ECE 893 Industrial Applications of Nonlinear Control Dr

Slides:



Advertisements
Similar presentations
 (x) f(x,u) u x f(x,  (x) x. Example: Using feed-forward, what should be canceled?
Advertisements

Lecture 2. A Day of Principles The principle of virtual work d’Alembert’s principle Hamilton’s principle 1 (with an example that applies ‘em all at the.
9.11. FLUX OBSERVERS FOR DIRECT VECTOR CONTROL WITH MOTION SENSORS
ELECTRIC DRIVES Ion Boldea S.A.Nasar 1998 Electric Drives.
7. Modeling of Electromechanical Systems
Hybrid Terminal Sliding-Mode Observer Design Method for a Permanent-Magnet Synchronous Motor Control System 教授 : 王明賢 學生 : 胡育嘉 IEEE TRANSACTIONS ON INDUSTRIAL.
T. YOSHIDA, J. OYAMA, T. HIGUCHI, T. ABE and T. HIRAYAMA Department of Electrical and Electronic Engineering, Nagasaki University, Japan ON THE CHARACTERISTICS.
Robust and Efficient Control of an Induction Machine for an Electric Vehicle Arbin Ebrahim and Dr. Gregory Murphy University of Alabama.
Electric Drives FEEDBACK LINEARIZED CONTROL Vector control was invented to produce separate flux and torque control as it is implicitely possible.
Exponential Tracking Control of Hydraulic Proportional Directional Valve and Cylinder via Integrator Backstepping J. Chen†, W. E. Dixon‡, J. R. Wagner†,
Modeling of Induction Motor using dq0 Transformations
ECE Electric Drives Topic 4: Modeling of Induction Motor using qd0 Transformations Spring 2004.
1 In this lecture, a model based observer and a controller will be designed to a single-link robot.
ELECTRIC DRIVES Ion Boldea S.A.Nasar 1998 Electric Drives.
A Typical Feedback System
Topic 5: Dynamic Simulation of Induction Motor Spring 2004 ECE Electric Drives.
עקיבה אחר מטרה נעה Stable tracking control method for a mobile robot מנחה : ולדיסלב זסלבסקי מציגים : רונן ניסים מרק גרינברג.
February 24, Final Presentation AAE Final Presentation Backstepping Based Flight Control Asif Hossain.
ECE 3355 Electronics Lecture Notes Set 4 -- Version 21
The MAGLEV Alternative
1 © Alexis Kwasinski, 2011 DC micro-grids comprise cascade distributed power architectures – converters act as interfaces Point-of-load converters present.
In Engineering --- Designing a Pneumatic Pump Introduction System characterization Model development –Models 1, 2, 3, 4, 5 & 6 Model analysis –Time domain.
Vector Control of Induction Machines
A Shaft Sensorless Control for PMSM Using Direct Neural Network Adaptive Observer Authors: Guo Qingding Luo Ruifu Wang Limei IEEE IECON 22 nd International.
A Mathematical Analysis of a Sun Tracking Circuit for Photovoltaic Systems Dr. S. Louvros and Prof. S. Kaplanis T.E.I. of Patra, Greece.
Ch. 6 Single Variable Control
Linear System Theory Instructor: Zhenhua Li Associate Professor Mobile : School of Control Science and Engineering, Shandong.
1  (x) f(x,u) u x f(x,  (x) x Example: Using feed-forward, what should be canceled?
1 An FPGA-Based Novel Digital PWM Control Scheme for BLDC Motor Drives 學生 : 林哲偉 學號 :M 指導教授 : 龔應時 IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL.
FULL STATE FEEDBAK CONTROL:
Lecture 14: Stability and Control II Reprise of stability from last time The idea of feedback control Remember that our analysis is limited to linear systems.
Sliding Mode Control of PMSM Drives Subject to Torsional Oscillations in the Mechanical Load Jan Vittek University of Zilina Slovakia Stephen J Dodds School.
Student : YI-AN,CHEN 4992C085 IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 22, NO. 2, MARCH 2014.
1 In this lecture we will compare two linearizing controller for a single-link robot: Linearization via Taylor Series Expansion Feedback Linearization.
Sensorless Sliding-Mode Control of Induction Motors Using Operating Condition Dependent Models 教 授: 王明賢 學 生: 謝男暉 南台科大電機系.
Time-Varying Angular Rate Sensing for a MEMS Z-Axis Gyroscope Mohammad Salah †, Michael McIntyre †, Darren Dawson †, and John Wagner ‡ Mohammad Salah †,
Lecture #16 Nonlinear Supervisory Control João P. Hespanha University of California at Santa Barbara Hybrid Control and Switched Systems.
To clarify the statements, we present the following simple, closed-loop system where x(t) is a tracking error signal, is an unknown nonlinear function,
Self-Sensing Active Magnetic Dampers for Vibration Control
Modified by Albert W.J. Hsue,
Progress in identification of damping: Energy-based method with incomplete and noisy data Marco Prandina University of Liverpool.
23.5 Self-Induction When the switch is closed, the current does not immediately reach its maximum value Faraday’s Law can be used to describe the effect.
Speed-Sensorless Estimation for Induction motors using Extended Kalman Filters 教 授: 龔應時 學 生: 楊政達 Murat Barut; Seta Bogosyan; Metin Gokasan; Industrial.
State Observer (Estimator)
1 Lecture 15: Stability and Control III — Control Philosophy of control: closed loop with feedback Ad hoc control thoughts Controllability Three link robot.
Professor : Ming – Shyan Wang Department of Electrical Engineering Southern Taiwan University Thesis progress report Sensorless Operation of PMSM Using.
Disturbance rejection control method
(COEN507) LECTURE III SLIDES By M. Abdullahi
EKT 441 MICROWAVE COMMUNICATIONS CHAPTER 3: MICROWAVE NETWORK ANALYSIS (PART 1)
-magnetic levitation. By Tiffany Albertson. Maglev trains A few countries are using powerful electromagnets to develop high- speed trains, called maglev.
1 Chapter 3 State Variable Models The State Variables of a Dynamic System The State Differential Equation Signal-Flow Graph State Variables The Transfer.
Control Engineering. Introduction What we will discuss in this introduction: – What is control engineering? – What are the main types of control systems?
A SEMINAR ON MAGLEV.
Intelligent Robot Lab Pusan National University Intelligent Robot Lab Chapter 7. Forced Response Errors Pusan National University Intelligent Robot Laboratory.
Professor Mukhtar ahmad Senior Member IEEE Aligarh Muslim University
Han Ho Choi, Member, IEEE, Nga Thi-Tuy Vu, and Jin-Woo Jung IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 59, NO. 8, AUGUST 2012,pp /9/241.
SPEED CONTROL OF AN INDUCTION MOTOR DRIVE USING INDIRECT VECTOR CONTROL METHOD Presented by: Milred Millan Oram Regd. No: Branch: EE-A Guided.
CNC FEED DRIVES Akhil Krishnan G M.Tech 1. CONTENTS 1.Introduction 2.Requirements of CNC feed drives 3.Servo motor 3.1 Servo drive control 3.2 Components.
CNC FEED DRIVES.
7. Modeling of Electromechanical Systems
A few illustrations on the Basic Concepts of Nonlinear Control
MESB374 System Modeling and Analysis Transfer Function Analysis
Arbin Ebrahim and Dr. Gregory Murphy University of Alabama
Autonomous Cyber-Physical Systems: Dynamical Systems
Modern Control Systems (MCS)
Digital and Non-Linear Control
Chapter 25 Elements of Electromechanical Energy Conversion.
Chapter 3 Modeling in the Time Domain
Chapter 7 Inverse Dynamics Control
CHAPTER 59 TRANSISTOR EQUIVALENT CIRCUITS AND MODELS
Presentation transcript:

ECE 893 Industrial Applications of Nonlinear Control Dr ECE 893 Industrial Applications of Nonlinear Control Dr. Ugur Hasirci Clemson University, Electrical and Computer Engineering Department Spring 2013 This lecture presents Backstepping controller design for another industrial example, a magnetic levitation train. The control design procedure to be presented in this lecture provides some additional design tools: (i) Nonlinear Damping, and (ii) A very simple model based observer design.

ECE 893 Industrial Applications of Nonlinear Control Dr ECE 893 Industrial Applications of Nonlinear Control Dr. Ugur Hasirci Clemson University, Electrical and Computer Engineering Department Spring 2013 Before the design, I would like to remind that the experimental setup is ready to test. By using the guide posted at course website, please install the software set, make the required laptop configuration, and then go to Riggs 25 (passcode is 1495#). At workstation 3, you will find the experimental setup. Connect the ethernet cable to your laptop (host computer), launch xPC Target Explorer, upload analog_loopback.mdl file, build it, and run the model. As you remember, this mdl file sends a sin signal to the Quanser Q4 analog output port. If you see a sin wave at the target PC monitor, then all your installations are ok !

ECE 893 Industrial Applications of Nonlinear Control Dr ECE 893 Industrial Applications of Nonlinear Control Dr. Ugur Hasirci Clemson University, Electrical and Computer Engineering Department Spring 2013 Magnetic levitation (maglev) trains present a powerful alternative to land, air, and classical rail transportation systems. Because of the friction between wheel and rail, conventional trains have speed limitations, operate at high noise levels and require frequent maintenance. Maglev trains replace wheel by electromagnets and produce the propulsion force without any contact. This motivates researchers to investigate some novel maglev topologies to increase the ride quality and to decrease the cost of the overall system.

ECE 893 Industrial Applications of Nonlinear Control Dr ECE 893 Industrial Applications of Nonlinear Control Dr. Ugur Hasirci Clemson University, Electrical and Computer Engineering Department Spring 2013 A novel topology for maglev systems was patented by Levi and Zabar. This system uses only one air-cored tubular linear induction motor to produce levitation, propulsion and guidance forces simultaneously . Specifically, we design, implement, test and control the newly proposed maglev system in this study. Main aim of the study is to prove that the experimental performance of this maglev system is satisfactory to use it in a commercial application.

Fig. 1. Proposed maglev system with energization from the wayside. ECE 893 Industrial Applications of Nonlinear Control Dr. Ugur Hasirci Clemson University, Electrical and Computer Engineering Department Spring 2013 The motor has two main parts as in a classical rotary induction motor: the primary; which consists of the drive coils, and the secondary; which consists of an aluminum slit sleeve. Drive coils can be placed either on the track or onboard the vehicle. Fig. 1 shows the proposed maglev system with energization from the wayside, and Fig. 2 shows the other configuration of the proposed system with energization onboard the vehicle. Fig. 1. Proposed maglev system with energization from the wayside. (1-vehicle, 2-drive coils, 3-aluminum sleeve, 4-support) Fig. 2. Proposed maglev system with energization onboard the vehicle. (1-vehicle, 2-drive coils, 3-aluminum sleeve, 4-support)

ECE 893 Industrial Applications of Nonlinear Control Dr ECE 893 Industrial Applications of Nonlinear Control Dr. Ugur Hasirci Clemson University, Electrical and Computer Engineering Department Spring 2013 Mechatronics Forum Dr. Ugur Hasirci Clemson University, Electrical and Computer Engineering Department Spring 2013 Main Advantages: This new system produces levitation, propulsion and guidance forces simultaneously by using only one motor. It is not needed to control the levitation and guidance forces because the restoring force centers the moving part, as will be proved experimentally in the following.

Drive Coils Entire System Acceleration Sensor ECE 893 Industrial Applications of Nonlinear Control Dr. Ugur Hasirci Clemson University, Electrical and Computer Engineering Department Spring 2013 Mechatronics Forum Dr. Ugur Hasirci Clemson University, Electrical and Computer Engineering Department Spring 2013 Drive Coils Entire System Acceleration Sensor

ECE 893 Industrial Applications of Nonlinear Control Dr ECE 893 Industrial Applications of Nonlinear Control Dr. Ugur Hasirci Clemson University, Electrical and Computer Engineering Department Spring 2013

ECE 893 Industrial Applications of Nonlinear Control Dr ECE 893 Industrial Applications of Nonlinear Control Dr. Ugur Hasirci Clemson University, Electrical and Computer Engineering Department Spring 2013

ECE 893 Industrial Applications of Nonlinear Control Dr ECE 893 Industrial Applications of Nonlinear Control Dr. Ugur Hasirci Clemson University, Electrical and Computer Engineering Department Spring 2013

ECE 893 Industrial Applications of Nonlinear Control Dr ECE 893 Industrial Applications of Nonlinear Control Dr. Ugur Hasirci Clemson University, Electrical and Computer Engineering Department Spring 2013

ECE 893 Industrial Applications of Nonlinear Control Dr ECE 893 Industrial Applications of Nonlinear Control Dr. Ugur Hasirci Clemson University, Electrical and Computer Engineering Department Spring 2013 State-Space Model where ids and iqs are stator current components on d and q-axis, λds and λqs depict rotor flux components on d and q-axis, V is linear velocity, Rs is stator resistance per phase, Ls and Lr represent stator and rotor inductances per phase, Lm is magnetizing inductance per phase, p denotes number of poles, h is pole pitch, σ depicts leakage coefficient, Tr is rotor time constant, Kf represents force constant, FL depicts load force, M is the total mass of the moving part, B denotes viscous friction coefficient, and finally, Vd and Vq are stator voltages on d and q-axis.

ECE 893 Industrial Applications of Nonlinear Control Dr ECE 893 Industrial Applications of Nonlinear Control Dr. Ugur Hasirci Clemson University, Electrical and Computer Engineering Department Spring 2013 To simplify the control design procedure, system dynamics given can be written in a more compact form as Control problem can be defined as follow; drive the linear velocity x5 to a desired velocity profile x5d while the rotor flux components x3 and x4 are unmeasurable. It is assumed that all parameters related electrical and mechanical subsystems are known. To determine the performance of the controller to be designed, an error signal can be defined as

This implies the state estimation error system will be ECE 893 Industrial Applications of Nonlinear Control Dr. Ugur Hasirci Clemson University, Electrical and Computer Engineering Department Spring 2013 Due to the states x3 and x4 (rotor flux components) are unmeasurable, let design a model based observer as Observer where and are the estimates of unmeasurable states, x3 and x4. Define the state estimation errors as This implies the state estimation error system will be

ECE 893 Industrial Applications of Nonlinear Control Dr ECE 893 Industrial Applications of Nonlinear Control Dr. Ugur Hasirci Clemson University, Electrical and Computer Engineering Department Spring 2013 To show the stability of this observer, following Lyapunov function can be used: State estimation errors go to zero exponentially, and thus we can use estimated values of this unmeasurable states during the control design. ■

Nonlinear Damping Term ECE 893 Industrial Applications of Nonlinear Control Dr. Ugur Hasirci Clemson University, Electrical and Computer Engineering Department Spring 2013 By going back to the control design, we investigate the error system dynamics; If x3 and x4 were available for measurement, one could choose the produced electromechanical force Fe=c2(x1x4-x2x3) as the virtual control input and initiate directly the backstepping procedure. Since these state variables are not measured but estimated, observer backstepping should be applied. Nonlinear Damping Term The nonlinear damping term defined as where d1 is the damping coefficient, will be used to damp the state estimation errors and while designing the control input signals.

In this step, a new error variable (a new coordinate) is defined as ECE 893 Industrial Applications of Nonlinear Control Dr. Ugur Hasirci Clemson University, Electrical and Computer Engineering Department Spring 2013 In this step, a new error variable (a new coordinate) is defined as Then the final expression for the error system dynamics will be By following the standard backstepping procedure, let’s backstep on z1. To reach the control input signals u1 and u2, dynamics of z1 must be investigated. Note that z1 is a function of and . By using the partial differentiation, the expression for z1 dynamics is obtained as where are the factors of related signals which contain all measurable states and known parameters. For simplicity, their explicit expressions are written here due to their length. Then the feedback rule is where d2 is the second damping coefficient and Kz1 is a control gain. If control inputs are designed as above, the final dynamics for z1 will be

where ψd is the desired flux. Investigating ε dynamics yields ECE 893 Industrial Applications of Nonlinear Control Dr. Ugur Hasirci Clemson University, Electrical and Computer Engineering Department Spring 2013 A very important subtask in meeting the control objective is to ensure a bounded rotor flux, i.e., rotor flux should be forced to track a bounded signal. Since x3 and x4 are not measurable, we replace them by their estimates and define a new error variable as where ψd is the desired flux. Investigating ε dynamics yields Once again, we have to choose a virtual control input. One should select the term as virtual control input, then add and subtract a second stabilizing function α2 to the right hand side of above equation as shown in the following.

 By combining two expressions for control input signals, which are ECE 893 Industrial Applications of Nonlinear Control Dr. Ugur Hasirci Clemson University, Electrical and Computer Engineering Department Spring 2013 By combining two expressions for control input signals, which are we can write the control input signal in vector matrix form as 

The designed control law is well defined if the matrix ECE 893 Industrial Applications of Nonlinear Control Dr. Ugur Hasirci Clemson University, Electrical and Computer Engineering Department Spring 2013 The designed control law is well defined if the matrix is globally invertible. Before presenting the stability analysis, let me remind the final dynamics of all error variables. We will need them to complete the stability analysis.

Why do we add this term to the final Lyapunov function? ECE 893 Industrial Applications of Nonlinear Control Dr. Ugur Hasirci Clemson University, Electrical and Computer Engineering Department Spring 2013 To show the stability of the overall system, let’s select the Lyapunov function as follows: Why do we add this term to the final Lyapunov function? We already showed the exponential stability of the observation errors !!! The answer is hidden in another characteristic behavior of the nonlinear systems: ------------- FINITE ESCAPE TIME -------------- Please see the following side note, which is a well-known demonstration of Finite Escape Time.

Then the solution of the differential equation is ECE 893 Industrial Applications of Nonlinear Control Dr. Ugur Hasirci Clemson University, Electrical and Computer Engineering Department Spring 2013 Consider the system where is the observation error for unmeasurable state of the system, . Assume that we already showed the observation error goes to zero exponentially, i.e., Then the solution of the differential equation is which escape to infinity in a finite time, which is Consequently, even though the observer is exponentially stable, the overall system might become unstable in nonlinear systems. This is the reason to put the observation errors into the final Lyapunov function, and also to use Nonlinear Damping terms in the design, to damp the terms related observation errors. !!!!!! ■

Back to the stability analysis: ECE 893 Industrial Applications of Nonlinear Control Dr. Ugur Hasirci Clemson University, Electrical and Computer Engineering Department Spring 2013 Back to the stability analysis: GAS of the E.P. is achieved.

ECE 893 Industrial Applications of Nonlinear Control Dr ECE 893 Industrial Applications of Nonlinear Control Dr. Ugur Hasirci Clemson University, Electrical and Computer Engineering Department Spring 2013 Tracking Error

Your guns before today Backstepping Your guns as of today Backstepping ECE 893 Industrial Applications of Nonlinear Control Dr. Ugur Hasirci Clemson University, Electrical and Computer Engineering Department Spring 2013 Your guns before today Backstepping Your guns as of today Backstepping Nonlinear Damping You will have lots of guns at the end of the semester to control the systems in nature.