Disturbance rejection of fractional-order T-S fuzzy neural networks based on quantized dynamic output feedback controller

Karthick, S.; Sakthivel, R.; Ma, Youjie; Mohanapriya, S.; Leelamani, A.

doi:10.1016/j.amc.2019.06.029

Cited by 36 publications

(20 citation statements)

References 35 publications

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“…▪ Remark 9. Finite-time stabilization is a special case of fixed-time stabilization (if we take Υ = 0 Υ = R, I, J, K; = 1, … , n in (5), we obtain the finite-time control (20)). It should be noted that fixed-time stabilization can be realized within a settling time, which is independent of any initial values of the considered network and is only related to some known parameters of proposed controllers.…”

Section: Corollary 1 Under Assumption 1 System (3) Is Finite-time Smentioning

confidence: 99%

“…So it is important to analyze his dynamics behaviors. 8,[17][18][19][20][21][22][23] Rather than Lyapunov's classic asymptotic stability 6,[24][25][26][27] and exponential stability, 28 finite-time stability means that the system's solution trajectories converge the equilibrium point after a finite-time, and the finite-time is called the settling time or time convergence. [29][30][31][32] The finite-time stability is involved in many control problems, such as secure communication, 33 finite-time output feedback stabilization of the double integrator, 34 and the finite-time attitude tracking problem for a single spacecraft and multiple spacecraft.…”

Section: Introductionmentioning

confidence: 99%

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New feedback control techniques of quaternion fuzzy neural networks with time‐varying delay

Aouiti

Jallouli

2021

Intl J Robust & Nonlinear

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This article addresses the problems of fixed-time stabilization for a class of quaternion fuzzy neural networks (QFNNs) with time-varying delay. The QFNNs are developed by dividing our system into four real-valued parts based on the Hamilton rule. Then, based on fixed-time stability theory, some inequality techniques, and selecting the appropriate controllers and Lyapunov function, a novel criterion guaranteeing the fixed-time stabilization and the finite-time stabilization of the addressed system is derived. Finally, three numerical examples are presented to show the effectiveness of our theoretical results. K E Y W O R D S finite-time stabilization, fixed-time stabilization, fuzzy neural networks, quaternion neural networks, time-varying delay

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Section: Corollary 1 Under Assumption 1 System (3) Is Finite-time Smentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

New feedback control techniques of quaternion fuzzy neural networks with time‐varying delay

Aouiti

Jallouli

2021

Intl J Robust & Nonlinear

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“…e most obvious advantage of this method is that it uses less fuzzy rules, and then the linear subsystems are fuzzy combined to get the overall model of the system. e results of control analysis and stability analysis with the T-S control method are also reported in the literature [33][34][35]. In [33], under the condition of quantization, a dynamic output feedback controller was used to control the fuzzy T-S fractional-order neural network.…”

Section: Introductionmentioning

confidence: 98%

“…e results of control analysis and stability analysis with the T-S control method are also reported in the literature [33][34][35]. In [33], under the condition of quantization, a dynamic output feedback controller was used to control the fuzzy T-S fractional-order neural network. A static feedback controller with fuzzy T-S structure was designed in [34].…”

Section: Introductionmentioning

confidence: 98%

T-S Fuzzy Control of Uncertain Fractional-Order Systems with Time Delay

Hao

Zhang

2021

Journal of Mathematics

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In this article, the adaptive control of uncertain fractional-order time-delay systems (FOTDSs) with external disturbances is discussed. A Takagi-Sugenu (T-S) fuzzy model with if-then rules is adopted to characterize the dynamic equation of the FOTDS. Besides, a fuzzy adaptive method is proposed to stabilize the model. By utilizing the Lyapunov functions, a robust controller is constructed to stabilize the FOTDS. Due to the uncertainty of system parameters, some fractional-order adaptation laws are designed to update these parameters. At the same time, some if-then rules with linear structure based on the fuzzy T-S adaption concept are established. The designed method not only guarantees that the state of closed-loop system asymptotically converges to origin but also keeps the signal in the FOTDS bounded. Finally, the applicability of the control method is proved by simulation examples.

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“…Enlightened by the quantization in communication systems, researchers are more and more focused on quantized control of uncertain systems subject to input quantization. Logarithmic quantizer (LQ) [10], [11], one of the most employed quantizers in digital communication systems, can largely reduce the communication data size and meanwhile, it is capable of generating quantized signals with constant quantization signal-to-noise ratio (SNR) owing to its exponentially transformed quantization levels compared with uniform quantizer (UQ) [12], such that the robustness of system will not change along with the variation of control signal magnitude. Nevertheless, the introduction of input quantization typically induces unexpected quantization error, which will inevitably influence the tracking accuracy of MEMS gyroscope, while to the best of authors' knowledge, how to make a compromise between the tracking performance and bandwidth restriction is rarely studied in existing results [4]- [9].…”

Section: Introductionmentioning

confidence: 99%

MLP-Based Neural Guaranteed Performance Control for MEMS Gyroscope With Logarithmic Quantizer

Shao

Zhang

2020

IEEE Access

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In this paper, a minimum-learning-parameter (MLP) based neural control method is proposed for micro-electro-mechanical system (MEMS) gyroscope with prescribed performance and input quantization. For the first time, a logarithmic quantizer (LQ) is employed to generate smooth input control signal for MEMS gyroscope, which greatly reduces the communication data size as well as actuator bandwidth. To improve the performance of MEMS gyroscope in the presence of quantization error, a prescribed performance control scheme consisting of preselected performance boundaries and an error transformation is utilized, such that preselected transient and steady-state properties can be assured. In contrast to the neural control strategies subject to the issue of learning explosion, a MLP-based neural network (NN) is introduced to estimate the unknown uncertainties using the norm of neural weight. To eliminate the effect of quantization error induced by LQ, a robust quantized control is designed to further ensure the closed-loop system suffering from discontinuous dynamics with prescribed ultimately uniformly bounded (UUB) performance. In the end, a series of simulations are presented to validate the superiority of the proposed control methodology.

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Disturbance rejection of fractional-order T-S fuzzy neural networks based on quantized dynamic output feedback controller

Cited by 36 publications

References 35 publications

New feedback control techniques of quaternion fuzzy neural networks with time‐varying delay

New feedback control techniques of quaternion fuzzy neural networks with time‐varying delay

T-S Fuzzy Control of Uncertain Fractional-Order Systems with Time Delay

MLP-Based Neural Guaranteed Performance Control for MEMS Gyroscope With Logarithmic Quantizer

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