Dynamical neural networks-based inverse optimal sliding mode controller

2016 ◽  
Author(s):  
Ayman K. Alhejji ◽  
Mohamed R. Sayeh
Keyword(s):  
Neural Networks ◽  
Sliding Mode ◽  
Sliding Mode Controller ◽  
Dynamical Neural Networks

scholarly journals Neural adaptive sliding mode controller for unmanned surface vehicle steering system

2018 ◽  
Vol 10 (9) ◽  
pp. 168781401879552 ◽  
Author(s):  
Lili Wan ◽  
Yixin Su ◽  
Huajun Zhang ◽  
Yongchuan Tang ◽  
Binghua Shi
Keyword(s):  
Neural Networks ◽  
Sliding Mode Control ◽  
Radial Basis Function ◽  
Basis Function ◽  
Sliding Mode ◽  
Steering System ◽  
Sliding Mode Controller ◽  
Unmanned Surface Vehicle ◽  
Adaptive Sliding Mode Controller ◽  
Radial Basis

Unmanned surface vehicle has the properties such as complexity, nonlinearity, time variability, and uncertainty, which lead to the difficulty of obtaining a precise kinematics model. A neural adaptive sliding mode controller for the unmanned surface vehicle steering system is developed based on the sliding mode control technique and the radial basis function neural network. In the new approach, two parallel radial basis function neural networks are used to reduce the influence of the system uncertainties and eliminate the dependency of the controller on the precise kinematics model of the system. Among these two radial basis function neural networks, one is used to approximate the unknown nonlinear yaw dynamics and the other is used to adjust the control gain as well as realize the variable gain sliding mode control. The weights of the two neural networks are trained online using the sliding surface variable and the control, where the Lyapunov method is used to derive the adaptive laws to ensure the stability of the whole closed-loop system. The proposed adaptive controller is suitable for the steering control at different cruising speeds with bounded external disturbances. The simulation results show that the proposed controller has a good control performance regarding the smooth control, fast response, and high accuracy.

scholarly journals Projective Synchronization of Nonidentical Fractional-Order Memristive Neural Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Chong Chen ◽  
Zhixia Ding
Keyword(s):  
Neural Networks ◽  
Fractional Order ◽  
Sliding Mode ◽  
Direct Method ◽  
Projective Synchronization ◽  
Sliding Mode Controller ◽  
Memristive Neural Networks ◽  
Lyapunov Direct Method ◽  
Sliding Motion ◽  
Fractional Order Integral

This paper investigates projective synchronization of nonidentical fractional-order memristive neural networks (NFMNN) via sliding mode controller. Firstly, based on the sliding mode control theory, a new fractional-order integral sliding mode controller is designed to ensure the occurrence of sliding motion. Furthermore, according to fractional-order differential inequalities and fractional-order Lyapunov direct method, the trajectories of the system converge to the sliding mode surface to carry out sliding mode motion, and some sufficient criteria are obtained to achieve global projective synchronization of NFMNN. In addition, the conclusions extend and improve some previous works on the synchronization of fractional-order memristive neural networks (FMNN). Finally, a simulation example is given to verify the effectiveness and correctness of the obtained results.

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