Automatic Navigation of Intelligent Vehicle Control System Based on Laser Sensor

2014 ◽  
Vol 1049-1050 ◽  
pp. 661-664
Author(s):  
Yong Qi Han ◽  
Yi Liu ◽  
Nan Lin
Keyword(s):  
Control System ◽  
Movement Speed ◽  
Laser Sensor ◽  
Complete Control ◽  
Loop Control ◽  
Feature Extraction Method ◽  
Pid Algorithm ◽  
Automatic Navigation ◽  
Photoelectric Encoder ◽  
Fuzzy Control Algorithm

This smart car control system by MC9S12G128 controller as the core, the use of a row of 18 laser tubes and 6 receiving tube as the path recognition sensor, through MC9S12G128 I / 0 interface for sensor signal, and use the feature extraction method to extract the track type, realization of the starting line, cross line recognition. According to road information, the design adopts fuzzy control algorithm of output PWM wave to control the gear steering, complete control of the direction of the car, the car speed extracted with the use of photoelectric encoder, comparing with the current set point to get speed deviation, and output PWM pulse is obtained by using the PID algorithm to control the motor to adjust the car speed, complete the closed-loop control of the car movement speed. Through the experiment, the system is designed to achieve the desired effect.

Simulating Design of Central Air Conditioning Control System in Office Buildings Based on PLC

2014 ◽  
Vol 525 ◽  
pp. 621-624
Author(s):  
Xue Jing Sun
Keyword(s):  
Control System ◽  
Software Design ◽  
Air Conditioning ◽  
Complete Control ◽  
Loop Control ◽  
Pid Algorithm ◽  
Central Air Conditioning ◽  
Programming Software ◽  
Plc Programming ◽  
Conditioning Control

In this paper, based on the Siemens S7-200 programmable controller in the central air conditioning control system, hardware and software of the central air-conditioning control system is designed. In hardware design PLC controller is used as controlling core to control temperature and humidity of the entire process. In the software design, control programming and multi-loop control of temperature, humidity PID algorithm are completed by using PLC programming software STEP7 because of its characteristics that it is easy to read and easy to achieve complete control. Lastly, Related parameter is set and the information is displayed by touching screen.

scholarly journals A Multivariable Controller for an Automotive Gas Turbine

1979 ◽  
Author(s):  
D. E. Winterbone ◽  
N. Munro ◽  
D. J. Nuske
Keyword(s):  
Control System ◽  
Gas Turbine ◽  
Transfer Functions ◽  
Poor Response ◽  
Test Bed ◽  
Complete Control ◽  
Loop Control ◽  
Non Linear ◽  
Basic Objective ◽  
Careful Manipulation

A complete control system design study is described, starting with a non-linear mathematical model and finishing with the control hardware. The basic objective of the study was to design a controller which reduced the characteristically poor response of the two-shaft automotive gas turbine. This was achieved by identifying the reasons for the acceleration delay and then designing the controller to compensate for them. The gas turbine was simulated by means of a quasi-steady non-linear thermodynamic model implemented on a digital computer. Careful manipulation of the equations enabled the model to run in real-time. This model was linearized at various operating points and the transfer functions obtained were compared with those measured on the plant. The control system was designed using Rosenbrock’s multivariable inverse Nyquist array technique. These compensators were grafted onto the original single loop control box and fitted to the engine. The results obtained on the engine test bed are compared with those using the original controller. A very large reduction in response lag is obtained with the multivariable control system.

Study on AGC Control System of 300 Reversible Cold Strip Mill

2011 ◽  
Vol 130-134 ◽  
pp. 3485-3488
Author(s):  
Tao Liu ◽  
Yi Qun Wang ◽  
Zi Gong Xue
Keyword(s):  
Control System ◽  
Response Characteristic ◽  
Hydraulic Cylinder ◽  
Rolling Process ◽  
Outer Loop ◽  
Loop Control ◽  
Pid Algorithm ◽  
Integral Action ◽  
The Stability ◽  
Cold Mill

In allusion to 300 Reversible cold mill, the composing and the theory of the auto gauge control system is presented. To meet the characteristic of the 300 reversible cold mill, the PID algorithm With Variable Integral Action is used in position and pressure inner loop control of press down hydraulic cylinder to elevate the response characteristic, and the dynamic press compensating algorithm is used in the gauge control outer loop to elevate the precision of strip thickness, and the stability of rolling process. Fig.1. 300 cold roling mill1 Introduction

Control Algorithm Analysis of Rig's Electro-Hydraulic Proportional Control System

2013 ◽  
Vol 423-426 ◽  
pp. 2837-2840
Author(s):  
Xiao Ying Zhao
Keyword(s):  
Control System ◽  
Pid Control ◽  
Control Algorithm ◽  
System Structure ◽  
Algorithm Analysis ◽  
Control Performance ◽  
Simulation Experiments ◽  
Proportional Control ◽  
Pid Algorithm ◽  
Fuzzy Control Algorithm

The electro-hydraulic proportional control system of the rig rotary mechanism was designed, and the work principle and system structure were introduced. On the bases of analyzing the characteristics of PID control algorithm and Fuzzy control algorithm, the design and research were carried. Simulation experiments show that the Fuzzy - PID algorithm effectively improves the control performance of the control system, and has a good environmental adaptability.

Design and Implementation of PID Algorithm Based on PLC Closed Loop Control System

2015 ◽  
Vol 727-728 ◽  
pp. 712-716
Author(s):  
Zhi Wei Guo ◽  
Cong Quan Hu
Keyword(s):  
Control System ◽  
Closed Loop ◽  
Digital Controller ◽  
Closed Loop Control ◽  
Output Variable ◽  
Loop Control ◽  
Pid Algorithm ◽  
Design And Implementation ◽  
Closed Loop Control System ◽  
Loop Control System

The article describes three usual manners and introduces about one Siemens S7-200PLC’s closed loop control system how to realize the PID , the principle of the PID digital controller, how to use the PID digital controller, an example about how to program with it, and how to convert the input-output variable.

Combined Temperature and Force Control for Robotic Friction Stir Welding

2013 ◽  
Author(s):  
Axel Fehrenbacher ◽  
Christopher B. Smith ◽  
Neil A. Duffie ◽  
Nicola J. Ferrier ◽  
Frank E. Pfefferkorn ◽  
...  
Keyword(s):  
Control System ◽  
Force Control ◽  
Closed Loop ◽  
Interface Temperature ◽  
Closed Loop Control ◽  
Weld Quality ◽  
Loop Control ◽  
Friction Stir ◽  
Tool Position ◽  
Robotic Friction Stir Welding

The objective of this research is to develop a closed-loop control system for robotic friction stir welding (FSW) that simultaneously controls force and temperature in order to maintain weld quality under various process disturbances. FSW is a solid-state joining process enabling welds with excellent metallurgical and mechanical properties, as well as significant energy consumption and cost savings compared to traditional fusion welding processes. During FSW, several process parameter and condition variations (thermal constraints, material properties, geometry, etc.) are present. The FSW process can be sensitive to these variations, which are commonly present in a production environment; hence, there is a significant need to control the process to assure high weld quality. Reliable FSW for a wide range of applications will require closed-loop control of certain process parameters. A linear multi-input-multi-output process model has been developed that captures the dynamic relations between two process inputs (commanded spindle speed and commanded vertical tool position) and two process outputs (interface temperature and axial force). A closed-loop controller was implemented that combines temperature and force control on an industrial robotic FSW system. The performance of the combined control system was demonstrated with successful command tracking and disturbance rejection. Within a certain range, desired axial forces and interface temperatures are achieved by automatically adjusting the spindle speed and the vertical tool position at the same time. The axial force and interface temperature is maintained during both thermal and geometric disturbances and thus weld quality can be maintained for a variety of conditions in which each control strategy applied independently could fail.

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].

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