Reduced vibration of off-road vehicle nonlinear suspension system using an adaptive integral sliding mode-neural network controller

2019 ◽  
Vol 8 (1) ◽  
pp. 291-301 ◽  
Author(s):  
Hamid Taghavifar
Keyword(s):  
Neural Network ◽  
Sliding Mode ◽  
Suspension System ◽  
Road Vehicle ◽  
Integral Sliding Mode ◽  
Neural Network Controller ◽  
Network Controller

Aviation Flight Attitude Control Technology

2014 ◽  
Vol 716-717 ◽  
pp. 1567-1571
Author(s):  
Jun Hu
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Sliding Mode ◽  
Gaussian White Noise ◽  
Training Data ◽  
External Interference ◽  
Neural Network Controller ◽  
Network Controller ◽  
Fuzzy Neural ◽  
The Impact

This paper presents a range of two miniature spacecraft attitude Fuzzy Neural Network Sliding adaptive controller, using a weighting factor to combine indirect and direct fuzzy neural network controller fuzzy neural network controller. Interval two free parameters of fuzzy neural network adaptive sliding mode controller via output feedback control law and adaptive law be adjusted online. Simulation results show that after joining 10db Gaussian white noise, and in order to reduce the impact of external interference and noise training data, a controller with respect to the range of large amount of control type II generated by the controller. Overall adaptive scheme guarantees the global stability of the closed-loop system, all signals involved are bounded in some way, and also showed a high level of tracking performance.

Stable Neural Network Controller Based Observer for Rigid Robot Manipulators

2003 ◽  
Vol 15 (1) ◽  
pp. 77-83 ◽  
Author(s):  
Boubaker Daâchi ◽  
◽  
Abdelaziz Benallegue
Keyword(s):  
Neural Network ◽  
Sliding Mode ◽  
Robot Manipulators ◽  
Stability Study ◽  
Control Law ◽  
Neural Net ◽  
Adaptation Algorithm ◽  
Neural Network Controller ◽  
Network Controller ◽  
Joint Velocity

We propose a neural network controller using only joint position measurements for rigid robot manipulators. The joint velocity needed for the control law is estimated using an observer based on sliding mode. A decomposed structure neural network approximates the unknown model of the system. Each neural network (MLP) approximates a separate element of the dynamical model. These approximations are used to conduct an adaptive stable control law. The TaylorYoung series was used to solve the nonlinearity problem of the MLP and to lead to the parameters adaptation algorithm. The corresponding parameters are the weights of the neural net. They are updated via the adaptation algorithm derived from stability study of the system in closed loop using the Lyapunov approach and intrinsic properties of robot manipulators. Simulations were conducted to show the conductance of the proposed controller.

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