scholarly journals Design of Contour Error Coupling Controller Based on Neural Network Friction Compensation

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Sanxiu Wang ◽  
Guang Chen ◽  
Yueli Cui
Keyword(s):  
Neural Network ◽  
Control Method ◽  
Rbf Neural Network ◽  
Friction Model ◽  
Contour Error ◽  
Friction Compensation ◽  
Contour Control ◽  
Lugre Friction ◽  
Contour Error Control ◽  
Control Accuracy

In two-axis servo contour motion control, friction and various uncertainties unavoidably exist, reducing the contour control accuracy. This paper proposes a neural network contour error coupling control method based on LuGre friction compensation, which includes a contour error calculation model, single-axis computed torque controller (CTC), and neural network friction compensation controller. The LuGre friction model can describe servo system’s complicated static and dynamic friction characteristics, and the RBF neural network has a universal approximation property to realize compensation control of friction. Simulation results indicate that the proposed contour error control method can effectively compensate for the effect of friction and improve contour control accuracy.

scholarly journals Contour control of biaxial motion system based on RBF neural network and disturbance observer

2021 ◽  
Vol 13 (8) ◽  
pp. 168781402110348
Author(s):  
Sanxiu Wang ◽  
Shengtao Jiang
Keyword(s):  
Neural Network ◽  
Feedback Linearization ◽  
Disturbance Observer ◽  
Control Method ◽  
Rbf Neural Network ◽  
Servo System ◽  
Observation Error ◽  
Tracking Accuracy ◽  
Motion System ◽  
Contour Control

Friction is the main factor which degrades the control precisions of the servo system. In this paper, a cross coupled control method based on RBF neural network and disturbance observer is proposed for multi-axis servo system with LuGre friction, in order to implement high precision tracking and contouring control. Firstly, a feedback linearization controller is designed to realize the position stable tracking for single-axis motion; then, the disturbance observer is used to observe and compensate the friction. However, in practical application, the observation gain is difficult to select, and it is easy to cause observation error. In order to enhance the tracking accuracy and system robustness, the RBF neural network is introduced to approximate the disturbance observation error online. Finally, the cross coupled control is used to coordinate the motion between the axes to improve the contour accuracy. The simulation results show that the proposed method can effectively compensate the influence of friction on the system, has good tracking accuracy and high contour control precision.

scholarly journals Research on Adaptive Friction Compensation of Digital Hydraulic Cylinder Based on LuGre Friction Model

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Shouling Jiang ◽  
Kun Zhang ◽  
Hui Wang ◽  
Donghu Zhong ◽  
Jinpeng Su ◽  
...  
Keyword(s):  
Control Method ◽  
Dead Zone ◽  
Hydraulic Cylinder ◽  
Friction Model ◽  
Friction Compensation ◽  
State Variables ◽  
Lugre Friction Model ◽  
Overall Performance ◽  
Lugre Friction ◽  
Adaptive Ability

This paper aims to eliminate nonlinear friction from the performance of the digital hydraulic cylinder to enable it to have good adaptive ability. First, a mathematical model of a digital hydraulic cylinder based on the LuGre friction model was established, and then a dual-observer structure was designed to estimate the unobservable state variables in the friction model. The Lyapunov method is used to prove the global asymptotic stability of the closed-loop system using the adaptive friction compensation method. Finally, Simulink is used to simulate the system performance. The simulation results indicate that the addition of adaptive friction compensation control can effectively reduce system static error, suppress system limit loop oscillation, “position decapitation,” “speed dead zone,” and low-speed creep phenomena, and improve the overall performance of the digital hydraulic cylinder. The control method has practical application value for improving the performance index of the digital hydraulic cylinder.

scholarly journals Design of a Predictive RBF Compensation Fuzzy PID Controller for 3D Laser Scanning System

2020 ◽  
Vol 10 (13) ◽  
pp. 4662 ◽  
Author(s):  
Minghui Zhao ◽  
Xiaobin Xu ◽  
Hao Yang ◽  
Zhijie Pan
Keyword(s):  
Neural Network ◽  
Laser Scanning ◽  
Control Method ◽  
Rbf Neural Network ◽  
Step Response ◽  
Scanning System ◽  
3D Laser Scanning ◽  
Pitching Motion ◽  
Fuzzy Algorithm ◽  
Laser Scanning System

A new proportional integral derivative (PID) control method is proposed for the 3D laser scanning system converted from 2D Lidar with a pitching motion device. It combines the advantages of a fuzzy algorithm, a radial basis function (RBF) neural network and a predictive algorithm to control the pitching motion of 2D Lidar quickly and accurately. The proposed method adopts the RBF neural network and feedback compensation to eliminate the unknown nonlinear part in the Lidar pitching motion, adaptively adjusting the PID parameter by a fuzzy algorithm. Then, the predictive control algorithm is adopted to optimize the overall controller output in real time. Finally, the simulation results show that the step response time of the Lidar pitching motion system using the control method is reduced from 15.298 s to 1.957 s with a steady-state error of 0.07°. Meanwhile, the system still has favorable response performance for the sinusoidal and step inputs under model mismatch and large disturbance. Therefore, the control method proposed above can improve the system performance and control the pitching motion of the 2D Lidar effectively.

scholarly journals Study on the Low Velocity Stability of a Prostate Seed Implantation Robot’s Rotatory Joint

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 284 ◽  
Author(s):  
Bing Li ◽  
Yongde Zhang ◽  
Lipeng Yuan ◽  
Xiaolin Xi
Keyword(s):  
Prostate Cancer ◽  
Parameter Identification ◽  
Control Method ◽  
Friction Model ◽  
Friction Torque ◽  
Low Velocity ◽  
Seed Implantation ◽  
Compensation Control ◽  
Lugre Friction Model ◽  
Lugre Friction

Prostate cancer has one of the highest incidences of male malignant tumors worldwide. Its treatment involves the robotic implantation of radioactive seeds in the perineum, a safe and effective procedure for early, low-risk prostate cancer. In order to ensure precise positioning, the seed implantation needle is set at low terminal velocity. In this paper, the motion output position instability caused by the friction torque of the robot’s motor and rotating joint during low velocity motion was analyzed and studied. This paper also presents a compensation control method based on the LuGre friction model, which offers piecewise parameter identification with GA-PSO. First, based on an analysis of its structure and working principle, the friction torque model of the robotic system and the torque model of the driving motor are established, and the influence of friction torque on motion stability analyzed. Then, based on experimental data of the relationship between velocity and friction torque for no-friction compensation, the velocity point of the minimum torque of the rotating joint and the critical Stribeck velocity point were used for segmental parameter identification; cubic spline interpolation was used for segmental fitting. Furthermore, on the basis of the LuGre model identification method, parameter identification of the genetic algorithm-particle swarm optimization, and compensation control of the LuGre friction model, a control method is analysed and set forth. Malab2017a/Simulink simulation software was used to simulate and analyze the control method, and verify its feasibility. Finally, the cantilever prostate seed implantation robot system was tested to verify the effectiveness of the segmented identification method and the compensation control strategy. The results reveal that motion output position stability at low velocity meets the requirements of the cantilever prostate seed implantation robot, thus providing a vital reference for further research.

scholarly journals Adaptive PD Control Based on RBF Neural Network for a Wire-Driven Parallel Robot and Prototype Experiments

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Yuqi Wang ◽  
Qi Lin ◽  
Xiaoguang Wang ◽  
Fangui Zhou
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Control Method ◽  
Rbf Neural Network ◽  
Approximation Error ◽  
Wind Tunnel Test ◽  
Parallel Robot ◽  
Network Control ◽  
Pd Control ◽  
Control Scheme

An adaptive PD control scheme is proposed for the support system of a wire-driven parallel robot (WDPR) used in a wind tunnel test. The control scheme combines a PD control and an adaptive control based on a radial basis function (RBF) neural network. The PD control is used to track the trajectory of the end effector of the WDPR. The experimental environment, the external disturbances, and other factors result in uncertainties of some parameters for the WDPR; therefore, the RBF neural network control method is used to approximate the parameters. An adaptive control algorithm is developed to reduce the approximation error and improve the robustness and control precision of the WDPR. It is demonstrated that the closed-loop system is stable based on the Lyapunov stability theory. The simulation results show that the proposed control scheme results in a good performance of the WDPR. The experimental results of the prototype experiments show that the WDPR operates on the desired trajectory; the proposed control method is correct and effective, and the experimental error is small and meets the requirements.

Neural Network Trajectory Tracking Control of Compliant Parallel Robot

2015 ◽  
Vol 799-800 ◽  
pp. 1069-1073
Author(s):  
Hao Tian ◽  
Yue Qing Yu
Keyword(s):  
Neural Network ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Control Method ◽  
Rbf Neural Network ◽  
Control Strategies ◽  
Parallel Robot ◽  
Network Control ◽  
Trajectory Tracking Control ◽  
Compliant Joint

Trajectory tracking control of compliant parallel robot is presented. According to the characteristics of compliant joint, the system model is derived and the dynamic equation is obtained based on the Lagrange method. Radial Basis Function (RBF) neural network control is designed to globally approximate the model uncertainties. Further, an itemized approximate RBF control method is proposed for higher identify precision. The trajectory tracking abilities of two control strategies are compared through simulation.

scholarly journals Model Free Adaptive Control of Switched Reluctance Motor for Electric Vehicle

2021 ◽  
pp. 104-114
Author(s):  
Xifeng Mi , Yuanyuan Fan
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
Control Method ◽  
Rbf Neural Network ◽  
Switched Reluctance Motor ◽  
Torque Ripple ◽  
Torque Distribution ◽  
Switched Reluctance ◽  
Model Free ◽  
Reluctance Motor

In this paper, the model free adaptive control method of switched reluctance motor for electric vehicle is studied. Based on the torque distribution control of SRM, a SRM control strategy based on torque current hybrid model based on RBF neural network is proposed in this paper. Based on the deviation between the dynamic average value and instantaneous value of SRM output torque, the online learning of RBF neural network is realized. At the same time, this paper constructs a torque current hybrid model, obtains the current variation law of SRM under low torque ripple operation, and reduces the torque ripple of SRM. The SRM torque distribution control is realized on the SRM experimental platform. Compared with the voltage chopper control method, the experimental results show that the torque ripple of SRM can be reduced by adopting the torque distribution control strategy.

Applied of RBF Neural Network in Pressure and Flow Control System

2015 ◽  
Vol 713-715 ◽  
pp. 909-914
Author(s):  
Fei Wang ◽  
Xiao Hua Sun ◽  
Zhi Jun Zou ◽  
Hao Li
Keyword(s):  
Neural Network ◽  
Flow Control ◽  
Control Method ◽  
Rbf Neural Network ◽  
Control Process ◽  
Mathematic Model ◽  
Pressure Control ◽  
Water Pipe ◽  
Object A ◽  
Pid Algorithm

This paper is addressed water pipe pressure control method, chose a water pipe with bypass valve as an object. The paper made the pressure and flow control of the object a mathematic model. This paper design a PID control algorithm which base on RBF neural network to control the pressure and flow of the object, and simulate the control process both RBF neural network PID algorithm and PID algorithm. The last part of the paper contrasts the two simulation result.

Sign in / Sign up

Export Citation Format

Share Document