Fuzzy PID control method of deburring industrial robots

Tao, Yong; Zheng, Jiaqi; Lin, Yang; Wang, Tianmiao; Xiong, Hegen; He, Guo‐Zhong; Xu, Dong

doi:10.3233/ifs-151945

Cited by 27 publications

(10 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Yung Tao et al [14] used the PID approach for each sampling to ensure the consistency of the final transponder route. Testing shows more reliable and effective fuzzy PID performance than the PID power.…”

Section: Literature Reviewmentioning

confidence: 99%

Performance analysis and improvement of robot arms 5DOF using PID and fuzzy controllers: A comparative study

Gouda

said

Elmoushi

et al. 2022

Port-Said Engineering Research Journal

View full text Add to dashboard Cite

Robot modeling is essential before using control systems to verify that the desired task is completed with the lowest feasible error rate according to the inputs. When modeling a robot, the derivation of the forward movement of the robot axes is a fundamental step based on the method of Denavit-Hartenberg. This research aims to control the robot arm motion using two controllers. PID controller was used as a reference to compare the results with those of an FLC. First, an FLC was used to enhance the nonlinearity of the robot arm. FLC was designed based on Mamadani pro-Max inference. Four different defuzzification methods were used and compared to obtain the control signal. These are BOA, MOM, SOM, and COG. The results of this controller were then compared to the PID results on a transient response scale. Finally, simulation was done using MATLAB Simulink software. Based on the simulation results, BOA, MOM, and SOM techniques yield almost identical results; however, the COG strategy yields a broad range of outcomes. Implementing a simple defuzzification approach resulted in system optimization due to the complexity of processes like fuzzification and defuzzification. Based on the simulation results, the FLC system yields better results than those obtained with a PID. The FLC has lower rise time, settling time, steady-state error, and less overshoot than the PID controller.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Performance analysis and improvement of robot arms 5DOF using PID and fuzzy controllers: A comparative study

Gouda

said

Elmoushi

et al. 2022

Port-Said Engineering Research Journal

View full text Add to dashboard Cite

show abstract

“…Nevertheless, this method requires a considerable computational attempt. The FLC rules in the designed FSA for PID were constructed as follows (Tao et al 2015):…”

Section: Cascaded Fuzzy Self-adaptive Pid Control Designmentioning

confidence: 99%

Cascade Control Strategy on Servo Pneumatic System with Fuzzy Self-Adaptive System

Irawan

Azahar

2020

J Control Autom Electr Syst

View full text Add to dashboard Cite

Servo pneumatic position control has gained considerable attention due to its superiority in improving the precision of automation and mechatronic systems. This paper presents the proposed cascaded fuzzy self-adaptive with proportional, integral, and derivative (CFSAPID) control strategy in providing an accurate rod-piston displacement as desired input with stable pressure in chambers. Unlike the conventional cascade strategy, the control law of proposed CFSAPID was designed based on the Mamdani-type fuzzy approach as a dynamic tuner to each stage in the proportional, integral, and derivative (PID) controller that deals with the nonlinearity of servo pneumatics adequately. The bounder sector zone of Mamdani-type fuzzy was determined based on position and velocity states on servo pneumatic rod-piston, and the parameter was constructed to obtain the optimum gain parameters of PID in each stage. The proposed CFSAPID was verified using a pneumatic proportional valve with a double-acting cylinder dynamic model plant. Simulation and analyses were emphasized on steady-state error, difference in pressures of pneumatic cylinder's chambers, hysteresis effect, tuning parameters convergence, performance criteria, and performance indices. The results show that the proposed CFSAPID controller was able to withstand the load disturbances, at variable input trajectory with a stable pressure in chambers at almost minimum hysteresis effect compared to the FSAPID and single PID controllers.

show abstract

“…A sliding mode control method based on radial basis function neural network was proposed for the deburring of industry robotic systems, without the requirements for strict constraints, an accurate model and exact parameters [34]. A fuzzy proportional-integral-derivative (PID) control method for deburring industrial robots was proposed and the PID controller parameters can be updated online at each sampling time to allow adaptive compensation for error and guarantee trajectory accuracy of the end-effector [35]. A vision-based approach, a Pythagorean hodograph quintic spline interpolator based on S curve acceleration/deceleration and an integrated process control structure consisting of an adaptive disturbance compensator, a sliding mode controller, and a friction compensator were investigated for force control and contour following in industrial applications such as deburring [36].…”

Section: Introductionmentioning

confidence: 99%

“…The tool path adaptation for robotic deburring implemented usually needs to supplement extra equipment or processes, e.g., using a vision system described in [27,36] or direct teaching [29]. Other studies on the process parameter control for robotic deburring have certain constraints, e.g., the deburring tool is an abrasive diamond disc in the control strategy for the process parameter control, and other deburring tools are not considered [30]; the designed control action and implementation are more intricate, such as [25,31,32,34,36]; the procedure of the proposed approach is more complicated, such as [33]; or detailed deburring process parameters such as robotic feed and spindle speed for the deburring industrial robot are not considered [35].…”

Section: Introductionmentioning

confidence: 99%

A Robotic Deburring Methodology for Tool Path Planning and Process Parameter Control of a Five-Degree-of-Freedom Robot Manipulator

Guo

Zhu

et al. 2019

Applied Sciences

View full text Add to dashboard Cite

Industrial robotics is a continuously developing domain, as industrial robots have demonstrated to possess benefits with regard to robotic automation solutions in the industrial automation field. In this article, a new robotic deburring methodology for tool path planning and process parameter control is presented for a newly developed five-degree-of-freedom hybrid robot manipulator. A hybrid robot manipulator with dexterous manipulation and two experimental platforms of robot manipulators are presented. A robotic deburring tool path planning method is proposed for the robotic deburring tool position and orientation planning and the robotic layered deburring planning. Also, a robotic deburring process parameter control method is proposed based on fuzzy control. Furthermore, a dexterous manipulation verification experiment is conducted to demonstrate the dexterous manipulation and the orientation reachability of the robot manipulator. Additionally, two robotic deburring experiments are conducted to verify the effectiveness of the two proposed methods and demonstrate the highly efficient and dexterous manipulation and deburring capacity of the robot manipulator.

show abstract

Fuzzy PID control method of deburring industrial robots

Cited by 27 publications

References 19 publications

Performance analysis and improvement of robot arms 5DOF using PID and fuzzy controllers: A comparative study

Performance analysis and improvement of robot arms 5DOF using PID and fuzzy controllers: A comparative study

Cascade Control Strategy on Servo Pneumatic System with Fuzzy Self-Adaptive System

A Robotic Deburring Methodology for Tool Path Planning and Process Parameter Control of a Five-Degree-of-Freedom Robot Manipulator

Contact Info

Product

Resources

About