Force Tracking Control of a Testing Device II: Controller Implementation

2010 ◽  
Author(s):  
Jian Chen ◽  
Peng Li ◽  
Xuemin Chen ◽  
Gangbing Song
Keyword(s):  
Tracking Control ◽  
Testing Device ◽  
Force Tracking

scholarly journals A model reference adaptive variable impedance control method for robot

2021 ◽  
Vol 336 ◽  
pp. 03005
Author(s):  
Xinchao Sun ◽  
Lianyu Zhao ◽  
Zhenzhong Liu
Keyword(s):  
Real Time ◽  
Tracking Control ◽  
Control Method ◽  
Impedance Control ◽  
Tracking Error ◽  
Reference Trajectory ◽  
Control Parameters ◽  
Model Reference ◽  
Force Tracking ◽  
Model Reference Adaptive

As a simple and effective force tracking control method, impedance control is widely used in robot contact operations. The internal control parameters of traditional impedance control are constant and cannot be corrected in real time, which will lead to instability of control system or large force tracking error. Therefore, it is difficult to be applied to the occasions requiring higher force accuracy, such as robotic medical surgery, robotic space operation and so on. To solve this problem, this paper proposes a model reference adaptive variable impedance control method, which can realize force tracking control by adjusting internal impedance control parameters in real time and generating a reference trajectory at the same time. The simulation experiment proves that compared with the traditional impedance control method, this method has faster force tracking speed and smaller force tracking error. It is a better force tracking control method.

Force tracking control for electrohydraulic servo system based on adaptive neuro-fuzzy inference system (ANFIS) controller

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lie Yu ◽  
Lei Ding ◽  
Fangli Yu ◽  
Jianbin Zheng ◽  
Yukang Tian
Keyword(s):  
Tracking Control ◽  
Fuzzy Inference ◽  
Sinusoidal Signal ◽  
Inference System ◽  
Content Type ◽  
Signal Tracking ◽  
Neuro Fuzzy ◽  
Actual Force ◽  
Force Tracking ◽  
Electrohydraulic Servo System

PurposeThe purpose of this paper is to apply a intelligent algorithm to conduct the force tracking control for electrohydraulic servo system (EHSS). Specifically, the adaptive neuro-fuzzy inference system (ANFIS) is selected to improve the control performance for EHSS.Design/methodology/approachTwo types of input–output data were chosen to train the ANFIS models. The inputs are the desired and actual forces, and the output is the current. The first type is to set a sinusoidal signal for the current to produce the actual driving force, and the desired force is chosen as same as the actual force. The other type is to give a sinusoidal signal for the desired force. Under the action of the PI controller, the actual force tracks the desired force, and the current is the output of the PI controller.FindingsThe models built based on the two types of data are separately named as the ANFIS I controller and the ANFIS II controller. The results reveal that the ANFIS I controller possesses the best performance in terms of overshoot, rise time and mean absolute error and show adaptivity to different tracking conditions, including sinusoidal signal tracking and sudden change signal tracking.Originality/valueThis paper is the first time to apply the ANFIS to optimize the force tracking control for EHSS.

Inversion-free force tracking control of piezoelectric actuators using fast finite-time integral terminal sliding-mode

Mechatronics ◽  
2019 ◽  
Vol 57 ◽  
pp. 39-50 ◽  
Author(s):  
Jinoh Lee ◽  
Maolin Jin ◽  
Navvab Kashiri ◽  
Darwin G. Caldwell ◽  
Nikolaos G. Tsagarakis
Keyword(s):  
Finite Time ◽  
Tracking Control ◽  
Sliding Mode ◽  
Piezoelectric Actuators ◽  
Terminal Sliding Mode ◽  
Time Integral ◽  
Force Tracking

scholarly journals Adaptive Vision and Force Tracking Control for Robots With Constraint Uncertainty

2010 ◽  
Vol 15 (3) ◽  
pp. 389-399 ◽  
Author(s):  
Chien Chern Cheah ◽  
Saing Paul Hou ◽  
Yu Zhao ◽  
Jean-Jacques E. Slotine
Keyword(s):  
Tracking Control ◽  
Force Tracking
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