ECE 192: NATCAR Team (Triton X) Sponsored by IEEE (http://ieee.ucsd.edu) Vincent Bantigue, Joseph Formanes,

Slides:



Advertisements
Similar presentations
Proportional, Integral, Derivative Line Following October 5, 2013.
Advertisements

Students : Hiba Ghannam Hawa’ Osama Supervisor : Aladdin Masri R OBOTIC V ACUUM C LEANER.
5/4/2006BAE Analog to Digital (A/D) Conversion An overview of A/D techniques.
ALİ RIZA GÜMÜŞ EVREN KÖYBAŞI VOJTECH HEMALA Eskişehir, 2013.
Autonomous Quadrocopter Proposal Brad Bergerhouse, Nelson Gaske, Austin Wenzel Dr. Malinowski.
EE396 Project Micromouse Team: Ocha. Team Members Kanoa Jou (Programmer) Ryan Sato (Hardware) KiWoon Ahn (Recorder) Alan Do (Programmer)
Tracking Migratory Birds Around Large Structures Presented by: Arik Brooks and Nicholas Patrick Advisors: Dr. Huggins, Dr. Schertz, and Dr. Stewart Senior.
‘Iole o Mãnoa Mouse of Mãnoa. Team Members Jeff Fines Designer, Fabricator, Programmer & Thomas Matsushima Designer, Fabricator, Programmer.
Roberto - Balancing Robot RIT Computer Engineering Senior Design Project.
Preliminary Design Review
Solar Tracking Project Team Members: –Cristian Ruvalcaba –Ken Seal –David Clark –Mark McKinley –Richard DeJarnatt.
Tracking Rover Team Rubber Ducky Alex Chi Joshua Rubin Alexander Starick Ryan Ramos.
Tag Bot: A Robotic Game of Tag Jonathan Rupe Wai Yip Leung.
DC Motor Control  mouse EE 496 Advisor: Dr. Tep Dobry.
 Main Components:  Sensors  Micro controller  Motor drivers  Chasis.
Lecture 3: The Controller PID Control and Speed Profile.
Digital to Analog Converters
Writing a Program Chapter 1. Introduction We all “start” by learning how to code in some programming language. –With a small, hypothetical, and fairly.
Abstract There are many applications today that require a means of controlling a particular setting given an ever changing environment without human intervention.
Acceleration Based Pedometer
Behavior Based Robotics: A Wall Following Behavior Arun Mahendra - Dept. of Math, Physics & Engineering, Tarleton State University Mentor: Dr. Mircea Agapie.
RC Car Thomas Chau, Ben Sack, Peter Tsonev. Overview Goal: to build a smart RC car that corrects itself using sensors. Objective: testing our run at high.
Automatic Control Mike Robinson. You can measure the distance from the RC car to some target. What could your program do to keep the car as close to the.
A Shaft Sensorless Control for PMSM Using Direct Neural Network Adaptive Observer Authors: Guo Qingding Luo Ruifu Wang Limei IEEE IECON 22 nd International.
GROBI Gizmo Remote Operated Bluetooth Interface Sponsor: Calit 2 Mentors: Paul Blair & Javier Rodriguez Molina Team: Kristi Tsukida & Eldridge Alcantara.
Ryan Courtney Senior Design II Advisor: Junkun Ma.
Analog to Digital conversion. Introduction  The process of converting an analog signal into an equivalent digital signal is known as Analog to Digital.
Software Tutorial 26 Sept Agenda Development Environment Brain Board API Sensors Basic Behaviour Control.
ECE 477 Design Review Group 11  Fall Outline Project overviewProject overview Project-specific success criteriaProject-specific success criteria.
CS 478: Microcontroller Systems University of Wisconsin-Eau Claire Dan Ernst Feedback Control.
By: 1- Aws Al-Nabulsi 2- Ibrahim Wahbeh 3- Odai Abdallah Supervised by: Dr. Kamel Saleh.
STEPPER MOTORS Name: Mr.R.Anandaraj Designation: Associate. Professor Department: Electrical and Electronics Engineering Subject code :EC 6252 Year: II.
1 Lecture on Lab 6 Lab 7 Lab 8. 2 Lab 6: Open Loop Controller As you learned in lab 5, there are two kinds of control systems: open loop and closed loop.
Pioneers in Engineering, UC Berkeley Pioneers in Engineering Week 8: Sensors and Feedback.
PID. The proportional term produces an output value that is proportional to the current error value. Kp, called the proportional gain constant.
Automatic accident avoiding system PROJECT MEMBERS MUTHUKUMAR.K (05ME33) SAKTHIDHASAN.S (05ME39) SAKTHIVEL.N (05ME40) VINOTH.S (05ME56) PROJECT GUIDE:
PID CONTROLLERS By Harshal Inamdar.
Control systems KON-C2004 Mechatronics Basics Tapio Lantela, Nov 5th, 2015.
DATA HANDLING Some situations arise where a group of bits have to be handled. (ex) a sensor supplies an analogue signal which is converted to, say, an.
July 18, UCSD - R.A. de Callafon Short Intro to Micro Processors and I/O functions of our Kinetic Sculpture Control Box Raymond de Callafon.
Mark Randall & Kevin Claycomb Faculty Advisor: David Mitchell Industrial Sponsor: IEEE.
Strong as a Buck. Meet The Team Warn Wilson John Clark Dre Crumbly Electrical Engineering Computer Engineering.
Current Works Determined drift during constant velocity test caused by slight rotation which results in gravity affecting accelerometers Analyzed data.
Lecture 16: Introduction to Control (Part II)
ECE 192: NATCAR Team (Triton X) Sponsored by IEEE ( Vincent Bantigue, Joseph Formanes,
Image Processing A Study in Pixel Averaging Building a Resolution Pyramid With Parallel Computing Denise Runnels and Farnaz Zand.
ECE 192: NATCAR Team (Triton X) Sponsored by IEEE ( Vincent Bantigue, Joseph Formanes,
ECE 192: NATCAR Team (Triton X) Sponsored by IEEE ( Vincent Bantigue, Joseph Formanes,
Embedded Control Systems Dr. Bonnie Heck School of ECE Georgia Tech.
Lab 1 Summary.
ECE 4330 – Final Project By: John Litzenberger.  A IC temperature sensor (DS1620)  Reads through ADC (pin.0 Port A)  Feedback control for extreme conditions.
ECE 192: NATCAR Team (Triton X) Sponsored by IEEE ( Vincent Bantigue, Joseph Formanes,
ECE 192: NATCAR Team (Triton X) Sponsored by IEEE ( Vincent Bantigue, Joseph Formanes,
Flow of signal So you have a sensor, now to process data taken from a sensor you will need a processing unit and that is your controller. sensorcontroller.
ECE 192: NATCAR Team (Triton X) Sponsored by IEEE ( Vincent Bantigue, Joseph Formanes,
By : Rohini H M USN : 2VX11LVS19.  This system includes sensors for measuring vehicle speed; steering input; relative displacement of the wheel assembly.
Lecture Notes / PPT UNIT III
Components of Mechatronic Systems AUE 425 Week 2 Kerem ALTUN October 3, 2016.
6: Processor-based Control Systems CET360 Microprocessor Engineering J. Sumey.
Track-While-Scan (TWS)
Obstacle avoiding robot { pixel }
Robotic Vacuum Cleaner
PID Controllers Jordan smallwood.
DC MOTOR SPEED CONTROL 1. Introduction
6: Processor-based Control Systems
Better Line Following with PID
Bell Work: Motion of a Car
ECE 477 Final Presentation Team 1  Spring 2008
Image Acquisition and Processing of Remotely Sensed Data
PID Line Follower.
Presentation transcript:

ECE 192: NATCAR Team (Triton X) Sponsored by IEEE ( Vincent Bantigue, Joseph Formanes, Henry Kao, Puneet Khattar, Advisor: Dr. Clark Guest Week 7, 2/18/05

Agenda: Tasks Accomplished this week PID Control Theory IR Sensors Upcoming Tasks for next week

Tasks Accomplished this week: Acquired IR sensor parts In the process of building IR sensor circuit Continued creating MATLAB models of IR sensor system Continued microcontroller programming I/O Programming learned I/O Programming learned PWM Output learned PWM Output learned Studied PID (Proportional, Integral, Derivative) Control Theory

PID Control Theory: To control speed and steering using PID, an algorithm manipulates one control output to force a process value towards a reference point. Analogy: Cruise Control System Control Output is acceleration Control Output is acceleration Process Value is current speed Process Value is current speed Reference point is target speed Reference point is target speed Analogy courtesy of

PID (cont’d)- Proportional: e n = y n – r n e n is error, y n is process value, and r n is reference point (target). e n is error, y n is process value, and r n is reference point (target). All three components of PID are driven by the error, e n All three components of PID are driven by the error, e n uP n = K p * e n u n is control output, K p = proportionality constant u n is control output, K p = proportionality constant Problem: Need to know when u n = 0 (when e n = 0) Problem: Need to know when u n = 0 (when e n = 0)

PID (cont’d)- Integral: uI n = K i * Σ 0 n (e n * dt) Over time, the larger the summation gets, this component contributes more to the control output In the example of the cruise control, if the integral increases over time, the acceleration increases to get to the target speed faster In the example of the cruise control, if the integral increases over time, the acceleration increases to get to the target speed faster

PID (cont’d)- Derivative: uD n = K d * de n / dt The rate of the change of the error with respect to time. Effect: holds back PID system. Prevents oscillations by predicting future of error. Prevents oscillations by predicting future of error. When the error is approaching stability, derivative component is less. When the error is approaching stability, derivative component is less. Usually K d is small because its highly sensitive to noise

PID (cont’d): u n = K p * uP n + K i * uP i + K d * uP d The total control output is a sum of the proportional, integral and derivative components.

Row Sensor

Sensors: Rate of Change Response Create a history of past line center points (LCPs) Error (E): Distance from row’s center point Desire Decreasing Error over time If E n < E n-1, Decreasing Average Error: Check Over Previous k errors { int(En T { int(En T Where T is some threshold > k/2 s.t. the majority of the Error differences are decreasing over time Where T is some threshold > k/2 s.t. the majority of the Error differences are decreasing over time If Avg Error increase, Alter turn degree

Accurate Path Calculation Create a good model for the car Need the acceleration graph of the motor Need true velocity of the car (place optical sensors on motor shaft Need Arc Sensing ability (possibly with multirow arrays)

Upcoming Tasks for next week: Finish working prototype Assemble Motor Controller Assemble Motor Controller Finish assembling IR sensor array circuit Finish assembling IR sensor array circuit Assemble DC-DC converter Assemble DC-DC converter Complete basic Program Complete basic Program Begin testing

Microcontroller: Concatenated PWM registers for use of 16bit duty resolution (instead of 8bit) Created code for translating 16bits of data from input port registers into distance error Translated PID algorithm from MATLAB to C Learned to load data into FLASH memory (with help from members of other groups)

Microcontroller (cont’d): Sensor data conversion pseudo-code: for i = 0 to 7 (bits of sensor) for i = 0 to 7 (bits of sensor) Mask = bitwise shifted left by I Current = mask & sensor_registers If (current == 1){ //that particular bit is high white detected Error = error + (val – i) * spacing //val is (N-1)/2, spacing is distance b/w center of two sensors next to each other Error = error + (val – i) * spacing //val is (N-1)/2, spacing is distance b/w center of two sensors next to each other Num = num+1 Num = num+1}} If (num == 0) Error = previous error else Error = error/(float)(num);

Microcontroller (cont’d): SPACING ERROR Example: Bits = 8, Val = 3.5 Bit 5 high: (3.5 – 5)x 2cm = -3, Bit 6 high: (3.5-6)x0.2cm = -5 Error = = -8cm Real Error = -8cm / 2 = -4cm 2cm

Microcontroller (cont’d): Obstacles: 4Kb on EEPROM is not nearly enough to fit our program (solved: used FLASH) 4Kb on EEPROM is not nearly enough to fit our program (solved: used FLASH) Bootloader says “Flash Program Error” when I try to load my program into Flash Bootloader says “Flash Program Error” when I try to load my program into Flash (will call Imagecraft and read documentation to find out how to properly load FLASH)