Research on Robot Grinding Force Control Method

2021 ◽  
pp. 821-829
Author(s):  
MingJian Sun ◽  
Kai Guo ◽  
Jie Sun
Keyword(s):  
Force Control ◽  
Control Method ◽  
Grinding Force

scholarly journals An Adaptive Sliding-Mode Iterative Constant-force Control Method for Robotic Belt Grinding Based on a One-Dimensional Force Sensor

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1635 ◽  
Author(s):  
Tie Zhang ◽  
Ye Yu ◽  
Yanbiao Zou
Keyword(s):  
Force Control ◽  
Control Method ◽  
Sliding Mode ◽  
Force Sensor ◽  
Grinding Force ◽  
One Dimension ◽  
Constant Force ◽  
Belt Grinding ◽  
Robotic Belt Grinding ◽  
The Relationship

To improve the processing quality and efficiency of robotic belt grinding, an adaptive sliding-mode iterative constant-force control method for a 6-DOF robotic belt grinding platform is proposed based on a one-dimension force sensor. In the investigation, first, the relationship between the normal and the tangential forces of the grinding contact force is revealed, and a simplified grinding force mapping relationship is presented for the application to one-dimension force sensors. Next, the relationship between the deformation and the grinding depth during the grinding is discussed, and a deformation-based dynamic model describing robotic belt grinding is established. Then, aiming at an application scene of robot belt grinding, an adaptive iterative learning method is put forward, which is combined with sliding mode control to overcome the uncertainty of the grinding force and improve the stability of the control system. Finally, some experiments were carried out and the results show that, after ten times iterations, the grinding force fluctuation becomes less than 2N, the mean value, standard deviation and variance of the grinding force error’s absolute value all significantly decrease, and that the surface quality of the machined parts significantly improves. All these demonstrate that the proposed force control method is effective and that the proposed algorithm is fast in convergence and strong in adaptability.

Design and Simulation of Grinding Force Control System for Wafer Self-Rotating Grinding

2011 ◽  
Vol 403-408 ◽  
pp. 762-766
Author(s):  
Jia Ping Yu ◽  
Xin Wei ◽  
Zhuo Chen ◽  
Pei Yong Lin
Keyword(s):  
Control System ◽  
Pid Control ◽  
Force Control ◽  
Control Method ◽  
Grinding Force ◽  
Fuzzy Pid ◽  
Fuzzy Pid Control ◽  
Modeling Simulation ◽  
Force Control System ◽  
Simulation And Experiment

According to the features of the self–rotating grinding, the real-time grinding force control system was designed. The Fuzzy-PID control method has been proved to be the most suitable control method and fulfill the system’s needs through the modeling, simulation and experiment of the system in this paper.

Limitations of an Indentation Model for Grinding Gamma Titanium Aluminide

2002 ◽  
Author(s):  
H. Ali Razavi ◽  
Steven Danyluk ◽  
Thomas R. Kurfess
Keyword(s):  
Force Control ◽  
Titanium Aluminide ◽  
Normal Force ◽  
Removal Rate ◽  
Cubic Boron Nitride ◽  
Normal Component ◽  
Grinding Force ◽  
Cutting Depth ◽  
Gamma Titanium Aluminide ◽  
Gamma Titanium

This paper explores the limitations of a previously reported indentation model that correlated the depth of plastic deformation and the normal component of the grinding force. The indentation model for grinding is studied using force control grinding of gamma titanium aluminide (TiAl-γ). Reciprocating surface grinding is carried out for a range of normal force 15–90 N, a cutting depth of 20–40 μm and removal rate of 1–9 mm3/sec using diamond, cubic boron nitride (CBN) and aluminum oxide (Al2O3) abrasives. The experimental data show that the indentation model for grinding is a valid approximation when the normal component of grinding force exceeds some value that is abrasive dependent.

Constant force control for aluminum wheel hub grinding based on ESO + backstepping

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shijie Dai ◽  
Yufeng Zhao ◽  
Wenbin Ji ◽  
Jiaheng Mu ◽  
Fengbao Hu
Keyword(s):  
Control Strategy ◽  
Force Control ◽  
Control Method ◽  
Response Speed ◽  
Backstepping Control ◽  
Constant Force ◽  
Content Type ◽  
Disturbance Rejection Control ◽  
The Stability ◽  
Differential Control

Purpose This paper aims to present a control method to realize the constant force grinding of automobile wheel hub. Design/methodology/approach A constant force control strategy combined by extended state observer (ESO) and backstepping control is proposed. ESO is used to estimate the total disturbance to improve the anti-interference and stability of the system and Backstepping control is used to improve the response speed of the system. Findings The simulation and grinding experimental results show that, compared with the proportional integral differential control and active disturbance rejection control, the designed controller can improve the dynamic response performance and anti-interference ability of the system and can quickly track the expected force and improve the grinding quality of the hub surface. Originality/value The main contribution of this paper lies in the proposed of a new constant force control strategy, which significantly improved the stability and precision of grinding force.

A novel adaptive force control method for IPMC manipulation

2012 ◽  
Vol 21 (7) ◽  
pp. 075016 ◽  
Author(s):  
Lina Hao ◽  
Zhiyong Sun ◽  
Zhi Li ◽  
Yunquan Su ◽  
Jianchao Gao
Keyword(s):  
Force Control ◽  
Control Method ◽  
Adaptive Force Control

EXPERIMENTAL VERIFICATION OF BOUNDED-FORCE CONTROL METHOD

1996 ◽  
Vol 25 (2) ◽  
pp. 179-193 ◽  
Author(s):  
B. INDRAWAN ◽  
T. KOBORI ◽  
M. SAKAMOTO ◽  
N. KOSHIKA ◽  
S. OHRUI
Keyword(s):  
Experimental Verification ◽  
Force Control ◽  
Control Method

Hardware-in-the-Loop Simulation for Active Force Control with Iterative Learning Applied to an Active Vehicle Suspension System

2013 ◽  
Vol 465-466 ◽  
pp. 801-805
Author(s):  
Rosmazi Rosli ◽  
Musa Mailah ◽  
Gigih Priyandoko
Keyword(s):  
Force Control ◽  
Control Method ◽  
Learning Algorithm ◽  
Suspension System ◽  
Iterative Learning ◽  
Practical Implementation ◽  
Hardware In The Loop ◽  
Active Force ◽  
Physical Test ◽  
Active Force Control

The paper focuses on the practical implementation of a novel control method to an automotive suspension system using active force control (AFC) with iterative learning algorithm (ILA) and proportional-integral-derivative (PID) control strategy. The overall control system to be known as AFC-IL scheme essentially comprises three feedback control loops to cater for a number of specific tasks, namely, the innermost loop for the force tracking of the pneumatic actuator using PI controller, intermediate loops applying AFC with ILA strategy for the compensation of the disturbances and the outermost loop using PID controller for the computation of the desired force. A number of experiments were carried out on a physical test rig with hardware-in-the-loop simulation (HILS) feature that fully incorporates the theoretical elements. The performance of the proposed control method was evaluated and benchmarked to examine the effectiveness of the system in suppressing the vibration effect of the suspension system. It was found that the experimental results demonstrate the superiority of the active suspension system with proposed AFC-IL scheme compared to the PID and passive counterparts.

Simulations and Experiments on the Force Control of Hydraulic Servo System for Hydraulic Robots

2016 ◽  
Vol 826 ◽  
pp. 128-133 ◽  
Author(s):  
Hyo Gon Kim ◽  
Jong Won Lee ◽  
Yong Ho Choi ◽  
Jeong Woo Park ◽  
Jin Ho Suh
Keyword(s):  
Force Control ◽  
High Performance ◽  
Control Method ◽  
Servo System ◽  
Force Density ◽  
Hydraulic Actuator ◽  
Underwater Robots ◽  
Heavy Load ◽  
Hydraulic Servo ◽  
Hydraulic Servo System

Because hydraulic actuator has higher power and force density, it is normally used in heavy load manipulator robots and industrial equipment which require high torque. Also, the hydraulic actuator is applied to underwater robots that need high performance maneuver in underwater operations. The force control has benefits to those kind of robots to ensure compliance with user or environment. However, the hydraulic actuator is difficult to control forces due to the non-linearity characteristic of the hydraulic servo system. In this paper, we propose a force control method with compensation of force derivative and natural velocity feedback. We also describe a method of applying it to the real system. In order to evaluate the effect of the proposed control method, the simulations and experiments were performed.

The Adaptive Control of Grinding Force of CNC Cam Grinder Based on Neural Network

2012 ◽  
Vol 217-219 ◽  
pp. 2051-2055
Author(s):  
Ming Li Xie ◽  
Ling Lu
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Control Method ◽  
Metal Removal ◽  
Removal Rate ◽  
Grinding Force ◽  
Control Measures ◽  
Numerical Control ◽  
Grinding Wheel ◽  
Grinding Process

In the process of cam grinding, the fluctuation of grinding force can lead to the abnormal wear of the grinding wheel, the decrease of the grinding surface quality and even the damage of the grinding process system. The paper took the grinding process of numerical control cam grinding machine as research subject, the grinding force mathematical model was built, the indirect test and control measures were researched and an adaptive control method based on neural network was proposed and applied to the grinding force control of the cam grinding process. At last, the controller was designed and the grinding simulation was performed with MATLAB, which proved that the system could solve the fluctuation of grinding force during the process of cam grinding and the controller was equipped with good dynamic characteristic. The results indicate that the method can realize the purpose of optimal metal removal rate and enhance the grinding quality of cams.

A Novel Force Control Method for Quasi-Static Underactuated Multibody Systems

2009 ◽  
Vol 23 (10) ◽  
pp. 1249-1260 ◽  
Author(s):  
Yongan Huang ◽  
Zhouping Yin ◽  
Xiangtao Hu ◽  
Youlun Xiong
Keyword(s):  
Force Control ◽  
Multibody Systems ◽  
Control Method
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