Robust Stabilization of T–S Fuzzy Stochastic Descriptor Systems via Integral Sliding Modes

Li, Jinghao; Zhang, Qingling; Yan, Xing‐Gang; Spurgeon, Sarah K.

doi:10.1109/tcyb.2017.2749244

Cited by 70 publications

(62 citation statements)

References 43 publications

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“…Paper 25 successfully proposed the concept of Multilayer Fuzzy model for identifying uncertain nonlinear MISO system. Furthermore, in order to compensate the uncertainties and guarantee the asymptotic stability, there are many different adaptive fuzzy control methods based on the classic advanced control algorithm such as sliding mode control (SMC) [26][27][28][29][30][31][32] , H 2/ ∞ technique [10][11][12][13] , linearization feedback control [33][34][35][36] , back-stepping technique [37][38][39][40] . However, these above-mentioned techniques require knowing in advance the characteristics of nonlinear uncertain SISO or MIMO system.…”

Section: Introductionmentioning

confidence: 99%

Adaptive multilayer T-S fuzzy controller for nonlinear SISO system optimized by differential evolution algorithm

Kien

Ánh

Sơn

2020

Sci. Tech. Dev. J.- Eng. Tech.

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In this paper, the authors propose a novel adaptive multilayer T-S fuzzy controller (AMTFC) with an optimized soft computing algorithm for a class of robust control uncertain nonlinear SISO systems. First, a new multilayer T-S fuzzy was created by combined multiple simple T-S fuzzy models with a sum function in the output. The multi-layer fuzzy model used in nonlinear identification has many advantages over conventional fuzzy models, but it cannot be created by the writer's experience or the trial and error method. It can only be created using an optimization algorithm. Then the parameters of the multilayer fuzzy model are optimized by the differential evolution DE algorithm is used to offline identify the nonlinear inverse system with uncertain parameters. The trained model was validated by a different dataset from the training dataset to guarantee the convergence of the training algorithm. Second, for robustly and adaptive purposes, the authors have proposed an additional adaptive fuzzy model based on Lyapunov stability theory combined with the optimized multilayer fuzzy. The adaptive fuzzy based on the sliding mode surface is designed to guarantee that the closed-loop system is asymptotically stable has been proved base on a Lyapunov stability theory. Furthermore, simulation tests are performed in the Matlab/Simulink environment that controlling a water level of a coupled tank with uncertain parameters are given to illustrate the effectiveness of the proposed control scheme. The proposed control algorithm is implemented in simulation with many different control parameters, and it is also compared with the conventional adaptive control algorithm and inverse controller. The simulation results also show the superior of the proposed controller than an adaptive fuzzy control or inverse controller when using the least mean square error standard.

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Section: Introductionmentioning

confidence: 99%

Adaptive multilayer T-S fuzzy controller for nonlinear SISO system optimized by differential evolution algorithm

Kien

Ánh

Sơn

2020

Sci. Tech. Dev. J.- Eng. Tech.

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“…For improvement of in the control area, an integral control design has been developed for improving and achieving performance in controlling various control field [42]- [51]. Based on the LMI approach, the asymptotic stability has been ensured by using the integral sliding-mode control, such that this method can remove a restrictive fuzzy assumption of the integral sliding-mode control [48], [49]. Considering the next application, the doubly fed induction generator (DFIG) wind energy system has been guaranteed to have asymptotic stability under the nonlinearity, uncertain parameters, and the disturbance by employing the robust H ∞ fuzzy integral controller [50].Moreover, an H ∞ fuzzy integral controller has been considered for the case of the nonlinear descriptor system [51]; however, an issue regarding the existence of uncertain parameters remains to be considered in the literature.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy-Modeled Prescribed Performance Integral Controller Design for Nonlinear Descriptor System With Uncertainties

Assawinchaichote

Shi

2020

IEEE Access

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A suitable solution for a nonlinear descriptor system with uncertainties is considered in this paper by designing a fuzzy-modeled prescribed performance integral controller. The system with a parasitic parameter ε is known as the descriptor system, and this parasitic parameter is used for specifying a fast mode of such a system. Based on a linear matrix inequality (LMI) approach, the interaction of fast and slow dynamic modes which causes ill-conditioned LMI result has normally occurred in the nonlinear descriptor system with uncertainties. Therefore, with the design of the fuzzy-modeled prescribed performance integral controller, such a system with parametric uncertainties is represented by a Takagi-Sugeno fuzzy model, and the H ∞ fuzzy state-feedback controller is designed to achieve an adequate condition for overcoming the effects of the parasitic parameter, the uncertainties, and the exogenous input disturbance. Moreover, the integral controller is added to increase the performances of stability. In summary, the design process of the proposed controller and the numerical example serve to illustrate various performance results of the proposed controller. INDEX TERMS H ∞ fuzzy controller, integral controller, linear matrix inequality (LMI), Takagi-Sugeno fuzzy model, uncertain nonlinear descriptor system.

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“…Descriptor systems have been extensively employed to power systems, economic systems, and circuits systems. Many outstanding research results have been published, such as descriptor time‐delay systems, descriptor uncertain systems, and descriptor stochastic systems . However, there are barely reports involving event‐triggered transmission for networked descriptor systems despite their respectively noteworthy advantages.…”

Section: Introductionmentioning

confidence: 99%

“…Many outstanding research results have been published, such as descriptor time-delay systems, descriptor uncertain systems, and descriptor stochastic systems. [31][32][33][34] However, there are barely reports involving event-triggered transmission for networked descriptor systems despite their respectively noteworthy advantages. In the work of Wang et al, 35 an event-triggered H ∞ control for networked descriptor system is considered.…”

Section: Introductionmentioning

confidence: 99%

Network‐based integral sliding mode control for descriptor systems with event‐triggered sampling scheme

Wang

Zhang

Ren

et al. 2019

Intl J Robust & Nonlinear

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Summary This paper investigates event‐triggered sliding mode control for descriptor systems subject to networked‐induced delay. For handling disturbance and saving data transmission in networks for descriptor systems, sliding model control and event‐triggered control are combined, whereas how to combine them to design a stable controller is challenging due to the complexity of descriptor systems. Firstly, a new discrete event‐triggered scheme is introduced for deciding whether sampled data is sent out or not for networked‐based descriptor systems. Secondly, integral‐type switching surface and event‐triggered scheme are integrated resulting in a novel time‐delay system. The existence condition of integral‐type switching surface and the stability condition of the sliding mode dynamic are presented. An event‐triggered sliding mode controller is derived for driving the closed‐loop system trajectories to the practical switching surface in finite time. Finally, two examples are employed to show the effectiveness and advantages of the proposed method.

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Robust Stabilization of T–S Fuzzy Stochastic Descriptor Systems via Integral Sliding Modes

Cited by 70 publications

References 43 publications

Adaptive multilayer T-S fuzzy controller for nonlinear SISO system optimized by differential evolution algorithm

Adaptive multilayer T-S fuzzy controller for nonlinear SISO system optimized by differential evolution algorithm

Fuzzy-Modeled Prescribed Performance Integral Controller Design for Nonlinear Descriptor System With Uncertainties

Network‐based integral sliding mode control for descriptor systems with event‐triggered sampling scheme

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