Dual Adaptive Neural Network Controller for Underactuated Systems

Robotica ◽  
2021 ◽  
pp. 1-18
Author(s):  
Seyed Hassan Zabihifar ◽  
Hamed Navvabi ◽  
Arkady Semenovich Yushchenko
Keyword(s):  
Neural Network ◽  
Adaptive Controller ◽  
Underactuated Systems ◽  
Adaptive Neural Network ◽  
Neural Network Controller ◽  
Network Controller ◽  
Control Scheme ◽  
Equivalent Control ◽  
Inertia Wheel Pendulum ◽  
The Given

SUMMARY A new stable adaptive controller based on a neural network for underactuated systems is proposed in this paper. The control scheme has been developed for two underactuated systems as examples. The Furuta pendulum and the Inertia Wheel Pendulum (IWP) have been examined in this paper. The presented approach aims to address the control problem of the given system in swing up, stabilization, and disturbance rejection. To avoid oscillations, two adaptive neural networks (ANNs) are implemented. The first one is used to approximate the equivalent control online and the second one to minimize the oscillations.

scholarly journals Adaptive Neural Network Control with Backstepping for Surface Ships with Input Dead-Zone

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Guoqing Xia ◽  
Xingchao Shao ◽  
Ang Zhao ◽  
Huiyong Wu
Keyword(s):  
Neural Network ◽  
Explicit Knowledge ◽  
Dead Zone ◽  
Nonlinear Function ◽  
Lyapunov Theory ◽  
Adaptive Neural Network ◽  
Neural Network Controller ◽  
Network Controller ◽  
Adaptive Neural Network Control ◽  
Fuzzy Logic Technique

This paper addresses the problem of adaptive neural network controller with backstepping technique for fully actuated surface vessels with input dead-zone. The combination of approximation-based adaptive technique and neural network system is used for approximating the nonlinear function of the ship plant. Through backstepping and Lyapunov theory synthesis, an indirect adaptive network controller is derived for dynamic positioning ships without dead-zone property. In order to improve the control effect, a dead-zone compensator is derived using fuzzy logic technique to handle the dead-zone nonlinearity. The main advantage of the proposed controller is that it can be designed without explicit knowledge about the ship motion model, and dead-zone nonlinearity is well compensated. A set of simulations is carried out to verify the performance of the proposed controller.

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