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.

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.

The Study of BP Neural Network PID Control Based on the Cutting Chatter Suppression System

2014 ◽  
Vol 1030-1032 ◽  
pp. 1574-1577
Author(s):  
Dao Kun Zhang ◽  
Rui Huo ◽  
Shu Ying Li ◽  
Xing Ke Cui ◽  
Cui Ping Liu
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
Bp Neural Network ◽  
Pid Control ◽  
Simulated Data ◽  
Chatter Suppression ◽  
Cutting Chatter ◽  
Adaptive Ability ◽  
Neural Network Pid ◽  
Suppression System

The intelligent control strategy of BP neural combined network with classical PID control is mainly studied and simulated. The advantages of the control strategy are discussed. Based on the simulated data, the BP neural network PID control has the stronger adaptive ability.

Research on Control Strategy of Automatic Control System for Sewage Treatment

2013 ◽  
Vol 791-793 ◽  
pp. 690-693
Author(s):  
Zhang Hong ◽  
Xiao Liang Liu ◽  
Fang Wei
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
Bp Neural Network ◽  
Pid Control ◽  
Pid Controller ◽  
Treatment Process ◽  
Sewage Treatment ◽  
Bp Algorithm ◽  
Dissolved Oxygen Concentration ◽  
Control Effect

For the characteristics of the sewage treatment process and a combination of BP algorithm and conventional PID control, a PID controller is proposed based on BP neural network to realize the online adjustment of PID controller parameters. This control strategy will be applied to the control of the DO(Dissolved Oxygen) concentration in sewage treatment, and a contrast has been made with conventional PID control effect.

scholarly journals A Novel PID Control Strategy Based on Improved GA-BP Neural Network for Phase-Shifted Full-Bridge Current-Doubler Synchronous Rectifying Converter

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Hemiao Liu ◽  
Yanming Cheng ◽  
Yulian Zhao ◽  
Mahmoud Al Shurafa ◽  
Jing Wu ◽  
...  
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
Bp Neural Network ◽  
Pid Control ◽  
Network Control ◽  
Maximum Efficiency ◽  
Neural Network Control ◽  
Control Performance ◽  
Wide Range ◽  
Neural Network Pid

In this paper, a phase-shifted full-bridge current-doubler synchronous rectifying converter (PSFB-CDSRC) based on IGBT and its control strategies are studied. In the main circuit, a current doubling synchronous rectifying circuit based on IGBT is presented to further reduce the power loss of power devices. Moreover, in the control strategy, in view of the existing researches, the basic BP neural network PID control performance of the rectifying converter still can be further improved. Therefore, this paper combines the quasi-Newton algorithm and traditional GA to propose an improved GA-BP (IGA-BP) neural network to further improve PID control performance. The simulation results demonstrate that the maximum efficiency of 5 V/500 A rectifying converter based on the proposed circuit scheme can reach 94.1%, and the rectifying converter has a good performance of excellent waveform and wide range of load. IGA-BP neural network PID control responds fast and reaches the stable state quickly in comparison with that controlled by the GA-BP neural network control strategy, and the steady-state time can be reduced by 10.5% through using IGA-BP neural network control strategy. This study can provide a valuable guidance and reference, not only in circuit scheme but also in the optimal PID control strategy for design of the high-efficiency DC/DC rectifying converter with higher power in the future.

Speed-Regulation System of PMLSM Based on BP Neural Network PID Control

2012 ◽  
Vol 466-467 ◽  
pp. 1217-1221 ◽  
Author(s):  
Fu Zhong Wang ◽  
Qiong Xia Yu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Pid Control ◽  
Dynamic Performance ◽  
Regulation System ◽  
Time Varying ◽  
Bp Network ◽  
Speed Controller ◽  
Speed Regulation ◽  
Neural Network Pid

For the characteristics of permanent magnet linear synchronous motor (PMLSM) hoisting system’s nonlinear, time-varying and vulnerable to disturbance, based on the established PMLSM d-q axis dynamic model, designed of an improved BP neural network PID speed controller. Modified the fixed learning rate in BP neural network to adaptive adjustable, and added the momentum to reduce the oscillation tendency in the learning process, greatly improved the convergence speed and avoided the network into a local minimum. Compared with the simulations of traditional PID and the improved BP network PID speed controller, the results showed that the improved BP neural network PID speed controller had the high quality and it can make the system with better dynamic performance and robustness.

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.

scholarly journals An Improved PID Controller for the Compliant Constant-Force Actuator Based on BP Neural Network and Smith Predictor

2021 ◽  
Vol 11 (6) ◽  
pp. 2685
Author(s):  
Guojin Pei ◽  
Ming Yu ◽  
Yaohui Xu ◽  
Cui Ma ◽  
Houhu Lai ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Pid Control ◽  
Pid Controller ◽  
Experimental Validation ◽  
Control Method ◽  
Smith Predictor ◽  
Constant Force ◽  
Matlab Simulation ◽  
Adjustment Time

A compliant constant-force actuator based on the cylinder is an important tool for the contact operation of robots. Due to the nonlinearity and time delay of the pneumatic system, the traditional proportional–integral–derivative (PID) method for constant force control does not work so well. In this paper, an improved PID control method combining a backpropagation (BP) neural network and the Smith predictor is proposed. Through MATLAB simulation and experimental validation, the results show that the proposed method can shorten the maximum overshoot and the adjustment time compared with traditional the PID method.

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