A new design of output feedback sliding mode controller for T‐S fuzzy‐affine systems

Ji, Wenqiang; Qiu, Jianbin; Lam, Hak‐Keung

doi:10.1002/asjc.2323

Cited by 8 publications

(5 citation statements)

References 45 publications

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“…As shown in Figure 2, sliding mode control 19 is a control method which moves the system state from an asymptotic mode to a sliding mode through a switch function. its tracking performance is good, but at the cost of high chattering.…”

Section: Design Of Fuzzy Controllermentioning

confidence: 99%

A novel trajectory tracking control approach for uncertain 6-DOF manipulators based on fuzzy sliding mode of radial basis function neural network

Liu

Yang

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part I:

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Due to the influence of uncertain external disturbance and model error, the trajectory tracking of manipulators has some problems such as inaccuracy, low speed and severe chattering. To solve the problems mentioned, a sliding mode control method combined fuzzy controller and Radial Basis Function Neural Network (RBFNN) was proposed. The RBFNN was used to approximate the equivalent system model of manipulators, and the weights of their hidden layers were updated adaptively online. The robust item with the form of sliding mode had been set to compensate the uncertain external disturbances or the approximation errors of the RBFNN. A fuzzy controller was used to adjust the switching gain online adaptively and to solve the chattering of the sliding mode control. The stability and finite time convergence of the closed-loop system had been analyzed by using Lyapunov method. Finally, simulation experiments about trajectory tracking were implemented by MATLAB/SIMULINK, which shows that the proposed method can improve the position-tracking accuracy by more than 40%, the speed-tracking accuracy by more than 70%, and the cumulative-error accuracy by more than 40% compared with the comparison method. Moreover, there is no chattering. The proposed method has been proved to be efficient.

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Section: Design Of Fuzzy Controllermentioning

confidence: 99%

A novel trajectory tracking control approach for uncertain 6-DOF manipulators based on fuzzy sliding mode of radial basis function neural network

Liu

Yang

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part I:

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“…Sliding mode control (SMC) is a well-known robust controller and has been utilized for T-S fuzzy model widely [7]. SMC-based T-S fuzzy controller has been presented [8].…”

Section: Introductionmentioning

confidence: 99%

Sliding mode learning control for T‐S fuzzy system and an application to hypersonic flight vehicle

Guo

et al. 2022

Asian Journal of Control

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Sliding mode‐based learning control is presented for T‐S fuzzy system. A T‐S fuzzy model with both uncertainties and unmodeled dynamics is proposed firstly, in which the information of uncertainties and unmodeled dynamics are assumed to be unknown. Then, according to a given reference model, state‐tracking error system is built. Respecting facts, the input matrices of the built T‐S fuzzy model are different from each other. An extended state observer is built for estimating the unknown uncertainties and unmodeled dynamics, and a corresponding sliding surface is proposed. A learning controller is then presented for the closed loop system. Moreover, a numerical simulation result on hypersonic flight vehicles is considered to testify the controller's availability.

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“…Compared with state feedback controls problem which need all state variables of dynamical systems to be available, output feedback controls problem is more realistic and hence preferable for many control systems [3,33,35]. Some interesting results on finite-time output feedback dissipative control for some kind of integer-order systems have been reported in recent years [33,36,37].…”

Section: Introductionmentioning

confidence: 99%

Output feedback finite‐time dissipative control for uncertain nonlinear fractional‐order systems

Hong

Thuận

2021

Asian Journal of Control

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This paper solves the robustly finite‐time dissipativity control problems for uncertain nonlinear fractional‐order systems (FOS). Firstly, by using some basic mathematical transformations associated with linear matrix inequality (LMI) techniques, a novel condition for the existence of output feedback controllers is established to ensure the closed‐loop FOS is finite‐time stabilizable. Then, based on the proposed stabilization criterion combined with some well‐known properties of fractional calculus, the finite‐time dissipativity control problem for nonlinear FOS subject to uncertainties is studied for the first time. Two numerical examples are provided to illustrate the effectiveness of the proposed results.

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A new design of output feedback sliding mode controller for T‐S fuzzy‐affine systems

Cited by 8 publications

References 45 publications

A novel trajectory tracking control approach for uncertain 6-DOF manipulators based on fuzzy sliding mode of radial basis function neural network

A novel trajectory tracking control approach for uncertain 6-DOF manipulators based on fuzzy sliding mode of radial basis function neural network

Sliding mode learning control for T‐S fuzzy system and an application to hypersonic flight vehicle

Output feedback finite‐time dissipative control for uncertain nonlinear fractional‐order systems

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