Observer-based output feedback control design of discrete-time Takagi-Sugeno fuzzy systems: A multi-samples method

In this paper, the problem of observer-based output feedback control design of discrete-time Takagi-Sugeno (T-S) fuzzy systems is investigated based on a multi-samples method. In order to ameliorate the existing results in the literature, a fuzzy observer-based output feedback controller, which relies on multi-samples normalized fuzzy weighting functions, is developed for relaxing the design conditions of observer-based output feedback control. Moreover, thanks to the special form of the developed observer-based output feedback controller, it is shown that all the designed parameters can be solved via a set of strict linear matrix inequalities, i.e., the drawback induced by the usual two-step method is resolved by means of a compromising way. Finally, the benefit of the proposed result is validated by using some numerical experiments.

[1]  Shaocheng Tong,et al.  Homogenous Polynomially Parameter-Dependent $H_{ \infty}$ Filter Designs of Discrete-Time Fuzzy Systems , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[2]  Ligang Wu,et al.  Reliable Filtering With Strict Dissipativity for T-S Fuzzy Time-Delay Systems , 2014, IEEE Transactions on Cybernetics.

[3]  Baocang Ding,et al.  Homogeneous Polynomially Nonquadratic Stabilization of Discrete-Time Takagi–Sugeno Systems via Nonparallel Distributed Compensation Law , 2010, IEEE Transactions on Fuzzy Systems.

[4]  Ji-Chang Lo,et al.  Existence of Similarity Transformation Converting BMIs to LMIs , 2007, IEEE Transactions on Fuzzy Systems.

[5]  Ligang Wu,et al.  Induced l2 filtering of fuzzy stochastic systems with time-varying delays , 2013, IEEE Transactions on Cybernetics.

[6]  Baocang Ding,et al.  Poly‐quadratic stability of discrete‐time nonlinear systems in Takagi‐Sugeno's form , 2009 .

[7]  Guang-Hong Yang,et al.  Adaptive Reliable $H_{\infty}$ Filtering Against Sensor Failures , 2007, IEEE Transactions on Signal Processing.

[8]  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.

[9]  Yuanqing Xia,et al.  New LMI Approach to Fuzzy $H_{\infty}$ Filter Designs , 2009, IEEE Transactions on Circuits and Systems II: Express Briefs.

[10]  Xiao-Zheng Jin,et al.  Adaptive output regulation and circuit realization for a class of attenuated coupled networks , 2015, Commun. Nonlinear Sci. Numer. Simul..

[11]  Xiangpeng Xie,et al.  Observer Design of Discrete-Time T–S Fuzzy Systems Via Multi-Instant Homogenous Matrix Polynomials , 2014, IEEE Transactions on Fuzzy Systems.

[12]  Guanghong Yang,et al.  Static output feedback H ∞ control of a class of nonlinear discrete-time systems , 2009 .

[13]  Yongduan Song,et al.  A novel approach to output feedback control of fuzzy stochastic systems , 2014, Autom..

[14]  Peng Shi,et al.  A Novel Observer-Based Output Feedback Controller Design for Discrete-Time Fuzzy Systems , 2015, IEEE Transactions on Fuzzy Systems.

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

[16]  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.

[17]  Yongduan Song,et al.  A Novel Control Design on Discrete-Time Takagi–Sugeno Fuzzy Systems With Time-Varying Delays , 2013, IEEE Transactions on Fuzzy Systems.

[18]  Xiangpeng Xie,et al.  Relaxed observer design of discrete-time T-S fuzzy systems via a novel multi-instant fuzzy observer , 2014, Signal Process..

[19]  Dong Yue,et al.  Control Synthesis of Discrete-Time T–S Fuzzy Systems via a Multi-Instant Homogenous Polynomial Approach , 2016, IEEE Transactions on Cybernetics.

[20]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

[21]  Minrui Fei,et al.  An improved result on the stability of uncertain T-S fuzzy systems with interval time-varying delay , 2013, Fuzzy Sets Syst..

[22]  Dong Yue,et al.  Relaxed Stability and Stabilization Conditions of Networked Fuzzy Control Systems Subject to Asynchronous Grades of Membership , 2014, IEEE Transactions on Fuzzy Systems.

[23]  Guang-Hong Yang,et al.  Nonfragile $H_{\infty}$ Filtering of Continuous-Time Fuzzy Systems , 2011, IEEE Transactions on Signal Processing.

[24]  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.

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

[26]  Guang-Hong Yang,et al.  Insensitive reliable H∞ filtering against sensor failures , 2013, Inf. Sci..

[27]  Xiao‐Heng Chang A Descriptor Representation Approach , 2012 .

[28]  Guang-Hong Yang,et al.  Static output feedback Hinfinity control of a class of nonlinear discrete-time systems , 2009, Fuzzy Sets Syst..

[29]  J. Lauber,et al.  An Efficient Lyapunov Function for Discrete T–S Models: Observer Design , 2012, IEEE Transactions on Fuzzy Systems.

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

[31]  Xiaodong Liu,et al.  Stability analysis for discrete‐time fuzzy system by utilizing homogeneous polynomial matrix function , 2009 .