Precise friction control for the nonlinear friction system using the friction state observer and sliding mode control with recurrent fuzzy neural networks

Mechatronics ◽  
2009 ◽  
Vol 19 (6) ◽  
pp. 805-815 ◽  
Author(s):  
Han Me Kim ◽  
Soo Hong Park ◽  
Seong Ik Han
Keyword(s):  
Neural Networks ◽  
Sliding Mode Control ◽  
Sliding Mode ◽  
State Observer ◽  
Fuzzy Neural Networks ◽  
Nonlinear Friction ◽  
Friction Control ◽  
Mode Control ◽  
Fuzzy Neural

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 An extended state observer-based full-order sliding mode control for robotic joint actuated by antagonistic pneumatic artificial muscles

2021 ◽  
Vol 18 (1) ◽  
pp. 172988142098603
Author(s):  
Daoxiong Gong ◽  
Mengyao Pei ◽  
Rui He ◽  
Jianjun Yu
Keyword(s):  
Sliding Mode Control ◽  
Sliding Mode ◽  
State Observer ◽  
Extended State Observer ◽  
Artificial Muscles ◽  
Extended State ◽  
Robot System ◽  
Mode Control ◽  
Physical Experiments ◽  
Pneumatic Artificial Muscles

Pneumatic artificial muscles (PAMs) are expected to play an important role in endowing the advanced robot with the compliant manipulation, which is very important for a robot to coexist and cooperate with humans. However, the strong nonlinear characteristics of PAMs hinder its wide application in robots, and therefore, advanced control algorithms are urgently needed for making the best use of the advantages and bypassing the disadvantages of PAMs. In this article, we propose a full-order sliding mode control extended state observer (fSMC-ESO) algorithm that combines the ESO and the fSMC for a robotic joint actuated by a pair of antagonistic PAMs. The fSMC is employed to eliminate the chattering and to guarantee the finite-time convergence, and the ESO is adopted to observe both the total disturbance and the states of the robot system, so that we can inhibit the disturbance and compensate the nonlinearity efficiently. Both simulations and physical experiments are conducted to validate the proposed method. We suggest that the proposed method can be applied to the robotic systems actuated by PAMs and remarkably improve the performance of the robot system.

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