scholarly journals Control Method of Flexible Manipulator Servo System Based on a Combination of RBF Neural Network and Pole Placement Strategy

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 896
Author(s):  
Dongyang Shang ◽  
Xiaopeng Li ◽  
Meng Yin ◽  
Fanjie Li
Keyword(s):  
Neural Network ◽  
Rotation Angle ◽  
Control Method ◽  
Rbf Neural Network ◽  
Servo System ◽  
Flexible Manipulator ◽  
Pole Placement ◽  
Flexible Manipulators ◽  
Angle Error ◽  
Assumed Mode Method

Gravity and flexibility will cause fluctuations of the rotation angle in the servo system for flexible manipulators. The fluctuation will seriously affect the motion accuracy of end-effectors. Therefore, this paper adopts a control method combining the RBF (Radial Basis Function) neural network and pole placement strategy to suppress the rotation angle fluctuations. The RBF neural network is used to identify uncertain items caused by the manipulator’s flexibility and the time-varying characteristics of dynamic parameters. Besides, the pole placement strategy is used to optimize the PD (Proportional Differential) controller’s parameters to improve the response speed and stability. Firstly, a dynamic model of flexible manipulators considering gravity is established based on the assumed mode method and Lagrange’s principle. Then, the system’s control characteristics are analyzed, and the pole placement strategy optimizes the parameters of the PD controllers. Next, the control method based on the RBF neural network is proposed, and the Lyapunov stability theory demonstrates stability. Finally, numerical analysis and control experiments prove the effectiveness of the control method proposed in this paper. The means and standard deviations of rotation angle error are reduced by the control method. The results show that the control method can effectively reduce the rotation angle error and improve motion 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 Adaptive RBF Neural Network Control Method for Pneumatic Position Servo System

2020 ◽  
Vol 53 (2) ◽  
pp. 8826-8831
Author(s):  
Hai-Peng Ren ◽  
Shan-Shan Jiao ◽  
Xuan Wang ◽  
Jie Li
Keyword(s):  
Neural Network ◽  
Control Method ◽  
Rbf Neural Network ◽  
Servo System ◽  
Network Control ◽  
Neural Network Control ◽  
Position Servo

Application of PID Control Based on RBF Neural Network in Electro-Hydraulic Servo System for Steel Strip Deviation

2012 ◽  
Vol 468-471 ◽  
pp. 434-438
Author(s):  
Xue Qin Kou ◽  
Li Chen Gu
Keyword(s):  
Neural Network ◽  
Pid Control ◽  
Control Method ◽  
Rbf Neural Network ◽  
Steel Strip ◽  
Dynamic Performance ◽  
Servo System ◽  
Pid Algorithm ◽  
Hydraulic Servo ◽  
Hydraulic Servo System

The working principle of the electrical-hydraulic servo system for steel strip deviation is introduced. The mathematical model of the system is established, and the performance indexes in time domain are analyzed with MATLAB. Aiming at the steel strip deviation, an improved PID control method based on RBF neural network is proposed. According to Jacobian information identification of RBF neural network combined with incremental PID algorithm, the self-tuning of parameters is implemented so that the performance of the system can achieve the designed requirements. Compared with traditional PID control, the simulation results show RBF- PID control has better dynamic performance and stabilization.

Control Method for Flexible Joints in Manipulator Based on BP Neural Network Tuning PI Controller

Mathematics ◽  
2021 ◽  
Vol 9 (23) ◽  
pp. 3146
Author(s):  
Hexu Yang ◽  
Xiaopeng Li ◽  
Jinchi Xu ◽  
Dongyang Shang ◽  
Xingchao Qu
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
Bp Neural Network ◽  
Servo System ◽  
Pole Assignment ◽  
Pi Controller ◽  
Friction Torque ◽  
System Model ◽  
Angle Error ◽  
Assignment Method

With the development of robot technology, integrated joints with small volume and convenient installation have been widely used. Based on the double inertia system, an integrated joint motor servo system model considering gear angle error and friction interference is established, and a joint control strategy based on BP neural network and pole assignment method is designed to suppress the vibration of the system. Firstly, the dynamic equation of a planetary gear system is derived based on the Lagrange method, and the gear vibration of angular displacement is calculated. Secondly, the vibration displacement of the sun gear is introduced into the motor servo system in the form of the gear angle error, and the double inertia system model including angle error and friction torque is established. Then, the PI controller parameters are determined by pole assignment method, and the PI parameters are adjusted in real time based on the BP neural network, which effectively suppresses the vibration of the system. Finally, the effects of friction torque, pole damping coefficient and control strategy on the system response and the effectiveness of vibration suppression are analyzed.

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 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-based adaptive fault-tolerant vibration control of single-link flexible manipulator

2019 ◽  
Vol 42 (3) ◽  
pp. 430-438 ◽  
Author(s):  
Le Li ◽  
Jinkun Liu
Keyword(s):  
Neural Network ◽  
Dynamic Model ◽  
Fault Tolerant ◽  
Rbf Neural Network ◽  
Direct Method ◽  
Fault Tolerant Control ◽  
Flexible Manipulator ◽  
Actuator Failure ◽  
Single Link ◽  
Boundary Disturbance

This paper proposes an adaptive fault-tolerant control scheme for a single-link flexible manipulator with actuator failure and uncertain boundary disturbance. The dynamic model of the flexible manipulator as-described by partial differential equations (PDEs) is derived under Hamilton’s principle. The dynamic model is then used to design an adaptive fault-tolerant control (FTC) scheme which tracks the given angle and regulates vibration in the case of actuator failure. The boundary disturbance is compensated by a radial basis function (RBF) neural network. The whole closed-loop system is proven asymptotically stable by Lyapunov direct method and LaSalle’s invariance principle. Simulation results indicate that the proposed controller is superior to the traditional PD controller.

Design of Neural Network Controller for Space Robot with Flexible Manipulators

2013 ◽  
Vol 394 ◽  
pp. 393-397
Author(s):  
Jing Ma ◽  
Wen Hui Zhang ◽  
Zhi Hua Zhu
Keyword(s):  
Neural Network ◽  
Pid Control ◽  
Control Method ◽  
System Optimization ◽  
Space Robot ◽  
Learning Ability ◽  
Flexible Manipulators ◽  
Neural Network Controller ◽  
Non Linear System ◽  
Self Learning

Neural network self-learning optimization PID control algorithm is put forward for free-floating space robot with flexible manipulators. Firstly, dynamics model of space flexible robot is established, then, neural network with good learning ability is used to approach non-linear system. Optimization algorithm of network weights is designed to speed up the learning speed and the adjustment velocity. Error function is offered by PID controller. The neural network self-learning PID control method can improve the control precision.

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