Neural Network-Based an Adaptive Discrete-Time Global Sliding Mode Control Scheme

2006 ◽  
pp. 565-570
Author(s):  
Zhenyan Wang ◽  
Jinggang Zhang ◽  
Zhimei Chen ◽  
Yanzhao He
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
Discrete Time ◽  
Sliding Mode ◽  
Mode Control ◽  
Global Sliding Mode Control ◽  
Control Scheme

scholarly journals A New Control Method for Second-Order Multiple Models Control System Based on Global Sliding Mode

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Dan-xu Zhang ◽  
Yang-wang Fang ◽  
Peng-fei Yang ◽  
You-li Wu ◽  
Tong-xin Liu
Keyword(s):  
Control System ◽  
Sliding Mode Control ◽  
Finite Time ◽  
Sliding Mode ◽  
Second Order ◽  
Multiple Models ◽  
Finite Time Convergence ◽  
Mode Control ◽  
Global Sliding Mode Control ◽  
Control Scheme

This paper proposed a finite time convergence global sliding mode control scheme for the second-order multiple models control system. Firstly, the global sliding surface without reaching law for a single model control system is designed and the tracking error finite time convergence and global stability are proved. Secondly, we generalize the above scheme to the second-order multimodel control system and obtain the global sliding mode control law. Then, the convergent and stable performances of the closed-loop control system with multimodel controllers are proved. Finally, a simulation example shows that the proposed control scheme is more effective and useful compared with the traditional sliding mode control scheme.

Neural network–based sliding-mode control of a tendon sheath–actuated compliant rescue manipulator

2019 ◽  
Vol 233 (8) ◽  
pp. 1055-1066 ◽  
Author(s):  
Qingcong Wu ◽  
Xingsong Wang ◽  
Bai Chen ◽  
Hongtao Wu
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
Position Control ◽  
Sliding Mode ◽  
Radial Basis Function Network ◽  
Tendon Sheath ◽  
Step Response ◽  
Mode Control ◽  
Control Scheme ◽  
Modeling Uncertainties

The novel contribution of this article is to propose a neural network–based sliding-mode control strategy for improving the position-control performance of a tendon sheath–actuated compliant rescue manipulator. Structural design of a rescue robot with slender and compliant mechanical structure is introduced. The developed robot is capable of drilling into the narrow space under debris and accommodating complicated configuration in ruins. Dynamics modeling and parameters identification of a compliant gripper with flexible tendon sheath transmission are researched and discussed. Moreover, the neural network–based sliding-mode control scheme developed based on radial basis function network is proposed to improve the position-control accuracy of the gripper with modeling uncertainties and external disturbances. The stability of the proposed control system is demonstrated using Lyapunov stability theory. Further experimental investigation including trajectory-tracking experiments and step-response experiments are conducted to confirm the effectiveness of the proposed neural network–based sliding-mode control scheme. Experimental results show that the proposed neural network–based sliding-mode control scheme is superior to cascaded proportional–integral–derivative controller and conventional sliding-mode controller in position-control application.

scholarly journals Neural Network PID Algorithm for a Class of Discrete-Time Nonlinear Systems

2018 ◽  
Vol 14 (02) ◽  
pp. 103 ◽  
Author(s):  
Huifang Kong ◽  
Yao Fang
Keyword(s):  
Neural Network ◽  
Nonlinear System ◽  
Sliding Mode Control ◽  
Discrete Time ◽  
Control Algorithm ◽  
Sliding Mode ◽  
Model Parameters ◽  
Pid Algorithm ◽  
Mode Control ◽  
Model Free

<p class="0abstract"><span lang="EN-US">The control of nonlinear system is the hotspot in the control field. The paper proposes an algorithm to solve the tracking and robustness problem for the discrete-time nonlinear system. The completed control algorithm contains three parts. First, the dynamic linearization model of nonlinear system is designed based on Model Free Adaptive Control, whose model parameters are calculated by the input and output data</span><span lang="EN-US"> of system</span><span lang="EN-US">. Second, the model error is estimated using the Quasi-sliding mode control algorithm</span><span lang="EN-US">, hence, the whole model of system is estimated</span><span lang="EN-US">. Finally, the neural network </span><span lang="EN-US">PID </span><span lang="EN-US">controller is designed to get the optimal control law. The convergence and BIBO stability of the control system is proved by the Lyapunov function. The simulation results </span><span lang="EN-US">in</span><span lang="EN-US"> the </span><span lang="EN-US">linear and </span><span lang="EN-US">nonlinear system validate the effectiveness and robustness of the algorithm.</span><span lang="EN-US"> The robustness </span><span lang="EN-US">effort </span><span lang="EN-US">of </span><span lang="EN-US">Quasi-sliding mode control algorithm</span><span lang="EN-US"> in nonlinear system is also verified in the paper.</span></p>

Robust predictive sliding mode control for input rate-constrained discrete-time system

2018 ◽  
Vol 36 (3) ◽  
pp. 901-919 ◽  
Author(s):  
Rong Li ◽  
Qingxian Wu
Keyword(s):  
Sliding Mode Control ◽  
Discrete Time ◽  
Sliding Mode ◽  
Input Rate ◽  
Discrete Time System ◽  
Control Approach ◽  
Mode Control ◽  
Discrete Time Systems ◽  
Control Scheme ◽  
Time Systems

Abstract This paper investigates a class of uncertain linear discrete-time systems subject to input rate saturation. A predictive sliding mode control approach is proposed which guarantees the control inputs remain bounded in the input rate saturation. Furthermore, the disturbance observer is developed to compensate for the system uncertainty and disturbance. Finally, the simulations demonstrate the effectiveness of the proposed predictive sliding mode control scheme.

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