Robust Exponential $$H_{\infty }$$ H ∞ Filtering for Discrete-Time Switched Fuzzy Systems with Time-Varying Delay

Wang, Gang; Xie, Rongqiang; Zhang, Hongbin; Yu, Guanjie; Dang, Chuangyin

doi:10.1007/s00034-015-0062-0

Cited by 30 publications

(10 citation statements)

References 26 publications

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“…In this example, a single-link robot arm system is taken from [37] and [38] to further test the effectiveness of the proposed scheme. The single-link robot arm system is described by the following dynamic equation…”

Section: Examplementioning

confidence: 99%

“…Since the system mass m i , inertia J i and damping D i form a set of discrete sequences q i = (m i , J i , D i ) are changing depending on the angle η(t), so the robot arm can be viewed as switched system. In this example, we choose the same parameter as in [38], q 1 = (1, 1, 2), q 2 = (5, 5, 2), q 3 = (10, 10, 2). q i denotes the three different subsystems which means k ∈ {1, 2, 3}.…”

Section: Examplementioning

confidence: 99%

See 1 more Smart Citation

Adaptive Neural Constraint Output Control for a Class of Quantized Input Switched Nonlinear System

2019

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In this paper, an adaptive neural control issue is addressed for a class of switched unknown strict-feedback nonlinear system under constraint output, in which the input signal is quantized. The control goal is to design a quantized controller to ensure that the system's output signal follows a given reference signal, meanwhile, the system output signal meets the asymmetric constraint requirement. To this end, the radial basis function neural networks (RBFNNs) are employed to approximate the unknown nonlinear functions. Adaptive backstepping technique and barrier Lyapunov function method are utilized to design the tracking controller and analyze the closed-loop stability. The proposed control strategy is shown to deal with the presented problem well. Finally, two simulation examples are presented to illustrate the efficacy of the design scheme. INDEX TERMS Adaptive neural control, asymmetric constraint, backstepping, quantized control, switched systems.

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Section: Examplementioning

confidence: 99%

Section: Examplementioning

confidence: 99%

Adaptive Neural Constraint Output Control for a Class of Quantized Input Switched Nonlinear System

2019

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“…To characterize the exponential stability and the switching signal, the following two definitions are recalled [34]. Definition 1 System (3) with

w false(k false) = 0

is exponentially stable, if the solution

x false(k false)

satisfies

∥ x false(k false) ∥ ⩽ δ ϖ^{k - k_{0}} ∥ x false(k false) ∥_{L}

\forall k > k_{0}

, for constant

δ > 0

and

0 < ϖ < 1

, where

∥ x false(k false) ∥_{L} = \sup_{L = 0, \dots, d_{M}} falsefalse{∥ x false(k false) ∥, \dots, ∥ x false(k - L false) ∥, ∥ x false(k false) - x false(k - 1 false) ∥, ∥ x false(k + 1 - L false) - x false(k - L false) ∥ falsefalse}

. Definition 2 For switching signal

σ false(k false)

and any

k_{s} > k_{a} > k_{0}

, let

N_{σ false(k false)} false(k_{a}, k_{s} false)

be the switching numbers of

σ false(k false)

over interval

false[k_{a}, k_{s} false]

.…”

Section: System Description and Preliminariesmentioning

confidence: 99%

“…This completes the proof. □ Remark 2 • The main feature of Theorem 1 is neither the model transformation nor the bounding techniques are used to estimate the upper bound of the cross product terms (see [34, 37, 38]. • To bound the terms in

Δ_{α} V_{i 5} false(k false)

, different methods can be used from the literature to reduce the conservatism of the obtained criteria.…”

Section: False(qsrfalse) ‐Dissipativity Analysismentioning

confidence: 99%

Robust dissipative observer‐based control design for discrete‐time switched systems with time‐varying delay

Regaieg

Kchaou

Bosche

et al. 2019

IET Control Theory & Applications

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“…In practice, effects of time delay [9,10] and impulse [11,12] are usually inevitable. Therefore, there are lots of results on system and input-output analysis of delayed SSs [11][12][13][14][15][16][17][18][19][20][21]. For example of a discrete-time framework, the problem of robust exponential H ∞ filtering for switched fuzzy delayed systems was investigated in [15]; for example of a continuous-time framework, in [11], fault-tolerant synchronization for SSs with delay and impulse was considered.…”

Section: Introductionmentioning

confidence: 99%

External stability and $H_{\infty }$ control of switching systems with delay and impulse

Ren

2020

Adv Differ Equ

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In this paper, we investigate the external stability and $H_{\infty }$ H ∞ control of switching systems with time-varying delay and impulse. First of all, a modified two-direction inequality (relation) between the switching numbers and the maximum, minimum dwell time is proposed. This new inequality is applied to proving the external stability of switching systems with delay and impulse consisting of subsystems with Hurwitz stable matrices of internal dynamics. By using this new inequality, a normal $L_{2}$ L 2 norm constraint is derived rather than weighted $L_{2}$ L 2 norm constraint. In addition, by a realizable switching law, the obtained result is extended to the switching systems comprised of subsystems with both Hurwitz stable and unstable matrices of internal dynamics. The results are finally applied to $H_{\infty }$ H ∞ control and illustrated by a numerical example.

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Robust Exponential $$H_{\infty }$$ H ∞ Filtering for Discrete-Time Switched Fuzzy Systems with Time-Varying Delay

Cited by 30 publications

References 26 publications

Adaptive Neural Constraint Output Control for a Class of Quantized Input Switched Nonlinear System

Adaptive Neural Constraint Output Control for a Class of Quantized Input Switched Nonlinear System

Robust dissipative observer‐based control design for discrete‐time switched systems with time‐varying delay

External stability and $H_{\infty }$ control of switching systems with delay and impulse

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