دانشگاه صنعتي اميركبير دانشكده مهندسي پزشكي State-space Models with Embedded Integrator استاد درس دكتر فرزاد توحيدخواه آبان 1388 کنترل پيش بين-دکتر توحيدخواه.

Slides:



Advertisements
Similar presentations
Neural Simulation and Control.. Simulation Input/Output models Proces u(k) y(k+d) d(k) The NARMA model:
Advertisements

Robotics Research Laboratory 1 Chapter 6 Design Using State-Space Methods.
دانشگاه صنعتي اميركبير دانشكده مهندسي پزشكي Constraints in MPC کنترل پيش بين-دکتر توحيدخواه.
Use of Kalman filters in time and frequency analysis John Davis 1st May 2011.
Properties of State Variables
Design of LFC using Optimal Control Theory The optimal controller is designed to minimize the quadratic performance index of the following form For linear.
Observers and Kalman Filters
A Typical Feedback System
Kalman’s Beautiful Filter (an introduction) George Kantor presented to Sensor Based Planning Lab Carnegie Mellon University December 8, 2000.
President UniversityErwin SitompulModern Control 5/1 Dr.-Ing. Erwin Sitompul President University Lecture 5 Modern Control
AC modeling of quasi-resonant converters Extension of State-Space Averaging to model non-PWM switches Use averaged switch modeling technique: apply averaged.
ME 746 Spring Dynamic Models Differential Equations in State-Variable Form.
Frequency Response of Discrete-time LTI Systems Prof. Siripong Potisuk.
Digital Control Systems
1 數位控制(十). 2 Continuous time SS equations 3 Discretization of continuous time SS equations.

Slam is a State Estimation Problem. Predicted belief corrected belief.
Combined State Feedback Controller and Observer
Introduction to estimation theory Seoul Nat’l Univ.
Dynamic analysis of switching converters
Lecture 11: Kalman Filters CS 344R: Robotics Benjamin Kuipers.
Computer Vision Group Prof. Daniel Cremers Autonomous Navigation for Flying Robots Lecture 6.2: Kalman Filter Jürgen Sturm Technische Universität München.
4.5 Solving Systems using Matrix Equations and Inverses.
3.5 – Solving Systems of Equations in Three Variables.
Probabilistic Robotics Bayes Filter Implementations Gaussian filters.
Sliding Mode Control of PMSM Drives Subject to Torsional Oscillations in the Mechanical Load Jan Vittek University of Zilina Slovakia Stephen J Dodds School.
1 In this lecture we will compare two linearizing controller for a single-link robot: Linearization via Taylor Series Expansion Feedback Linearization.
TRANSFER FUNCTION FORMULATION OF ANALYSIS AND DESIGN PROBLEMS FOR DISCRETE/CONTINUOUS CONTROL THEORY by Bardhyl Prishtina Thesis Advisor: Dr. C.D. Johnson.
1 Chapter 2 1. Parametric Models. 2 Parametric Models The first step in the design of online parameter identification (PI) algorithms is to lump the unknown.
Multiple Model approach to Multi-Parametric Model Predictive Control of a Nonlinear Process a simulation case study Boštjan Pregelj, Samo Gerkšič Jožef.
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,
Human-Computer Interaction Kalman Filter Hanyang University Jong-Il Park.
ME375 Handouts - Spring 2002 Root Locus Method.
دانشگاه صنعتي اميركبير دانشكده مهندسي پزشكي استاد درس دكتر فرزاد توحيدخواه بهمن 1389 کنترل پيش بين-دکتر توحيدخواه MPC Stability-1.
Learning Theory Reza Shadmehr Optimal feedback control stochastic feedback control with and without additive noise.
Low Level Control. Control System Components The main components of a control system are The plant, or the process that is being controlled The controller,
Outline Introduction Reaction Wheels Modelling Control System Real Time Issues Questions Conclusions.
Robotics Research Laboratory 1 Chapter 7 Multivariable and Optimal Control.
1 Chapter 11 Compensator Design When the full state is not available for feedback, we utilize an observer. The observer design process is described and.
An Introduction to Kalman Filtering by Arthur Pece
Professor Walter W. Olson Department of Mechanical, Industrial and Manufacturing Engineering University of Toledo Observers/Estimators …  bnbn b n-1 b2b2.
دانشگاه صنعتي اميركبير
An Introduction To The Kalman Filter By, Santhosh Kumar.
دانشگاه صنعتي اميركبير دانشكده مهندسي پزشكي Constraints in MPC-2 کنترل پيش بين-دکتر توحيدخواه.
كنترل غير خطي جلسه دوم : شروع بحث نماي فاز (phase plane) سجاد ازگلي.
دانشگاه صنعتي اميركبير دانشكده مهندسي پزشكي State-space Models with Embedded Integrator Ref : Model Predictive Control System Design and Implementation.
State Observer (Estimator)
Model of Reluctance Synchronous Motor
Nonlinear State Estimation
Chapter 6 Root-Locus Analysis 6.1 Introduction - In some systems simple gain adjustment may move the closed- loop poles to desired locations. Then the.
دانشگاه صنعتي اميركبير دانشكده مهندسي پزشكي استاد درس دكتر فرزاد توحيدخواه بهمن 1389 کنترل پيش بين-دکتر توحيدخواه MPC Stability-2.
Cameron Rowe.  Introduction  Purpose  Implementation  Simple Example Problem  Extended Kalman Filters  Conclusion  Real World Examples.
دانشگاه صنعتي اميركبير دانشكده مهندسي پزشكي Constraints in MPC-2 کنترل پیش بین- دکتر توحیدخواه.
Root Locus. Closed-loop control system with a variable parameter K.
(COEN507) LECTURE III SLIDES By M. Abdullahi
DEPARTMENT OF MECHANICAL TECHNOLOGY VI -SEMESTER AUTOMATIC CONTROL 1 CHAPTER NO.6 State space representation of Continuous Time systems 1 Teaching Innovation.
Flexural stiffness design by integer linear programming.
2.4 – Solving Equations with the Variable on Each Side.
DSP-CIS Part-III : Optimal & Adaptive Filters Chapter-9 : Kalman Filters Marc Moonen Dept. E.E./ESAT-STADIUS, KU Leuven
Zhaoxia Fu, Yan Han Measurement Volume 45, Issue 4, May 2012, Pages 650–655 Reporter: Jing-Siang, Chen.
CIS 540 Principles of Embedded Computation Spring Instructor: Rajeev Alur
LQR Linear Quadratic Regulator
Kalman’s Beautiful Filter (an introduction)
دانشگاه صنعتي اميركبير دانشكده مهندسي پزشكي استاد درس دكتر فرزاد توحيدخواه بهمن 1389 MPC Stability-1 کنترل پيش بين-دکتر توحيدخواه 3.
دانشگاه صنعتي اميركبير
دانشگاه صنعتي اميركبير
Bayes and Kalman Filter
دانشگاه صنعتي اميركبير
دانشگاه صنعتي اميركبير
Presentation transcript:

دانشگاه صنعتي اميركبير دانشكده مهندسي پزشكي State-space Models with Embedded Integrator استاد درس دكتر فرزاد توحيدخواه آبان 1388 کنترل پيش بين-دکتر توحيدخواه

Taking a difference operation on both sides:

New state variable vector:

Example 1:

Characteristic equation:

Prediction of State and Output Variables

The future control trajectory: Future state variables:

Optimization

Example 2:

We can verify this by increasing Nc to 9, while maintaining r ω = 10 First four parameters in U are slightly different from the previous case

Receding Horizon Control

Example 4:

Receding horizon control

Closed-loop Control System

Standard form of linear time-invariant state feedback

Example 5 closed-loop feedback gain matrices in Ex. 2

State Estimation

Basic Ideas About an Observer

Example 7: Linearized equation of motion of a simple pendulum

(a) Estimation without observer

Model alone is not sufficient to predict the angle of the pendulum

(b) Estimation with observer

Example 8DC motor

Discrete-time model is observable ?

Second pole at λ = 1 cannot be moved no matter what choice we make for j 2

Kalman Filter

State Estimate Predictive Control

Closed-loop state-space equation:

Example 9. The augmented model for a double integrated plant (see Example 1) is given by