Sliding Mode Control Theory-Based Parameter Adaptation Rules for Fuzzy Neural Networks

2016 ◽  
pp. 85-131 ◽  
Author(s):  
Erdal Kayacan ◽  
Mojtaba Ahmadieh Khanesar
Keyword(s):  
Neural Networks ◽  
Control Theory ◽  
Sliding Mode Control ◽  
Sliding Mode ◽  
Fuzzy Neural Networks ◽  
Parameter Adaptation ◽  
Mode Control ◽  
Fuzzy Neural ◽  
Adaptation Rules

scholarly journals Improved Adaptive Sliding Mode Control for a Class of Uncertain Nonlinear Systems Subjected to Input Nonlinearity via Fuzzy Neural Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Tat-Bao-Thien Nguyen ◽  
Teh-Lu Liao ◽  
Jun-Juh Yan
Keyword(s):  
Neural Networks ◽  
Adaptive Control ◽  
Sliding Mode Control ◽  
Sliding Mode ◽  
Fuzzy Neural Networks ◽  
Adaptive Sliding Mode Control ◽  
Input Nonlinearity ◽  
Mode Control ◽  
Model Dynamics ◽  
Fuzzy Neural

The paper presents an improved adaptive sliding mode control method based on fuzzy neural networks for a class of nonlinear systems subjected to input nonlinearity with unknown model dynamics. The control scheme consists of the modified adaptive and the compensation controllers. The modified adaptive controller online approximates the unknown model dynamics and input nonlinearity and then constructs the sliding mode control law, while the compensation controller takes into account the approximation errors and keeps the system robust. Based on Lyapunov stability theorem, the proposed method can guarantee the asymptotic convergence to zero of the tracking error and provide the robust stability for the closed-loop system. In addition, due to the modification in controller design, the singularity problem that usually appears in indirect adaptive control techniques based on fuzzy/neural approximations is completely eliminated. Finally, the simulation results performed on an inverted pendulum system demonstrate the advanced functions and feasibility of the proposed adaptive control approach.

scholarly journals Parallel robot with fuzzy neural network sliding mode control

2018 ◽  
Vol 10 (10) ◽  
pp. 168781401880126 ◽  
Author(s):  
Jiangmin Xu ◽  
Qi Wang ◽  
Qing Lin
Keyword(s):  
Neural Network ◽  
Control Theory ◽  
Sliding Mode Control ◽  
Fuzzy Neural Network ◽  
Sliding Mode ◽  
Parallel Robot ◽  
Parallel Robots ◽  
Adaptive Sliding Mode Control ◽  
Mode Control ◽  
Fuzzy Neural

With the advancement in research on parallel robots, control theory is increasingly applied in the field of robotics. Owing to its robustness, sliding mode variable structure control is extensively used in parallel robots. This article presents an adaptive sliding mode control method for nonlinear systems. A parallel robot control model with adaptive fuzzy sliding mode control was designed based on a fuzzy neural network control theory, and simulation results demonstrate its effectiveness of the method.

Neural Networks Learning With Sliding Mode Control: The Sliding Mode Backpropagation Algorithm

1999 ◽  
Vol 09 (03) ◽  
pp. 187-193 ◽  
Author(s):  
GUSTAVO G. PARMA ◽  
BENJAMIN R. DE MENEZES ◽  
ANTÔNIO P. BRAGA
Keyword(s):  
Neural Networks ◽  
Control Theory ◽  
Sliding Mode Control ◽  
High Speed ◽  
Sliding Mode ◽  
Multi Layer Perceptron ◽  
Backpropagation Algorithm ◽  
Mode Control

Based on the classical backpropagation weight update equations, sliding mode control theory is introduced as a technique to adapt weights of a multi-layer perceptron. As will be demonstrated, the introduction of sliding mode has resulted in a much faster version of the standard backpropagation. The results show also that the proposed algorithm presents some important features of sliding mode control, which are robustness and high speed of learning. In addition to that, this paper shows also how control theory can be applied to train neural networks

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