Demonstrator of advanced controllers Hans Dirne Supervisors prof.dr.ir. J. van Amerongen dr.ir. J.F. Broenink dr.ir. T.J.A. de Vries ir. P.B.T. Weustink.

Slides:



Advertisements
Similar presentations
Feedback Control Real- time Scheduling James Yang, Hehe Li, Xinguang Sheng CIS 642, Spring 2001 Professor Insup Lee.
Advertisements

Chapter 9 PID Tuning Methods.
ABS Control Project Ondrej Ille Pre-bachelor Project.
5387 Avion Park Drive Highland Heights, Ohio INTUNE v4.4 Demonstration.

EE357 Control System I - Lec B2 (2010W) - Introduction.
João Rodrigues, Sérgio Brandão, Rui Rocha, Jorge Lobo, Jorge Dias {joaor, {jlobo, rprocha, Introduction The.
L OUISIANA T ECH U NIVERSITY Department of Electrical Engineering and Institute for Micromanufacturing INTRODUCTION PROBLEM FORMULATION STATE FEEDBACK.
Introduction CSCI 444/544 Operating Systems Fall 2008.
Mechatronics at the University of Calgary: Concepts and Applications Jeff Pieper.
Restricted Slow-Start for TCP William Allcock 1,2, Sanjay Hegde 3 and Rajkumar Kettimuthu 1,2 1 Argonne National Laboratory 2 The University of Chicago.
Robust control Saba Rezvanian Fall-Winter 88.
Low Complexity Keypoint Recognition and Pose Estimation Vincent Lepetit.
MotoHawk Training Model-Based Design of Embedded Systems.
 KHAIRUNNISA BINTI ABD AZIZ  MOHD SHAHRIL BIN IBRAHIM  MOHD SUFI B AB MAJID  MOHD SUPIAN B MOHD NAZIP  MOKTAR BIN YAAKOP.
280 SYSTEM IDENTIFICATION The System Identification Problem is to estimate a model of a system based on input-output data. Basic Configuration continuous.
Real-time Embedded Face Recognition for Smart Home Fei Zuo, Student Member, IEEE, Peter H. N. de With, Senior Member, IEEE.
NORM BASED APPROACHES FOR AUTOMATIC TUNING OF MODEL BASED PREDICTIVE CONTROL Pastora Vega, Mario Francisco, Eladio Sanz University of Salamanca – Spain.
The Mechatronics Design Lab Course at the University of Calgary Presented June 2, 2003.
Support Vector Regression David R. Musicant and O.L. Mangasarian International Symposium on Mathematical Programming Thursday, August 10, 2000
Model based friction compensation for an electro- mechanical actuator of a Stewart platform Maarten Willem van der Kooij Friday, November 4 th 2011 TexPoint.
Data Structures and Programming.  John Edgar2.
Advanced Phasor Measurement Units for the Real-Time Monitoring
N. Baćac*, V. Slukić*, M. Puškarić*, B. Štih*, E. Kamenar**, S. Zelenika** * University of Rijeka, Faculty of Engineering, Rijeka, Croatia ** University.
Portable and Predictable Performance on Heterogeneous Embedded Manycores (ARTEMIS ) ARTEMIS Project Review 28 nd October 2014 Multimedia Demonstrator.
Introduction to estimation theory Seoul Nat’l Univ.
Kalman filtering techniques for parameter estimation Jared Barber Department of Mathematics, University of Pittsburgh Work with Ivan Yotov and Mark Tronzo.
Ultrasonic Tracking System Group # 4 4/22/03 Bill Harris Sabie Pettengill Enrico Telemaque Eric Zweighaft.
A Shaft Sensorless Control for PMSM Using Direct Neural Network Adaptive Observer Authors: Guo Qingding Luo Ruifu Wang Limei IEEE IECON 22 nd International.
EM and expected complete log-likelihood Mixture of Experts
1 Outline:  Outline of the algorithm  MILP formulation  Experimental Results  Conclusions and Remarks Advances in solving scheduling problems with.
Book Adaptive control -astrom and witten mark
Upgrade to Real Time Linux Target: A MATLAB-Based Graphical Control Environment Thesis Defense by Hai Xu CLEMSON U N I V E R S I T Y Department of Electrical.
Optimal Power Control, Rate Adaptation and Scheduling for UWB-Based Wireless Networked Control Systems Sinem Coleri Ergen (joint with Yalcin Sadi) Wireless.
PSE and PROCESS CONTROL
Foot Throttle Foot throttle device for lower limb rehabilitation.
1 Final Conference, 19th – 23rd January 2015 Geneva, Switzerland RP 15 Force estimation based on proprioceptive sensors for teleoperation in radioactive.
Disturbance Rejection: Final Presentation Group 2: Nick Fronzo Phil Gaudet Sean Senical Justin Turnier.
Overview of Supervised Learning Overview of Supervised Learning2 Outline Linear Regression and Nearest Neighbors method Statistical Decision.
Chapter 8 Model Based Control Using Wireless Transmitter.
Distributed Laboratories: Control System Experiments with LabVIEW and the LEGO NXT Platform Greg Droge, Dr. Bonnie Heck Ferri, Jill Auerbach.
Observer-Based Robot Arm Control System Nick Vogel, Ron Gayles, Alex Certa Advised by: Dr. Gary Dempsey.
1 Structure of Aalborg University Welcome to Aalborg University.
IMPACT OF CACHE PARTITIONING ON MULTI-TASKING REAL TIME EMBEDDED SYSTEMS Presentation by: Eric Magil Research by: Bach D. Bui, Marco Caccamo, Lui Sha,
Intelligent controller design based on gain and phase margin specifications Daniel Czarkowski  and Tom O’Mahony* Advanced Control Group, Department of.
Review: Neural Network Control of Robot Manipulators; Frank L. Lewis; 1996.
ERT 210/4 Process Control Hairul Nazirah bt Abdul Halim Office: CHAPTER 8 Feedback.
Control systems KON-C2004 Mechatronics Basics Tapio Lantela, Nov 5th, 2015.
PID Control of Car Position Exercises in Manual Tuning.
Lecture 25: Implementation Complicating factors Control design without a model Implementation of control algorithms ME 431, Lecture 25.
Adaptive Control Loops for Advanced LIGO
Student Name USN NO Guide Name H.O.D Name Name Of The College & Dept.
Chapter 8. Learning of Gestures by Imitation in a Humanoid Robot in Imitation and Social Learning in Robots, Calinon and Billard. Course: Robots Learning.
Chapter 5 Dynamics and Regulation of Low-order Systems
Matlab Tutorial for State Space Analysis and System Identification
Disturbance rejection control method
Diagnostics and Optimization Procedures for Beamline Control at BESSY A. Balzer, P. Bischoff, R. Follath, D. Herrendörfer, G. Reichardt, P. Stange.
HIE-ISOLDE Workshop, 29 November 2013M. COLCIAGO1 * The research project has been supported by a Marie Curie Early Initial Training Network Fellowship.
ROBOTICS 01PEEQW Basilio Bona DAUIN – Politecnico di Torino.
XFEL The European X-Ray Laser Project X-Ray Free-Electron Laser Wojciech Jalmuzna, Technical University of Lodz, Department of Microelectronics and Computer.
A PID Neural Network Controller
Modelling & Simulation of Semiconductor Devices Lecture 1 & 2 Introduction to Modelling & Simulation.
Intelligent Control Grant Agreement No LLP UK-LEONARDO-LMP Project acronym: CLEM Project title: Cloud services for E-Learning in Mechatronics.
XFEL The European X-Ray Laser Project X-Ray Free-Electron Laser Wojciech Jalmuzna, Technical University of Lodz, Department of Microelectronics and Computer.
Mechatronics at the University of Calgary: Concepts and Applications
Chapter 4 Introduction to Automation
Raja M. Imran, D. M. Akbar Hussain and Mohsen Soltani
Presentation at NI Day April 2010 Lillestrøm, Norway
Quanser Rotary Family Experiments
Instrumentation and control
Presentation transcript:

Demonstrator of advanced controllers Hans Dirne Supervisors prof.dr.ir. J. van Amerongen dr.ir. J.F. Broenink dr.ir. T.J.A. de Vries ir. P.B.T. Weustink May 25 th, 2005 Master of Science assignment

May 25 th, 2005Demonstrator of advanced controllers2 Why this assignment? The Major ‘Mechatronics’ provides several courses in control theory, in which the theory is often supported by simulations. A physical setup might, in addition to simulations, be an enrichment for demonstrating control theory. Such a demonstration setup will be able to make the theory more insightful and will show real limitations in practical setups.

May 25 th, 2005Demonstrator of advanced controllers3 Objectives 1.To design, build and test a mechatronic demonstration setup, with which several control algorithms can be shown in practice 2.To be able to demonstrate performance differences of control algorithms in practice

May 25 th, 2005Demonstrator of advanced controllers4 Overview 1.Demonstration setup options 2.Control systems 3.Design of the new demonstrator 4.Experiments 5.Demonstration 6.Conclusions & recommendations

May 25 th, 2005Demonstrator of advanced controllers5 Demonstration setup options

May 25 th, 2005Demonstrator of advanced controllers6 1.Mechatronic system 2.Portable and easy to set up 3.Robust, safe and failsafe design 4.High level of observability 5.Representable by linear 4 th order model 6.Clear link with well known device Criteria

May 25 th, 2005Demonstrator of advanced controllers7 Three options 1.‘Linix’ laboratory setup 2.Setup of ‘Controllab Products B.V.’ 3.New build

May 25 th, 2005Demonstrator of advanced controllers8 Option 1: ‘Linix’ Laboratory Setup

May 25 th, 2005Demonstrator of advanced controllers9 ‘Linix’ Laboratory Setup motor encoders inertia 2 inertia 1 transmission

May 25 th, 2005Demonstrator of advanced controllers10 ‘Linix’ Laboratory Setup

May 25 th, 2005Demonstrator of advanced controllers11 ‘Linix’ Laboratory Setup Major disadvantage: slip between belt and inertias

May 25 th, 2005Demonstrator of advanced controllers12 Option 2: CLP setup

May 25 th, 2005Demonstrator of advanced controllers13 CLP setup

May 25 th, 2005Demonstrator of advanced controllers14 CLP setup

May 25 th, 2005Demonstrator of advanced controllers15 Sensor positions

May 25 th, 2005Demonstrator of advanced controllers16 Option 3: New Build Advantage Pure design freedom Disadvantage Requires very much time and effort to design

New buildLinixCLP-setup Mechatronic system√√√ Linear 4 th order model√limited linearto be determined Portable, easy to set up√√not in current form Robust, safe, failsafe√√feasible Observability√yes, 2 position Sensors yes, 4 position sensors Link with practical device√transmissionprinter Shows controller differences√no, due to nonlinearities To be determined RemarksTime constraint Overview demonstrators

May 25 th, 2005Demonstrator of advanced controllers18 Control Systems

May 25 th, 2005Demonstrator of advanced controllers19 Mathematical model – 6 th order Viscous PLUS coulomb friction

May 25 th, 2005Demonstrator of advanced controllers20 Focus 1.Linear Quadratic Gaussian (LQG) 2.Proportional, Integral, Differential (PID)

May 25 th, 2005Demonstrator of advanced controllers21 LQG explanation A LQG control algorithm is a combination of 1.Lin. Quad. Regulator (state feedback) 2.Lin. Quad. Estimator (state estimation) 4 th order linear model required!

May 25 th, 2005Demonstrator of advanced controllers22 4 th order linear model Required steps: 1.Downsize system order 2.Linearize system: discard coulomb friction Result: linear 4th order model (e.g. State Space)

May 25 th, 2005Demonstrator of advanced controllers23 LQG controlled system

May 25 th, 2005Demonstrator of advanced controllers24 PID

May 25 th, 2005Demonstrator of advanced controllers25 Tuning (1) For proper comparison of the PID with the LQG controlled system, tuning with the same criteria is required. 1.Avoid actuator saturation 2.Minimization of criterion: position errorcontroller output

May 25 th, 2005Demonstrator of advanced controllers26 Tuning (2) Tuning procedure: 1.Set Q and R 2.Minimize criterion J by optimizing controller gains (K LQG and K P,K I,K D )

May 25 th, 2005Demonstrator of advanced controllers27 Tuning (3) Optimization results K P = 15.7 K I = 42 K D = 1.6 K LQG = [3.7, 74, 8.2, 70] T

May 25 th, 2005Demonstrator of advanced controllers28 PID vs LQG (1) The PID controlled system consumes twice the power of the LQG system The maximum frame movement in the PID controlled system is twice compared to LQG

May 25 th, 2005Demonstrator of advanced controllers29 PID vs LQG (2) The LQG control algorithm leads to an unacceptable position error with the nonlinear process

May 25 th, 2005Demonstrator of advanced controllers30 LQG+

May 25 th, 2005Demonstrator of advanced controllers31 LQG+ vs LQG Effect of integrator: Static error is minimized! Interesting to see the performance of LQG+ in practice…

May 25 th, 2005Demonstrator of advanced controllers32 Design of the new demonstrator

May 25 th, 2005Demonstrator of advanced controllers33 Goal: to test a control algorithm on a physical setup Procedure How?

May 25 th, 2005Demonstrator of advanced controllers34 System overview (1) Client: Runs MS Windows Generating models Model control (start/stop/upload/delete) Setting parameters of controlled system real-time View parameters of controlled system real-time Server: Runs Linux, with real-time kernel Runs control system Performs I/O

May 25 th, 2005Demonstrator of advanced controllers35 System overview (2)

May 25 th, 2005Demonstrator of advanced controllers36 Realization Mechatronics Embedded PC + I/O Power (CPU) Power (motor) Motor amplifier

May 25 th, 2005Demonstrator of advanced controllers37 Experiments

May 25 th, 2005Demonstrator of advanced controllers38 Experiments Comparison of PID/LQG/LQG+ performance on the new demonstration setup Same controller parameters used as in simulation (after tuning) Performance comparison on: 1.Static error 2.Frame vibration 3.Power usage

May 25 th, 2005Demonstrator of advanced controllers39

May 25 th, 2005Demonstrator of advanced controllers40 Results The LQG+ controlled system outperforms the PID controlled system: Maximum frame movement differs factor 3 Total power consumption differs a factor 2 Both control algorithms minimize the static error, but the LQG controlled system is faster More performance increase is expected with a better model Differences in performance between 2 nd order PID and 4 th order LQG have now been demonstrated in practice

May 25 th, 2005Demonstrator of advanced controllers41 Demonstration

May 25 th, 2005Demonstrator of advanced controllers42 Demonstration What will be shown: 1.‘Homing’ of the demonstrator 1.Determining absolute position 2.PID controller in practice with various controller gains Furthermore: 1.Online adjustment of parameters 2.Real-time variable monitoring 3.Real-time animation of demonstration setup

May 25 th, 2005Demonstrator of advanced controllers43 Conclusions&Recommendations

May 25 th, 2005Demonstrator of advanced controllers44 Conclusions 1.The new mechatronic demonstration setup is a compact, integrated machine that forms a versatile development environment for testing various control algorithms in practice 2.The new demonstrator allows for easy comparison of different control algorithms 3. Non-linear friction elements in the process will lead to lower performance in position control of a 4 th order LQG-controlled system compared to a 2 nd order PID control algorithm 4.Addition of an integrating term leads to an ‘LQG+’ control algorithm that can compensate for differences between process and reference model.

May 25 th, 2005Demonstrator of advanced controllers45 Recommendations Hardware Expand safety system Reduce weight of the demonstrator (next version) Add parallel processing (e.g. distributed control) Software / control Experiment with more control systems (MRAS, (L)FF, ILC etc) Perform system identification General 1.Set up lab work assignments for student

May 25 th, 2005Demonstrator of advanced controllers46 Questions…?

May 25 th, 2005Demonstrator of advanced controllers47 THANK YOU FOR YOUR ATTENTION you are all invited for DRINKS at ‘De Tombe’, floor 0