Constant Acceleration Control of Valve-Control-Cylinder System Based on Neural Network

2012 ◽  
Vol 430-432 ◽  
pp. 2041-2045
Author(s):  
Jun Gong Ma ◽  
Xian Yang Shang
Keyword(s):  
Neural Network ◽  
Control System ◽  
Pid Controller ◽  
Mathematic Model ◽  
Neural Element ◽  
Real Time Control ◽  
Time Control ◽  
Set Up ◽  
Valve Control ◽  
Control Cylinder

To solve the serious problem of the nonlinear and Time-varying uncertainty of the valve-control-cylinder system, a control system was designed with neural-proportion-integral-differential (PID) theory. Because of the capacity of neural network, the control system showed adaptive capacity in the system of valve-control-cylinder. In this paper, the basic theory of a single neural element self-adaptive PID controller and a model identifier based on Radial Basis Function were described. The mathematic model of the valve-control-cylinder control system was set up. The simulation results prove that the neural-PID system can regulate the PID parameters dynamically by self-learning so that the system with the neural-PID controller showed quick track performance and capacity against the disturbance. The results also prove the validity and applicability of the system. The algorithm is simple, PID initial parameters are easy to adjust, easy in application of the real-time control the valve-control-cylinder system.

Research on Neural Network Control Technology for Satellite-Based Laser Communication Tracking Turntable

2014 ◽  
Vol 945-949 ◽  
pp. 2266-2271
Author(s):  
Li Hua Wang ◽  
Xiao Qiang Wu
Keyword(s):  
Neural Network ◽  
Control System ◽  
Pid Controller ◽  
Dynamic Performance ◽  
Mathematic Model ◽  
Network Control ◽  
Learning Ability ◽  
Space Environment ◽  
Laser Communication ◽  
Self Learning

In space laser communication tracking turntable work environment characteristics, we design a neural network PID control system which makes the system’s parameter self-tuning. The control system cans self-tune parameters under the changes of the object’ mathematic model, it solves the problem for the control object’s model changes under the space environment. It also looks for method for optimum control through the function of neural network's self-learning in order to solve the problem of the precision’s decline which arouse from vibration and disturbance. The simulation experiments confirmed the self-learning ability of neural network, and described the neural PID controller dynamic performance is superior to the classical PID controller through the output characteristic curves contrast.

scholarly journals Hybrid Multiple Soft-Sensor Models of Grinding Granularity Based on Cuckoo Searching Algorithm and Hysteresis Switching Strategy

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Jie-Sheng Wang ◽  
Na-Na Shen
Keyword(s):  
Neural Network ◽  
Soft Sensor ◽  
Grinding Process ◽  
Real Time Control ◽  
Switching Strategy ◽  
Time Control ◽  
Sensor Model ◽  
Optimum Model ◽  
Set Up ◽  
Sampling Instant

According to the characteristics of grinding process and accuracy requirements of technical indicators, a hybrid multiple soft-sensor modeling method of grinding granularity is proposed based on cuckoo searching (CS) algorithm and hysteresis switching (HS) strategy. Firstly, a mechanism soft-sensor model of grinding granularity is deduced based on the technique characteristics and a lot of experimental data of grinding process. Meanwhile, the BP neural network soft-sensor model and wavelet neural network (WNN) soft-sensor model are set up. Then, the hybrid multiple soft-sensor model based on the hysteresis switching strategy is realized. That is to say, the optimum model is selected as the current predictive model according to the switching performance index at each sampling instant. Finally the cuckoo searching algorithm is adopted to optimize the performance parameters of hysteresis switching strategy. Simulation results show that the proposed model has better generalization results and prediction precision, which can satisfy the real-time control requirements of grinding classification process.

A Study on PID Control and Simulation Based on BP Neural Network

2012 ◽  
Vol 468-471 ◽  
pp. 742-745
Author(s):  
Fang Fang Zhai ◽  
Shao Li Ma ◽  
Wei Liu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Pid Control ◽  
Pid Controller ◽  
Control Method ◽  
Uncertain System ◽  
Real Time Control ◽  
Time Control ◽  
The Neural Network ◽  
Neural Network Pid

This paper introduces the neural network PID control method, in which the parameters of PID controller is adjusted by the use of the self-study ability. And the PID controller can adapt itself actively. The dynamic BP algorithm of the three-layered network realizes the online real-time control, which displays the robustness of the PID control, and the capability of BP neural network to deal with nonlinear and uncertain system. A simulation is made by using of this method. The result of it shows that the neural network PID controller is better than the conventional one, and has higher accuracy and stronger adaptability, which can get the satisfied control result.

Coal Belt Conveyor PID Controller Parameter Regulation with Neural Network

2013 ◽  
Vol 319 ◽  
pp. 583-589 ◽  
Author(s):  
Xian Min Ma ◽  
Xiong Xiong Gao
Keyword(s):  
Neural Network ◽  
Network Theory ◽  
Pid Controller ◽  
Direct Torque Control ◽  
Torque Control ◽  
Belt Conveyor ◽  
Real Time Control ◽  
Time Control ◽  
Working Space ◽  
Neural Network Theory

The coal belt conveyor is an important transport equipment to transfer the coal between the mining working space and the ground. In the traditional belt conveyor control system, there are some disadvantages such as movement quiver with switch relay controller. In this paper, a control strategy using neural network theory to optimize the parameters of the speed PID controller is proposed to overcome the shortcomings, the direct torque control model is adopted to meet real time control requirement, and the optimization steps are described. The theory analysis and simulation results indicate that the system speed overshoot is small after the parameters of the speed PID controller are optimized with neural network theory, so the stability of the coal belt conveyor electrical control driving system is improved.

Pitch Attitude Control Design of Unmanned Aerial Vehicle via Hardware-in-the-Loop Simulation

2013 ◽  
Vol 284-287 ◽  
pp. 2271-2275
Author(s):  
Yun Ping Sun ◽  
Yen Chu Liang
Keyword(s):  
Control System ◽  
Data Acquisition ◽  
Real Time ◽  
Unmanned Aerial Vehicle ◽  
Pid Controller ◽  
Fuzzy Logic Controller ◽  
Hardware In The Loop ◽  
Real Time Control ◽  
Time Control ◽  
Aerial Vehicle

This paper describes an investigative hardware-in-the-loop simulation (HILS) effort through virtual instrumentation on longitudinal control of an unmanned aerial vehicle (UAV). The proportional-integral-differential (PID) controller and fuzzy logic controller (FLC) are designed for the pitch angle hold mode of autopilot; moreover, they are implemented by an embedded real-time control system as a prototype autopilot and tested by hardware-in-the-loop simulation. The hardware configuration of HILS is composed of a personal computer, an embedded real-time control system, several data acquisition devices, servo and sensor unit. The real-time control and data acquisition tasks in HILS is carried out by virtual instruments that is developed by graphical programming language LabVIEW. HILS provides a platform for researchers to correct and improve their design efficiently. The closed-loop performance between PID controller and FLC is evaluated in HILS. The results demonstrate that in the presence of unmodelled dynamics and nonlinear saturation the FLC has an excellent robust performance.

Disturbance learning controller design for unmanned surface vehicle using LSTM technique of recurrent neural network

2021 ◽  
pp. 1-11
Author(s):  
Sang-Ki Jeong ◽  
Dea-Hyeong Ji ◽  
Ji-Youn Oh ◽  
Jung-Min Seo ◽  
Hyeung-Sik Choi
Keyword(s):  
Neural Network ◽  
Control System ◽  
Flow Velocity ◽  
Recurrent Neural Network ◽  
Pid Controller ◽  
Short Term Memory ◽  
Ocean Currents ◽  
Ocean Current ◽  
Movement Speed ◽  
Marine Research

In this study, to effectively control small unmanned surface vehicles (USVs) for marine research, characteristics of ocean current were learned using the long short-term memory (LSTM) model algorithm of a recurrent neural network (RNN), and ocean currents were predicted. Using the results, a study on the control of USVs was conducted. A control system model of a small USV equipped with two rear thrusters and a front thruster arranged horizontally was designed. The system was also designed to determine the output of the controller by predicting the speed of the following currents and utilizing this data as a system disturbance by learning data from ocean currents using the LSTM algorithm of a RNN. To measure ocean currents on the sea when a small USV moves, the speed and direction of the ship’s movement were measured using speed, azimuth, and location (latitude and longitude) data from GPS. In addition, the movement speed of the fluid with flow velocity is measured using the installed flow velocity measurement sensor. Additionally, a control system was designed to control the movement of the USV using an artificial neural network-PID (ANN-PID) controller [12]. The ANN-PID controller can manage disturbances by adjusting the control gain. Based on these studies, the control results were analyzed, and the control algorithm was verified through a simulation of the applied control system [8, 9].

Sign in / Sign up

Export Citation Format

Share Document