Dynamical analysis, sliding mode synchronization of a fractional-order memristor Hopfield neural network with parameter uncertainties and its non-fractional-order FPGA implementation

2019 ◽  
Vol 228 (10) ◽  
pp. 2065-2080 ◽  
Author(s):  
Karthikeyan Rajagopal ◽  
Murat Tuna ◽  
Anitha Karthikeyan ◽  
İsmail Koyuncu ◽  
Prakash Duraisamy ◽  
...  
Keyword(s):  
Neural Network ◽  
Fractional Order ◽  
Sliding Mode ◽  
Hopfield Neural Network ◽  
Fpga Implementation ◽  
Dynamical Analysis ◽  
Parameter Uncertainties ◽  
Mode Synchronization

scholarly journals Multiple Loop Fuzzy Neural Network Fractional Order Sliding Mode Control of Micro Gyroscope

Mathematics ◽  
2021 ◽  
Vol 9 (17) ◽  
pp. 2124
Author(s):  
Yunmei Fang ◽  
Fang Chen ◽  
Juntao Fei
Keyword(s):  
Neural Network ◽  
Fractional Order ◽  
Fuzzy Neural Network ◽  
Sliding Mode ◽  
Parameter Uncertainties ◽  
Control Approach ◽  
Switching Law ◽  
Lumped Parameter ◽  
Fuzzy Neural ◽  
The Stability

In this paper, an adaptive double feedback fuzzy neural fractional order sliding control approach is presented to solve the problem that lumped parameter uncertainties cannot be measured and the parameters are unknown in a micro gyroscope system. Firstly, a fractional order sliding surface is designed, and the fractional order terms can provide additional freedom and improve the control accuracy. Then, the upper bound of lumped nonlinearities is estimated online using a double feedback fuzzy neural network. Accordingly, the gain of switching law is replaced by the estimated value. Meanwhile, the parameters of the double feedback fuzzy network, including base widths, centers, output layer weights, inner gains, and outer gains, can be adjusted in real time in order to improve the stability and identification efficiency. Finally, the simulation results display the performance of the proposed approach in terms of convergence speed and track speed.

scholarly journals Observer-based adaptive neural network backstepping sliding mode control for switched fractional order uncertain nonlinear systems with unmeasured states

2021 ◽  
pp. 002029402110211
Author(s):  
Tao Chen ◽  
Damin Cao ◽  
Jiaxin Yuan ◽  
Hui Yang
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Sliding Mode Control ◽  
Fractional Order ◽  
Sliding Mode ◽  
Tracking Error ◽  
Adaptive Neural Network ◽  
Dynamic Surface ◽  
Mode Control ◽  
The Stability

This paper proposes an observer-based adaptive neural network backstepping sliding mode controller to ensure the stability of switched fractional order strict-feedback nonlinear systems in the presence of arbitrary switchings and unmeasured states. To avoid “explosion of complexity” and obtain fractional derivatives for virtual control functions continuously, the fractional order dynamic surface control (DSC) technology is introduced into the controller. An observer is used for states estimation of the fractional order systems. The sliding mode control technology is introduced to enhance robustness. The unknown nonlinear functions and uncertain disturbances are approximated by the radial basis function neural networks (RBFNNs). The stability of system is ensured by the constructed Lyapunov functions. The fractional adaptive laws are proposed to update uncertain parameters. The proposed controller can ensure convergence of the tracking error and all the states remain bounded in the closed-loop systems. Lastly, the feasibility of the proposed control method is proved by giving two examples.

scholarly journals Nonlinear Dynamics and Chaos in Fractional-Order Hopfield Neural Networks with Delay

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Xia Huang ◽  
Zhen Wang ◽  
Yuxia Li
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fractional Order ◽  
Hopfield Neural Network ◽  
Hopfield Neural Networks ◽  
Phase Portraits ◽  
Chaotic Attractors ◽  
Chaotic Motions ◽  
Dynamical Behaviors ◽  
Nonlinear Dynamics And Chaos

A fractional-order two-neuron Hopfield neural network with delay is proposed based on the classic well-known Hopfield neural networks, and further, the complex dynamical behaviors of such a network are investigated. A great variety of interesting dynamical phenomena, including single-periodic, multiple-periodic, and chaotic motions, are found to exist. The existence of chaotic attractors is verified by the bifurcation diagram and phase portraits as well.

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