Adaptive Neural Network Control for Uncertain Time-Varying State Constrained Robotics Systems

2019 ◽  
Vol 49 (12) ◽  
pp. 2511-2518 ◽  
Author(s):  
Shu-Min Lu ◽  
Da-Peng Li ◽  
Yan-Jun Liu
Keyword(s):  
Neural Network ◽  
Network Control ◽  
Time Varying ◽  
Neural Network Control ◽  
Adaptive Neural Network ◽  
Adaptive Neural Network Control ◽  
Uncertain Time

scholarly journals Adaptive Neural Network Control for Nonlinear Hydraulic Servo-System with Time-Varying State Constraints

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Shu-Min Lu ◽  
Dong-Juan Li
Keyword(s):  
Neural Network ◽  
State Constraints ◽  
Servo System ◽  
Network Control ◽  
Time Varying ◽  
Neural Network Control ◽  
Adaptive Neural Network ◽  
Adaptive Neural Network Control ◽  
Hydraulic Servo ◽  
Hydraulic Servo System

An adaptive neural network control problem is addressed for a class of nonlinear hydraulic servo-systems with time-varying state constraints. In view of the low precision problem of the traditional hydraulic servo-system which is caused by the tracking errors surpassing appropriate bound, the previous works have shown that the constraint for the system is a good way to solve the low precision problem. Meanwhile, compared with constant constraints, the time-varying state constraints are more general in the actual systems. Therefore, when the states of the system are forced to obey bounded time-varying constraint conditions, the high precision tracking performance of the system can be easily realized. In order to achieve this goal, the time-varying barrier Lyapunov function (TVBLF) is used to prevent the states from violating time-varying constraints. By the backstepping design, the adaptive controller will be obtained. A radial basis function neural network (RBFNN) is used to estimate the uncertainties. Based on analyzing the stability of the hydraulic servo-system, we show that the error signals are bounded in the compacts sets; the time-varying state constrains are never violated and all singles of the hydraulic servo-system are bounded. The simulation and experimental results show that the tracking accuracy of system is improved and the controller has fast tracking ability and strong robustness.

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