Control Synthesis of Discrete-Time T–S Fuzzy Systems: Reducing the Conservatism Whilst Alleviating the Computational Burden

The augmented multi-indexed matrix approach acts as a powerful tool in reducing the conservatism of control synthesis of discrete-time Takagi–Sugeno fuzzy systems. However, its computational burden is sometimes too heavy as a tradeoff. Nowadays, reducing the conservatism whilst alleviating the computational burden becomes an ideal but very challenging problem. This paper is toward finding an efficient way to achieve one of satisfactory answers. Different from the augmented multi-indexed matrix approach in the literature, we aim to design a more efficient slack variable approach under a general framework of homogenous matrix polynomials. Thanks to the introduction of a new extended representation for homogeneous matrix polynomials, related matrices with the same coefficient are collected together into one sole set and thus those redundant terms of the augmented multi-indexed matrix approach can be removed, i.e., the computational burden can be alleviated in this paper. More importantly, due to the fact that more useful information is involved into control design, the conservatism of the proposed approach as well is less than the counterpart of the augmented multi-indexed matrix approach. Finally, numerical experiments are given to show the effectiveness of the proposed approach.

[1]  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.

[2]  Ligang Wu,et al.  Event-Triggered Fault Detection of Nonlinear Networked Systems , 2017, IEEE Transactions on Cybernetics.

[3]  Ricardo C. L. F. Oliveira,et al.  Selective $\hbox{\scr H}_2$ and $\hbox{\scr H}_\infty$ Stabilization of Takagi–Sugeno Fuzzy Systems , 2011, IEEE Transactions on Fuzzy Systems.

[4]  Abdesselem Boulkroune,et al.  Adaptive Fuzzy Backstepping Tracking Control for Strict-Feedback Systems With Input Delay , 2017, IEEE Transactions on Fuzzy Systems.

[5]  Fuwen Yang,et al.  $H_{\infty }$ Fault Detection for Networked Mechanical Spring-Mass Systems With Incomplete Information , 2016, IEEE Transactions on Industrial Electronics.

[6]  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.

[7]  Huaguang Zhang,et al.  Stability Analysis of T–S Fuzzy Control Systems by Using Set Theory , 2015, IEEE Transactions on Fuzzy Systems.

[8]  Jin Bae Park,et al.  Approaches to extended non-quadratic stability and stabilization conditions for discrete-time Takagi-Sugeno fuzzy systems , 2011, Autom..

[9]  Ligang Wu,et al.  Observer-based adaptive sliding mode control for nonlinear Markovian jump systems , 2016, Autom..

[10]  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.

[11]  Dong Hwan Lee,et al.  Relaxed LMI Conditions for Local Stability and Local Stabilization of Continuous-Time Takagi–Sugeno Fuzzy Systems , 2014, IEEE Transactions on Cybernetics.

[12]  Peng Yang,et al.  Further studies on LMI-based relaxed stabilization conditions for nonlinear systems in Takagi-Sugeno's form , 2006, Autom..

[13]  Shaocheng Tong,et al.  Adaptive Fuzzy Output-Feedback Control of Pure-Feedback Uncertain Nonlinear Systems With Unknown Dead Zone , 2014, IEEE Transactions on Fuzzy Systems.

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

[15]  Ligang Wu,et al.  Event-Triggered Control for Nonlinear Systems Under Unreliable Communication Links , 2017, IEEE Transactions on Fuzzy Systems.

[16]  Wei He,et al.  Adaptive Neural Network Control of an Uncertain Robot With Full-State Constraints , 2016, IEEE Transactions on Cybernetics.

[17]  Young Hoon Joo,et al.  LMI conditions for local stability and stabilization of continuous-time T-S fuzzy systems , 2015, International Journal of Control, Automation and Systems.

[18]  Shaocheng Tong,et al.  Adaptive Fuzzy Identification and Control for a Class of Nonlinear Pure-Feedback MIMO Systems With Unknown Dead Zones , 2015, IEEE Transactions on Fuzzy Systems.

[19]  Shaocheng Tong,et al.  Optimal Control-Based Adaptive NN Design for a Class of Nonlinear Discrete-Time Block-Triangular Systems , 2016, IEEE Transactions on Cybernetics.

[20]  Guang-Hong Yang,et al.  Robust Mixed $l_{1}/H_{\infty}$ Filtering for Affine Fuzzy Systems With Measurement Errors , 2014, IEEE Transactions on Cybernetics.

[21]  Ligang Wu,et al.  Model Approximation for Fuzzy Switched Systems With Stochastic Perturbation , 2015, IEEE Transactions on Fuzzy Systems.

[22]  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.

[23]  Thierry-Marie Guerra,et al.  Controller Design for TS Models Using Delayed Nonquadratic Lyapunov Functions , 2015, IEEE Transactions on Cybernetics.

[24]  Shaocheng Tong,et al.  Adaptive Fuzzy Output Feedback Dynamic Surface Control of Interconnected Nonlinear Pure-Feedback Systems , 2015, IEEE Transactions on Cybernetics.

[25]  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.

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

[27]  Tao Zou,et al.  Asymptotically necessary and sufficient stability with respect to nonquadratic Lyapunov function for Takagi-Sugeno model , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

[28]  Young Hoon Joo,et al.  On the Generalized Local Stability and Local Stabilization Conditions for Discrete-Time Takagi–Sugeno Fuzzy Systems , 2014, IEEE Transactions on Fuzzy Systems.

[29]  Jin Bae Park,et al.  Further improvement of periodic control approach for relaxed stabilization condition of discrete-time Takagi-Sugeno fuzzy systems , 2011, Fuzzy Sets Syst..

[30]  Zhigang Zeng,et al.  Aperiodic Sampled-Data Sliding-Mode Control of Fuzzy Systems With Communication Delays Via the Event-Triggered Method , 2016, IEEE Transactions on Fuzzy Systems.

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

[32]  Jin Bae Park,et al.  Improvement on Nonquadratic Stabilization of Discrete-Time Takagi–Sugeno Fuzzy Systems: Multiple-Parameterization Approach , 2010, IEEE Transactions on Fuzzy Systems.

[33]  Xiaodong Liu,et al.  Local analysis of continuous-time Takagi-Sugeno fuzzy system with disturbances bounded by magnitude or energy: A Lagrange multiplier method , 2013, Inf. Sci..

[34]  Thierry-Marie Guerra,et al.  Control Law Proposition for the Stabilization of Discrete Takagi–Sugeno Models , 2009, IEEE Transactions on Fuzzy Systems.

[35]  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..

[36]  Baocang Ding,et al.  Stabilization of Takagi–Sugeno Model via Nonparallel Distributed Compensation Law , 2008, IEEE Transactions on Fuzzy Systems.

[37]  Wei He,et al.  Adaptive Neural Network Control of a Marine Vessel With Constraints Using the Asymmetric Barrier Lyapunov Function. , 2017, IEEE transactions on cybernetics.

[38]  Yongduan Song,et al.  An Optimal Divisioning Technique to Stabilization Synthesis of T–S Fuzzy Delayed Systems , 2017, IEEE Transactions on Cybernetics.

[39]  Shaocheng Tong,et al.  Adaptive NN Tracking Control of Uncertain Nonlinear Discrete-Time Systems With Nonaffine Dead-Zone Input , 2015, IEEE Transactions on Cybernetics.

[40]  Shaocheng Tong,et al.  Fuzzy Approximation-Based Adaptive Backstepping Optimal Control for a Class of Nonlinear Discrete-Time Systems With Dead-Zone , 2016, IEEE Transactions on Fuzzy Systems.