scholarly journals Tracking Control of an Autonomous Head Feeding Combine

2021 ◽  
Vol 20 (2) ◽  
pp. 85-90
Author(s):  
Setya Permana Sutisna ◽  
Radite Praeko Agus Setiawan ◽  
I Dewa Made Subrata ◽  
Tineke Mandang
Keyword(s):  
Control System ◽  
Sampling Rate ◽  
Preliminary Test ◽  
Navigation Systems ◽  
Guidance Control ◽  
Steering Mechanism ◽  
Combine Movement ◽  
Combine Harvester ◽  
Lateral Error ◽  
Traveling Speed

This study aims to develop an autonomous combine harvester. A manual steering combine harvester was modified autonomously using navigation systems of an RTK-DGPS, a gyroscope, and crawler speed sensors. These sensors could determine the combine position and heading required to guide the path. The control system is processed for these navigation sensors' data to make the decision of combine movement. Moreover, it commands the actuator to move the steering lever mechanism. The steering control's desired heading angle was determined from lateral error, heading error, and the traveling speed. In this study, the combined harvester's average forward traveling speed was set at 0.17 m/s, adjusted to a navigation sensor's sampling rate of 5 Hz and the steering mechanism delay. The preliminary test showed the combine could turn by pivoting one of its tracks which turned the radius was into 0.4 m. Furthermore, a guidance control system of the combine harvester was developed based on this test result. The developed guidance control system was successfully guiding the combine to follow the harvesting path. The test results showed that the root mean square of the lateral error was less than 0.1 m.

Synthesis, Stability Analysis, and Experimental Implementation of a Multirate Repetitive Learning Controller

2002 ◽  
Vol 124 (4) ◽  
pp. 668-674 ◽  
Author(s):  
Nader Sadegh ◽  
Ai-Ping Hu ◽  
Courtney James
Keyword(s):  
Control System ◽  
Stability Analysis ◽  
Tracking Error ◽  
Sampling Rate ◽  
Weighted Averaging ◽  
System Control ◽  
Control Input ◽  
Experimental Implementation ◽  
Repetitive Learning ◽  
Averaging Filter

This paper describes a multirate repetitive learning controller with an adjustable sampling rate that may be used as an “add-on” module to enhance the tracking performance of a feedback control system. The sampling rate of the multirate controller is slower than the remainder of the control system, and is selected by the user to achieve the required system performance based on a trade-off between the accuracy and the complexity of the controller. The multirate controller learns the system control input based on the tracking error down-sampled using a weighted averaging filter. The output of the multirate controller is up-sampled through an arbitrary hold mechanism determined by the user. This paper extends the existing stability results for single-rate repetitive learning controllers to the proposed multirate scheme. It provides an explicit procedure for its design and stability analysis. In addition, the proposed multirate repetitive learning controller is implemented on a mechanical system performing a non-colocated control task, where its effectiveness in reducing tracking errors while following periodic reference trajectories is shown experimentally.

Generated Trajectory of Extended Lateral Guided Sensor Steering Mechanism for Steered Autonomous Vehicles in Real World Environments

2017 ◽  
Vol 29 (4) ◽  
pp. 660-667 ◽  
Author(s):  
Yoshihiro Takita ◽  
Keyword(s):  
Experimental Data ◽  
Control System ◽  
Real World ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Steering Mechanism ◽  
World Environment

This paper discusses the generated trajectory of an extended lateral guided sensor steering mechanism (SSM) method for a steered autonomous vehicle moving in a real world environment. In a previous study, an extended SSM was applied to the Smart Dump 9 and AR Chair robots for following preset waypoints on a map. These studies showed only the schematic idea of the method; the precise performance of the generated trajectory was not shown. This paper compares the Smart Dump 9 robot with a newly developed AR Skipper robot; these robots participated in the Tsukuba Challenge in 2015 and 2016, respectively. Finally, experimental data from the Tsukuba Challenge 2016 demonstrates the advantages of the extended SSM and developed control system.

Parking Guidance Control System Based on Internet of Things

2013 ◽  
Vol 273 ◽  
pp. 641-645 ◽  
Author(s):  
Rong Chun Sun ◽  
Yan Piao ◽  
Yu Wang ◽  
Han Wang
Keyword(s):  
Control System ◽  
Internet Of Things ◽  
Real Time ◽  
The Internet ◽  
Guidance Control ◽  
Control Center ◽  
The Real ◽  
Real Time Information ◽  
Time Information ◽  
The Internet Of Things

To help drivers to quickly find a spare parking, a parking guidance control system was proposed. The principle of ultrasonic ranging was used to detect the state of a parking space, and through the internet of things the parking detector transmits the real-time information to the control center. The control center mainly is an industrial computer and is responsible for dealing with the real-time information and sending the control command by internet of things. The guidance signs at each crossroad receive the wireless commands and execute them, by which the guidance function is performed. The internet of things was realized by ZigBee star network, in which the control center is a coordinator and other parts are routers or terminal equipments. The simulation experiment results show that the parking guidance system works well, and has the value of application and promotion to some extent.

scholarly journals Computer simulator for motion control hydrofoil vessels

2020 ◽  
Author(s):  
А.Л. Стариченков ◽  
Е.А. Стариченкова
Keyword(s):  
Control System ◽  
Motion Control ◽  
Computer Control ◽  
Control Object ◽  
Control Panel ◽  
Navigation Systems ◽  
Industrial Control ◽  
Video Frames ◽  
Software Product ◽  
Set Up

В современных высших гражданских и военных учебных заведениях при изучении систем управления и навигации используются тренажеры, построенные по следующему принципу: промышленный пульт управления (система управления), устанавливаемый на отечественных судовых системах, соединяется со схемой или устройством, моделирующем поведение объекта управления. Такие тренажеры зачастую дороги, сложны в изготовлении, обслуживании и наладке. При современном уровне развития вычислительной техники в обучении различным технологическим навыкам и умениям все чаще используются программы-тренажеры, с достаточно большой точностью моделирующие как систему управления, так и объект управления. В статье предлагается программный тренажер, позволяющий моделировать различные режимы движения судна на подводных крыльях, включая аварийные режимы движения, посредством отображения на дисплее компьютера пульта управления движением судна на подводных крыльях с возможностью изменения заданных значений кинематических параметров. В рассматриваемом тренажере предусмотрена возможность отображения процесса движения судна в продольной и поперечной плоскостях, а также графиков изменения во времени соответствующих параметров состояния судна. Приводятся видеокадры представленной программной оболочки. Обсуждаются преимущества использования предложенного программного продукта в качестве тренажера для обучения судоводителей. В статье представлен один из возможных вариантов программного обеспечения для тренажера управления движением судна на подводных крыльях. Для создания тренажера в данной работе была выбрана система Бирюза 2 , предназначенная для установки на пассажирских судах на подводных крыльях Циклон , Альбатрос и Колхида для повышения их маневренных и мореходных качеств. In modern higher civil and military educational institutions, when studying control and navigation systems, simulators are used based on the following principle: an industrial control panel (control system) installed on domestic ship systems is connected to a circuit or device that simulates the behavior of the control object. Such simulators are often expensive, difficult to manufacture, maintain and set up. At the current level of development of computer technology, training in various technological skills and abilities is increasingly used simulator programs that simulate both the control system and the control object with a fairly high accuracy. The article offers a software simulator that allows you to simulate various modes of movement of a hydrofoil vessel, including emergency modes of movement, by displaying a computer control panel for the movement of a hydrofoil vessel with the ability to change the set values of kinematic parameters. In this simulator, it is possible to display the process of ship movement in the longitudinal and transverse planes, as well as graphs of changes in time of the corresponding parameters of the ships condition. Video frames of the presented software shell are provided. The advantages of using the proposed software product as a simulator for training skippers are discussed. The article presents one of the possible software options for the hydrofoil motion control simulator. To create a simulator, the system Biryuza 2 was selected in this work. It is intended for installation on passenger hydrofoils Cyclone , Albatros and Kolhida to improve their maneuverability and seaworthiness.

scholarly journals Design of Multi-Rate Multi-Zone Wireless Fuzzy Temperature Control System for Greenhouse Application

2020 ◽  
Vol 26 (7) ◽  
pp. 97-114
Author(s):  
Qays Jebur Sabr
Keyword(s):  
Control System ◽  
Fuzzy Controller ◽  
Networked Control Systems ◽  
Sampling Rate ◽  
Sampling Period ◽  
Networked Control System ◽  
Networked Control ◽  
Temperature Control System ◽  
Temperature Controller ◽  
Input Gain

sensor sampling rate (SSR) may be an effective and crucial field in networked control systems.  Changing sensor sampling period after designing the networked control system is a critical matter for the stability of the system. In this article, a wireless networked control system with multi-rate sensor sampling is proposed to control the temperature of a multi-zone greenhouse. Here, a behavior based Mamdany fuzzy system is used in three approaches, first is to design the fuzzy temperature controller, second is to design a fuzzy gain selector and third is to design a fuzzy error handler. The main approach of the control system design is to control the input gain of the fuzzy temperature controller depending on the current zone and current sensor rate for each zone. Due to the low input gain of the fuzzy controller, the steady state output error of the greenhouse temperature is in the range (0.55 – 11.22) % when the system using five sensors of different sampling rates and in the range (2.43 - 16.74) % when the system using five sensors with the same sampling rates. Next, after designing the fuzzy error handler, this error doesn’t exceed 1.6%, but in most cases it is less than 0.15%.The work is Simulink designed and implemented using Matlab R2012b. The Zigbee wireless network is proposed for the system, it is implemented in Matlab using True time 2.0 library.

Sign in / Sign up

Export Citation Format

Share Document