Fuzzy Predictive Fault-Tolerant Control for Time-Delay Nonlinear Systems with Partial Actuator Failures

A fuzzy predictive fault-tolerant control (FPFTC) scheme is proposed for a wide class of discrete-time nonlinear systems with uncertainties, interval time-varying delays, and partial actuator failures as well as unknown disturbances, in which the main opinions focus on the relevant theory of FPFTC based on Takagi-Sugeno (T-S) fuzzy model description of these systems. The T-S fuzzy model represents the discrete-time nonlinear system in the form of the discrete uncertain time-varying delay state space, which is firstly constructed by a set of local linear models and the nonlinear membership functions. The novel improved state space model can be further obtained by extending the output tracking error to the constructed model. Then the fuzzy predictive fault-tolerant control law based on this extended model is designed, which can increase more control degrees of freedom. Utilizing Lyapunov-Krasovskill theory, less conservative delay-range-dependent stable conditions in terms of linear matrix inequality (LMI) constraints are given to ensure the asymptotically robust stability of closed-loop system. Meanwhile, the optimized cost function and H-infinity performance index are introduced to the stable conditions to guarantee the robust performance and antidisturbance capability. The simulation results on the temperature control of a strong nonlinear continuous stirred tank reactor (CSTR) show that the proposed control scheme is feasible and effective.

[1]  Donghua Zhou,et al.  Robust delay dependent iterative learning fault-tolerant control for batch processes with state delay and actuator failures , 2012 .

[2]  Fuqiang Liu,et al.  Receding horizon consensus of general linear multi-agent systems with input constraints: An inverse optimality approach , 2016, Autom..

[3]  Renquan Lu,et al.  New Minmax Linear Quadratic Fault-Tolerant Tracking Control for Batch Processes , 2016, IEEE Transactions on Automatic Control.

[4]  J. Duane Morningred,et al.  An Adaptive Nonlinear Predictive Controller , 1990, 1990 American Control Conference.

[5]  Marcin Witczak,et al.  Fault Diagnosis and Fault-Tolerant Control Strategies for Non-Linear Systems , 2014 .

[6]  Yingjian Lin A delay-dependent approach to robust H control for stochastic systemwith interval time delay , 2016 .

[7]  A. El Hajjaji,et al.  Adaptive fault tolerant control design for Takagi–Sugeno fuzzy systems with interval time-varying delay , 2014 .

[8]  Bing Chen,et al.  A Delay-Dependent Approach to Robust H∞ Control for Uncertain Stochastic Systems with State and Input Delays , 2009, Circuits Syst. Signal Process..

[9]  Fuli Wang,et al.  Robust dynamic output feedback fault-tolerant control for Takagi-Sugeno fuzzy systems with interval time-varying delay via improved delay partitioning approach , 2016 .

[10]  Honghai Liu,et al.  Parameter-dependent robust stability for uncertain Markovian jump systems with time delay , 2011, J. Frankl. Inst..

[11]  曹江涛,et al.  Incremental multivariable predictive functional control and its application in a gas fractionation unit , 2015 .

[12]  Furong Gao,et al.  Robust Iterative Learning Fault-Tolerant Control for Multiphase Batch Processes with Uncertainties , 2017 .

[13]  Yong-Yan Cao,et al.  Analysis and synthesis of nonlinear time-delay systems via fuzzy control approach , 2000, IEEE Trans. Fuzzy Syst..

[14]  Ahmed El Hajjaji,et al.  Finite frequency H∞ filter design for T-S fuzzy systems: New approach , 2018, Signal Process..

[15]  E. Yaz Linear Matrix Inequalities In System And Control Theory , 1998, Proceedings of the IEEE.

[16]  Maciej Ławryńczuk,et al.  Nonlinear predictive control of a boiler-turbine unit: A state-space approach with successive on-line model linearisation and quadratic optimisation. , 2017, ISA transactions.

[17]  Furong Gao,et al.  State Space Model Predictive Control for Advanced Process Operation: A Review of Recent Development, New Results, and Insight , 2017 .

[18]  Arturo F. Locatelli,et al.  Fault-Tolerant Pole-Placement in Double-Integrator Networks , 2012, IEEE Transactions on Automatic Control.

[19]  Michel Dambrine,et al.  Simultaneous Design of Parallel Distributed Output Feedback and Anti-windup Compensators for Constrained Takagi-Sugeno Fuzzy Systems , 2016 .

[20]  Maciej Lawrynczuk Computationally Efficient Model Predictive Control Algorithms: A Neural Network Approach , 2014 .

[21]  Furong Gao,et al.  Iterative learning fault-tolerant control for batch processes based on T-S fuzzy model , 2006 .

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

[23]  Fernando de Oliveira Souza,et al.  On delay-dependent stability conditions for Takagi-Sugeno fuzzy systems , 2014, J. Frankl. Inst..

[24]  Tingwen Huang,et al.  An Adaptive Takagi–Sugeno Fuzzy Model-Based Predictive Controller for Piezoelectric Actuators , 2017, IEEE Transactions on Industrial Electronics.

[25]  Furong Gao,et al.  An improved approach for H∞ design of linear quadratic tracking control for chemical processes with partial actuator failure , 2017 .

[26]  Furong Gao,et al.  Design of fuzzy iterative learning fault‐tolerant control for batch processes with time‐varying delays , 2018, Optimal control applications & methods.

[27]  Jili Tao,et al.  Improved State Space Model Predictive Control Design for Linear Systems with Partial Actuator Failure , 2014 .

[28]  Guang-Hong Yang,et al.  Fault Tolerant Controller Design for T–S Fuzzy Systems With Time-Varying Delay and Actuator Faults: A K-Step Fault-Estimation Approach , 2014, IEEE Transactions on Fuzzy Systems.

[29]  Baocang Ding,et al.  Dynamic Output Feedback-Predictive Control of a Takagi–Sugeno Model With Bounded Disturbance , 2017, IEEE Transactions on Fuzzy Systems.

[30]  J. Qiu,et al.  Robust stabilisation for a class of discrete-time systems with time-varying delays via delta operators , 2008 .

[31]  Cao Jiangtao,et al.  Nonlinear Adaptive Predictive Functional Control Based on the Takagi–Sugeno Model for Average Cracking Outlet Temperature of the Ethylene Cracking Furnace , 2015 .

[32]  S. Tong,et al.  Prescribed performance fuzzy adaptive fault-tolerant control of non-linear systems with actuator faults , 2014 .

[33]  Damiano Rotondo,et al.  Fault tolerant control of a proton exchange membrane fuel cell using Takagi–Sugeno virtual actuators , 2016 .

[34]  Michio Sugeno,et al.  Fuzzy Control Systems: Past, Present and Future , 2019, IEEE Computational Intelligence Magazine.

[35]  Bahram Shafai,et al.  Robust stability and stabilization of uncertain delay systems , 2012, 2012 American Control Conference (ACC).

[36]  Yao Lina,et al.  Fault Diagnosis and Sliding Mode Fault Tolerant Control for Non‐Gaussian Stochastic Distribution Control Systems Using T‐s Fuzzy Model , 2017 .

[37]  Huiping Li,et al.  Triggering and Control Codesign in Self-Triggered Model Predictive Control of Constrained Systems: With Guaranteed Performance , 2018, IEEE Transactions on Automatic Control.

[38]  W. Marsden I and J , 2012 .

[39]  Stéphane Ploix,et al.  Fault diagnosis and fault tolerant control , 2007 .

[40]  Furong Gao,et al.  Robust two-dimensional iterative learning control for batch processes with state delay and time-varying uncertainties , 2010 .

[41]  Fuli Wang,et al.  Robust fast adaptive fault estimation and tolerant control for T-S fuzzy systems with interval time-varying delay , 2017, Int. J. Syst. Sci..

[42]  Andreas Kroll,et al.  On Iterative Closed-Loop Identification Using Affine Takagi–Sugeno Models and Controllers , 2017, Int. J. Fuzzy Syst..

[43]  Hamid Reza Karimi,et al.  Robust Stability and Stabilization of Uncertain T–S Fuzzy Systems With Time-Varying Delay: An Input–Output Approach , 2013, IEEE Transactions on Fuzzy Systems.

[44]  Mohamed Chaabane,et al.  Fault-Tolerant Control for T–S Fuzzy Descriptor Systems with Sensor Faults: An LMI Approach , 2017, Int. J. Fuzzy Syst..

[45]  Limin Wang,et al.  Robust iterative learning control for multi-phase batch processes: an average dwell-time method with 2D convergence indexes , 2018, Int. J. Syst. Sci..

[46]  Yue Wang,et al.  Robust constrained model predictive fault-tolerant control for industrial processes with partial actuator failures and interval time-varying delays , 2019, Journal of Process Control.

[47]  R. Stephenson A and V , 1962, The British journal of ophthalmology.

[48]  Neil Genzlinger A. and Q , 2006 .

[49]  Mariagrazia Dotoli,et al.  Advanced control in factory automation: a survey , 2017, Int. J. Prod. Res..

[50]  C. Lien,et al.  Stability criteria for uncertain neutral systems with interval time-varying delays , 2008 .

[51]  Ahmad Taher Azar,et al.  A Novel Actuator Fault-tolerant Control Strategy of DFIG-based Wind Turbines Using Takagi-Sugeno Multiple Models , 2018 .

[52]  Eugênio B. Castelan,et al.  Fuzzy dynamic output feedback control through nonlinear Takagi-Sugeno models , 2015, Fuzzy Sets Syst..

[53]  Furong Gao,et al.  Delay-Range-Dependent-Based Hybrid Iterative Learning Fault-Tolerant Guaranteed Cost Control for Multiphase Batch Processes , 2018 .

[54]  S. Joe Qin,et al.  A survey of industrial model predictive control technology , 2003 .

[55]  Maciej Ławryńczuk,et al.  Computationally Efficient Model Predictive Control Algorithms , 2014 .

[56]  Lei Guo,et al.  Robust fault-tolerant control for flexible spacecraft against partial actuator failures , 2014 .