuzzy state-feedback control scheme and a new multi-instant Lyapunov function, which are homogeneous polynomially parameter-dependent on both the current-time normalized fuzzy weighting functions and the past-time normalized fuzzy weighting functions along two independent directions, are developed to stabilize the underlying 2-D T–S fuzzy system with less conservatism. Because more useful information about both current-time and past-time normalized fuzzy weighting functions is involved into control synthesis, the relaxation quality of control synthesis could be improved significantly. Finally, a numerical example is provided to illustrate the effectiveness of the proposed results.

[1]  TanakaK.,et al.  An approach to fuzzy control of nonlinear systems , 1996 .

[2]  Xiangpeng Xie,et al.  Relaxed Stability Conditions for Continuous-Time T–S Fuzzy-Control Systems Via Augmented Multi-Indexed Matrix Approach , 2011, IEEE Transactions on Fuzzy Systems.

[3]  Dongsheng Du,et al.  Reliable H∞ control for Takagi–Sugeno fuzzy systems with intermittent measurements , 2012 .

[4]  Derong Liu,et al.  Non-quadratic stabilization of discrete-time 2-D T-S fuzzy systems based on new relaxed conditions , 2009, 2009 International Conference on Networking, Sensing and Control.

[5]  G. Marchesini,et al.  State-space realization theory of two-dimensional filters , 1976 .

[6]  Dong Yue,et al.  Further Studies on Control Synthesis of Discrete-Time T–S Fuzzy Systems via Useful Matrix Equalities , 2014, IEEE Transactions on Fuzzy Systems.

[7]  Vimal Singh,et al.  Stability Analysis of 2-D Discrete Systems Described by the Fornasini–Marchesini Second Model With State Saturation , 2008, IEEE Transactions on Circuits and Systems II: Express Briefs.

[8]  T. Hinamoto Stability of 2-D discrete systems described by the Fornasini-Marchesini second model , 1997 .

[9]  Eric Rogers,et al.  Output-feedback control of discrete linear repetitive processes , 1993 .

[10]  Huaguang Zhang,et al.  A fuzzy support vector machine algorithm for classification based on a novel PIM fuzzy clustering method , 2014, Neurocomputing.

[11]  Xiuxia Yin,et al.  T–S fuzzy-model-based robust stabilization for a class of nonlinear discrete-time networked control systems , 2013 .

[12]  Guang-Hong Yang,et al.  H∞ control design for fuzzy discrete-time singularly perturbed systems via slow state variables feedback: An LMI-based approach , 2009, Inf. Sci..

[13]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[14]  Thierry-Marie Guerra,et al.  LMI-based relaxed nonquadratic stabilization conditions for nonlinear systems in the Takagi-Sugeno's form , 2004, Autom..

[15]  Songlin Hu,et al.  Robust H∞ control for T–S fuzzy systems with probabilistic interval time varying delay , 2012 .

[16]  Krzysztof Galkowski,et al.  LMI based output feedback control of discrete linear repetitive processes , 2004, Proceedings of the 2004 American Control Conference.

[17]  Chun-Hsiung Fang,et al.  A new LMI-based approach to relaxed quadratic stabilization of T-S fuzzy control systems , 2006, IEEE Trans. Fuzzy Syst..

[18]  L. Xiaodong,et al.  New approaches to H∞ controller designs based on fuzzy observers for T-S fuzzy systems via LMI , 2003, Autom..

[19]  Ricardo C. L. F. Oliveira,et al.  Parameter-Dependent LMIs in Robust Analysis: Characterization of Homogeneous Polynomially Parameter-Dependent Solutions Via LMI Relaxations , 2007, IEEE Transactions on Automatic Control.

[20]  Xiangpeng Xie,et al.  Control Synthesis of Discrete-Time T–S Fuzzy Systems Based on a Novel Non-PDC Control Scheme , 2013, IEEE Transactions on Fuzzy Systems.

[21]  Huijun Gao,et al.  I filtering for 2D Markovian jump systems , 2008, Autom..

[22]  Huaguang Zhang,et al.  Novel stability criterions of a new fuzzy cellular neural networks with time-varying delays , 2009, Neurocomputing.

[23]  Dong Yue,et al.  Further Studies on Control Synthesis of Discrete-Time T-S Fuzzy Systems via Augmented Multi-Indexed Matrix Approach , 2014, IEEE Transactions on Cybernetics.

[24]  R. Roesser A discrete state-space model for linear image processing , 1975 .

[25]  Yixin Yin,et al.  Further studies on relaxed stabilization conditions for discrete-time two-dimension Takagi-Sugeno fuzzy systems , 2012, Inf. Sci..

[26]  Eric Rogers,et al.  Analysis of Linear Iterative Learning Control Schemes - A 2D Systems/Repetitive Processes Approach , 2000, Multidimens. Syst. Signal Process..

[27]  Hamid Reza Karimi,et al.  Stability and l1-gain analysis for positive 2D T-S fuzzy state-delayed systems in the second FM model , 2014, Neurocomputing.

[28]  解相朋,et al.  基于新的松弛条件的离散时间2-D T-S模糊系统的镇定 , 2010 .

[29]  Xiangpeng Xie,et al.  An efficient approach for reducing the conservatism of LMI-based stability conditions for continuous-time T-S fuzzy systems , 2015, Fuzzy Sets Syst..

[30]  Krzysztof Galkowski,et al.  PI control of discrete linear repetitive processes , 2006, Autom..

[31]  Kazuo Tanaka,et al.  An approach to fuzzy control of nonlinear systems: stability and design issues , 1996, IEEE Trans. Fuzzy Syst..