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.

Coordinate Signal Control in Urban Traffic of Two-Direction Green Wave Based on Genetic BP Neural Network

2013 ◽  
Vol 823 ◽  
pp. 665-668 ◽  
Author(s):  
Shao Jiao Lv ◽  
Chun Gui Li ◽  
Zhe Ming Li ◽  
Qing Kai Zang
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
Bp Neural Network ◽  
Urban Traffic ◽  
Signal Control ◽  
Traffic Condition ◽  
Real Time Traffic ◽  
Coordinate Control ◽  
Analytical Algorithm ◽  
Trunk Road

To maximize the bandwidth of green wave of trunk road is a main issue in the research of signal control in urban traffic. However, the traditional analytical algorithmcan not be applied in actual traffic widely. A novel dynamic two-direction green wave coordinate control strategy was proposed to overcome the problem. By combining the genetic BP neural network with the traditional analytical algorithm, the urban traffic of two-direction was controlled coordinately online. Finally, an actual example was presented. It shows that not only the green wave bandwidth, the phase difference of each intersection and the critical cycle of trunk road were optimized according to real-time traffic flow, but also our algorithm can be used in different traffic condition by adjusting the parameters of the model.

scholarly journals Research on load simulator control strategy based on BP neural network and PID method

2020 ◽  
Vol 306 ◽  
pp. 03002
Author(s):  
Yong Zhou ◽  
Yubo Zhang ◽  
Tianhao Yang
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
Bp Neural Network ◽  
Pid Control ◽  
Control Method ◽  
Vector Method ◽  
Network Learning ◽  
Learning Speed ◽  
Speed Up ◽  
Load Simulator

In the research of load simulator control method, PID control is the most widely used control strategy, but PID controller’s three parameters is difficult to set. This paper proposes a BP neural network feedforward PID controller system which uses BP neural network for setting these parameters, and in order to make the network learning speed up the convergence speed and not fall into local minimum, the adaptive vector method is adopted to improve the algorithm. The simulation and experimental results show that this method is good at avoiding the primeval shock and the sine tracking performance of the system has also been improved.

Research on the PID Control Strategy in BP Neural Network of Terrain Detector-Milling Cutting Depth System

2014 ◽  
Vol 945-949 ◽  
pp. 1573-1578
Author(s):  
Xiao Feng ◽  
Hao Hu ◽  
Fan Rang Kong ◽  
Shi Qiu ◽  
Ye Sun
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
Bp Neural Network ◽  
Pid Control ◽  
Simulation Analysis ◽  
Dynamic Performance ◽  
Effective Control ◽  
Cutting Depth ◽  
Hydraulic Servo

Targeting at the nonlinear, time-varying characteristics of terrain detector-milling cutting depth electro-hydraulic servo system in soil milling collection machines, this paper proposed the PID control menthod in BP neural network of terrain detector - milling cutting depth system and designed PID controller in BP neural network and conducted simulation analysis by programming with Matlab. The results show that, when compared with conventional PID control, BP neural network compounded with PID control would enable the system better dynamic performance and follow-up characteristics, therefore, it is an effective control strategy.

Design of BP Neural Network Controller for Infrared Seeker Servo System Based on Stribeck Friction Model

2014 ◽  
Vol 615 ◽  
pp. 409-414
Author(s):  
Xin Ying Yan ◽  
Bo Mo ◽  
Ying He
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Dead Zone ◽  
Servo System ◽  
Friction Model ◽  
Guidance System ◽  
Low Speed ◽  
Neural Network Controller ◽  
Infrared Seeker ◽  
Stabilized Platform

The high precision of the seeker is the key to reduce the Miss-Distance and improve precision in the guidance system of missile, and the seeker stabilized platform servo system is safeguard of the overall performance of seeker. So based on the Stribeck friction model, this paper studies and compares the precision of position and velocity that controlled by PID control and BP neural network when the seeker platform working at low speed. Finally, according to the MATLAB simulation results, applying modern control theory as controller based on Stribeck friction model can improve precision and the problem of flat and dead zone at low speed.

scholarly journals Control Strategy with Adaptive Friction Torque Compensation for Radar Servo System

2019 ◽  
Vol 55 (18) ◽  
pp. 187 ◽  
Author(s):  
JIANG Renhua ◽  
LIU Chuang ◽  
NING Yinhang
Keyword(s):  
Control Strategy ◽  
Servo System ◽  
Friction Torque

scholarly journals Fault early warning of pitch system of wind turbine based on GA-BP neural network model

2020 ◽  
Vol 194 ◽  
pp. 03005
Author(s):  
Sihan Chen ◽  
Yongguang Ma ◽  
Liangyu Ma
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Wind Turbine ◽  
Early Warning ◽  
Bp Neural Network ◽  
Sliding Window ◽  
System Model ◽  
Scada System ◽  
Pitch System ◽  
Bp Neural Network Model

A fault early warning method based on genetic algorithm to optimize the BP neural network for the wind turbine pitch system is proposed. According to the parameters monitored by SCADA system, using correlation analysis to screen out the parameters of the pitch system with strong power correlation. The BP neural network optimized by genetic algorithm is used to establish the model of the pitch system under normal working conditions. The verification results show that the input parameters of the pitch system model determined by the correlation coefficient are more reasonable, and the accuracy of the pitch system model established by the genetic algorithm-optimized BP neural network is higher than that of the unoptimized model. Based on the above model, a sliding window model is established, and the early warning threshold is determined through the statistics of the residuals of the sliding window to realize the fault early warning of the pitch system of the wind turbine. The example shows that the method can give early warning in the event of failure, and verifies the effectiveness of the method.

scholarly journals Adaptive Neural Control for Hysteresis Motor Driving Servo System with Bouc-Wen Model

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Xuehui Gao
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
Neural Control ◽  
Servo System ◽  
High Gain ◽  
Tracking Errors ◽  
Vector Error ◽  
Canonical State ◽  
Adaptive Control Strategy ◽  
High Order Neural Network

An adaptive high-order neural network (HONN) control strategy is proposed for a hysteresis motor driving servo system with the Bouc-Wen model. To simplify control design, the model is rewritten as a canonical state space form firstly through coordinate transformation. Then, a high-gain state observer (HGSO) is proposed to estimate the unknown transformed state. Afterward, a filter for the tracking errors is adopted which converts the vector error e into a scalar error s. Finally, an adaptive HONN controller is presented, and a Lyapunov function candidate guarantees that all the closed-loop signals are uniformly ultimately bounded (UUB). Simulations verified the effectiveness of the proposed neural network adaptive control strategy for the hysteresis servo motor system.

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