scholarly journals Parameter Identification and Control Algorithm of Electrohydraulic Servo System for Robotic Excavator Based on Improved Hammerstein Model

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Shen Jinxing ◽  
Cui Hongxin ◽  
Feng Ke ◽  
Zhang Hong ◽  
Li Huanliang
Keyword(s):  
Control Algorithm ◽  
Servo System ◽  
Model Error ◽  
Hammerstein Model ◽  
Time Varying ◽  
Forgetting Factor ◽  
Robotic Excavator ◽  
Time Varying Systems ◽  
Electrohydraulic Servo System ◽  
And Control

In view of the nonlinearity and time-varying characteristics of the electrohydraulic servo system of the robotic excavator, a nonlinear adaptive identification and control algorithm based on improved Hammerstein model is proposed. The Hammerstein algorithm model can approximate the nonlinear system with enough precision, but for the time-varying systems is not satisfactory. In order to compensate for the influence of time-varying factors, the fuzzy control module is designed to adaptively update the forgetting factor. The experimental results show that the improved Hammerstein model error is about 40.11% less than the classical Hammerstein model error. This proves that the improved Hammerstein model is feasible and effective to describe the electrohydraulic servo system of the robotic excavator.

Study of Hydraulic Servo System Based on Fuzzy Sliding Mode Algorithm

2013 ◽  
Vol 753-755 ◽  
pp. 2674-2678
Author(s):  
Kun Yang ◽  
Cai Jun Liu ◽  
Shu Min Liu
Keyword(s):  
Control Algorithm ◽  
Sliding Mode ◽  
Servo System ◽  
Time Varying ◽  
External Interference ◽  
System A ◽  
Hydraulic Servo ◽  
Fuzzy Sliding Mode ◽  
Hydraulic Servo System ◽  
Position Servo

Based on the situation that the hydraulic position servo system is easily influenced by the external interference and the parameters of which are different with time-varying, the fuzzy control can soften the buffeting and the sliding algorithm has no the same problems as the hydraulic position servo system, a brandly-new fuzzy sliding control algorithm is designed. In the simulation process, within the parameters of simulated time-varying and outside strong interference, the results show that the hydraulic servo system based on fuzzy sliding mode control algorithm has a greater resistance to internal and external interference and time-varying parameters.

Iterative learning identification and control for point-to-point tracking of linear time-varying systems with unknown parameters and stochastic noise

2017 ◽  
Vol 40 (13) ◽  
pp. 3834-3845 ◽  
Author(s):  
Yan Geng ◽  
Xiaoe Ruan
Keyword(s):  
Linear Time ◽  
Tracking Error ◽  
Iterative Learning ◽  
Time Varying ◽  
Unknown Parameters ◽  
Point Tracking ◽  
Time Varying Systems ◽  
System Output ◽  
Point To Point ◽  
And Control

In this paper, an interactive iterative learning identification and control (ILIC) scheme is developed for a class of discrete-time linear time-varying systems with unknown parameters and stochastic noise to implement point-to-point tracking. The identification is to iteratively estimate the unknown system parameter matrix by adopting the gradient-type technique for minimizing the distance of the system output from the estimated system output, whilst the control law is to iteratively upgrade the current control input with the current point-to-point tracking error scaled by the estimated system parameter matrix. Thus, the iterative learning identification and the iterative learning control are scheduled in an interactive mode. By means of norm theory, the boundedness of the discrepancy between the system matrix estimation and the real one is derived, whilst, by the manner of the statistical technique, it is conducted that the mathematical expectation of the tracking error monotonically converges to nullity and the variance of the tracking error is bounded. Numerical simulations exhibit the validity and effectiveness of the proposed ILIC scheme.

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