Intelligent dynamic sliding-mode neural control using recurrent perturbation fuzzy neural networks

Hsu, Chun‐Fei; Chang, Chun‐Wei

doi:10.1016/j.neucom.2015.08.024

Cited by 21 publications

(6 citation statements)

References 37 publications

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“…To solve the problem of poor dynamic characteristics of traditional feed-forward neural network, the recurrent neural network (RNN) composed of feed-forward neural network and feedback loop can obtain more information whiling processing new inputs, thus obtaining better dynamic characteristics [26]. The recurrent perturbation neural network is proposed for inverted pendulum problem and chaotic synchronization problem [27]. RNNs are also used to estimate the nonlinear terms in an active power filter [28][29].…”

Section: Introductionmentioning

confidence: 99%

Double Recurrent Perturbation Fuzzy Neural Network Fractional-Order Sliding Mode Control of Micro Gyroscope

2021

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In this paper, a fractional-order sliding mode control method based on a double recurrent perturbation fuzzy neural network (DRPFNN) is proposed for a micro gyroscope system with parameter uncertainty and external disturbance. The DRPFNN is used to realize the adaptive estimation of the unknown part, which ensures that the controller is independent of the accurate mathematical model. The sine-cosine perturbation membership function is used to deal with the uncertainty of rules in neural network, increasing the accuracy, reduce the calculation load, and simplifying computational complexity. In addition, double recurrent links are added to transmit more information with superior dynamic ability. Then, the proposed control system is composed of a DRPFNN estimator and a fractional-order sliding mode controller (FSMC). The fractional calculus operator is introduced into the sliding surface to improve the controller flexibility. The parameter adaptive laws in the neural network are designed to be adaptively stabilized to the optimal value. Finally, simulation studies verify the effectiveness of the proposed control method, showing it can obtain higher control accuracy and enhance robustness than the traditional sliding mode control method. INDEX TERMSDouble recurrent perturbation fuzzy neural network; Recurrent neural network; Sliding mode control; Fractional order; Micro gyroscope.

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Section: Introductionmentioning

confidence: 99%

Double Recurrent Perturbation Fuzzy Neural Network Fractional-Order Sliding Mode Control of Micro Gyroscope

2021

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“…Fuzzy technology joined forces with artificial neural networks and genetic algorithms under the title of computational intelligence or soft computing. In recent years, the research on the dynamical behaviours of fuzzy neural networks has attracted much attention, see [18–22]. To summarize, we consider the following CNNs with stochastic perturbations and fuzzy operations: where α ij , β ij , T ij and S ij are elements of fuzzy feedback MIN, MAX template, fuzzy feed forward MIN and MAX template, respectively; ⋀ and ⋁ denote the fuzzy AND and fuzzy OR operation, respectively; d ij , η ij and σ j are similarly specified as that in system (1), w j is the standard Brownian motion defined on a complete probability space, i , j = 1, 2, …, n .…”

Section: Introductionmentioning

confidence: 99%

Mean almost periodicity and moment exponential stability of semi-discrete random cellular neural networks with fuzzy operations

2019

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By using the semi-discretization technique of differential equations, the discrete analogue of a kind of cellular neural networks with stochastic perturbations and fuzzy operations is formulated, which gives a more accurate characterization for continuous-time models than that by Euler scheme. Firstly, the existence of at least one p -th mean almost periodic sequence solution of the semi-discrete stochastic models with almost periodic coefficients is investigated by using Minkowski inequality, Hölder inequality and Krasnoselskii’s fixed point theorem. Secondly, the p -th moment global exponential stability of the semi-discrete stochastic models is also studied by using some analytical skills and the proof of contradiction. Finally, a problem of stochastic stabilization for discrete cellular neural networks is studied.

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“…To overcome this deficiency, a large amount of improvements to reduce the chatter have been developed. In Korkut and Guller (2007), Sun et al (2011), Hsu and Chang (2016) and Tang et al (2013), intelligent, adaptive and fractional enhancements for the chattering reduction have been investigated. Likewise, the methods based on exponent approach law have been proposed in Wang et al (2016) and Hou and Zhang (2016).…”

Section: Introductionmentioning

confidence: 99%

Sliding mode controller design for wood drying process

Zhou

Wang

2018

Wood Sci Technol

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This paper deals with the design of sliding mode controllers for the wood drying process. Prediction models based on a nonlinear autoregressive model with exogenous inputs of dry-bulb temperature and equilibrium moisture content during drying were built to design the controllers. A comparative study was performed in order to determine the controller presenting the better performance in practical application. The compared strategies were sliding mode control based on exponent approach law and variable rate approach law. The second controller was introduced to optimize the controller while reducing the chattering phenomenon in comparison with the exponent approach law. To demonstrate the effectiveness of the proposed control strategies, simulation and experimental results were presented.

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Intelligent dynamic sliding-mode neural control using recurrent perturbation fuzzy neural networks

Cited by 21 publications

References 37 publications

Double Recurrent Perturbation Fuzzy Neural Network Fractional-Order Sliding Mode Control of Micro Gyroscope

Double Recurrent Perturbation Fuzzy Neural Network Fractional-Order Sliding Mode Control of Micro Gyroscope

Mean almost periodicity and moment exponential stability of semi-discrete random cellular neural networks with fuzzy operations

Sliding mode controller design for wood drying process

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