Controllability and Observability

Slides:



Advertisements
Similar presentations
Vector Spaces A set V is called a vector space over a set K denoted V(K) if is an Abelian group, is a field, and For every element vV and K there exists.
Advertisements

Time Response and State Transition Matrix
PROCESS MODELLING AND MODEL ANALYSIS © CAPE Centre, The University of Queensland Hungarian Academy of Sciences Analysis of Dynamic Process Models C13.
Goodwin, Graebe, Salgado ©, Prentice Hall 2000 Chapter 17 Linear State Space Models.
Properties of State Variables
Gauss – Jordan Elimination Method: Example 2 Solve the following system of linear equations using the Gauss - Jordan elimination method Slide 1.
Solving Systems of Linear Equations Part Pivot a Matrix 2. Gaussian Elimination Method 3. Infinitely Many Solutions 4. Inconsistent System 5. Geometric.
Similarity Transformation. Basic Sets Use a new basis set for state space. Obtain the state-space matrices for the new basis set. Similarity transformation.
Eigenvalues and Eigenvectors
THE DIMENSION OF A VECTOR SPACE
1 數位控制(十一). 2 Methods of Pole Placement Method 1: Simple substitution Method 2: Use of Transformation matrix Method 3: Ackermann’s formula Method 4: Use.
LINEAR CONTROL SYSTEMS Ali Karimpour Assistant Professor Ferdowsi University of Mashhad.
5.II. Similarity 5.II.1. Definition and Examples
Lecture 03 Canonical forms.
Multivariable Control Systems
On the Realization Theory of Polynomial Matrices and the Algebraic Structure of Pure Generalized State Space Systems A.I.G. Vardulakis, N.P. Karampetakis.
Modern Control Systems1 Lecture 07 Analysis (III) -- Stability 7.1 Bounded-Input Bounded-Output (BIBO) Stability 7.2 Asymptotic Stability 7.3 Lyapunov.
Chapter 1 Systems of Linear Equations
5 5.1 © 2012 Pearson Education, Inc. Eigenvalues and Eigenvectors EIGENVECTORS AND EIGENVALUES.
Control Systems and Adaptive Process. Design, and control methods and strategies 1.
Digital Control Systems
1 數位控制(十). 2 Continuous time SS equations 3 Discretization of continuous time SS equations.
SYSTEMS OF LINEAR EQUATIONS
Autumn 2008 EEE8013 Revision lecture 1 Ordinary Differential Equations.
Linear Algebra (Aljabar Linier) Week 10 Universitas Multimedia Nusantara Serpong, Tangerang Dr. Ananda Kusuma
A matrix equation has the same solution set as the vector equation which has the same solution set as the linear system whose augmented matrix is Therefore:
Section 1.2 Gaussian Elimination. REDUCED ROW-ECHELON FORM 1.If a row does not consist of all zeros, the first nonzero number must be a 1 (called a leading.
Chapter 1 Linear Algebra S 2 Systems of Linear Equations.
6. Elementary Canonical Forms How to characterize a transformation?
Chapter 1 Systems of Linear Equations Linear Algebra.
Lesson 7 Controllability & Observability Linear system 1. Analysis.
The Canonical Form and Null Spaces Lecture III. The Canonical Form ä A canonical form is a solution to an underidentified system of equations. ä For example.
5 5.1 © 2016 Pearson Education, Ltd. Eigenvalues and Eigenvectors EIGENVECTORS AND EIGENVALUES.
4 4.5 © 2016 Pearson Education, Inc. Vector Spaces THE DIMENSION OF A VECTOR SPACE.
Def: A matrix A in reduced-row-echelon form if 1)A is row-echelon form 2)All leading entries = 1 3)A column containing a leading entry 1 has 0’s everywhere.
Lecture 3 Modeling (ii) –State Space approach
EE611 Deterministic Systems Controllability and Observability Discrete Systems Kevin D. Donohue Electrical and Computer Engineering University of Kentucky.
EE611 Deterministic Systems Multiple-Input Multiple-Output (MIMO) Feedback Kevin D. Donohue Electrical and Computer Engineering University of Kentucky.
LINEAR CONTROL SYSTEMS Ali Karimpour Associate Professor Ferdowsi University of Mashhad.
Eigenvalues and Eigenvectors
Chapter 12 Design via State Space <<<4.1>>>
Advanced Control Systems (ACS)
Eigenvalues and Eigenvectors
Pole Placement and Decoupling by State Feedback
§2-3 Observability of Linear Dynamical Equations
Pole Placement and Decoupling by State Feedback
§3-3 realization for multivariable systems
CHE 391 T. F. Edgar Spring 2012.
Linear Algebra: Matrices and Vectors – Part 3
Feedback Control Systems (FCS)
Modern Control Systems (MCS)
§2-3 Observability of Linear Dynamical Equations
Static Output Feedback and Estimators
Digital Control Systems
§3-2 Realization of single variable systems
State Space Method.
Linear Algebra Lecture 3.
8. Stability, controllability and observability
§2-2 Controllability of Linear Dynamical Equations
Equivalent State Equations
Chapter 3 Canonical Form and Irreducible Realization of Linear Time-invariant Systems.
Homework 3: Transfer Function to State Space
Linear State Space Models
Homework 3: Transfer Function to State Space
§3-2 Realization of single variable systems
Goodwin, Graebe, Salgado ©, Prentice Hall 2000 Chapter 17 Linear State Space Models.
Linear Algebra: Matrix Eigenvalue Problems – Part 2
THE DIMENSION OF A VECTOR SPACE
Linear Algebra Lecture 28.
Eigenvalues and Eigenvectors
Presentation transcript:

Controllability and Observability Lecture 06 Analysis (II) Controllability and Observability 6.1 Controllability and Observability 6.2 Kalman Canonical Decomposition 6.3 Pole-zero Cancellation in Transfer Function 6.4 Minimum Realization Modern Contral Systems

Modern Contral Systems Motivation1 uncontrollable controllable Modern Contral Systems

Modern Contral Systems Controllability and Observability Plant: Definition of Controllability A system is said to be (state) controllable at time , if there exists a finite such for any and any , there exist an input that will transfer the state to the state at time , otherwise the system is said to be uncontrollable at time . Modern Contral Systems

Modern Contral Systems Controllability Matrix Example: An Uncontrollable System ※ State is uncontrollable. Modern Contral Systems

Modern Contral Systems Proof of controllability matrix Initial condition Modern Contral Systems

Modern Contral Systems Motivation2 observable unobservable Modern Contral Systems

Modern Contral Systems Definition of Observability A system is said to be (completely state) observable at time , if there exists a finite such that for any at time , the knowledge of the input and the output over the time interval suffices to determine the state , otherwise the system is said to be unobservable at . Modern Contral Systems

Modern Contral Systems Observability Matrix Example: An Unobservable System ※ State is unobservable. Modern Contral Systems

Modern Contral Systems Proof of observability matrix Inputs & outputs Modern Contral Systems

Modern Contral Systems Example Plant: Hence the system is both controllable and observable. Modern Contral Systems

Modern Contral Systems Theorem I Controllable canonical form Controllable Theorem II Observable canonical form Observable Modern Contral Systems

Modern Contral Systems example Controllable canonical form Observable canonical form Modern Contral Systems

Modern Contral Systems Linear system 1. Analysis Theorem III Jordan form Jordan block Least row has no zero row First column has no zero column Modern Contral Systems

Modern Contral Systems Example If uncontrollable unobservable Modern Contral Systems

Modern Contral Systems

Modern Contral Systems controllable observable In the previous example controllable unobservable Modern Contral Systems

Modern Contral Systems Example L.I. L.I. L.D. L.I. Modern Contral Systems

Modern Contral Systems Kalman Canonical Decomposition Diagonalization: All the Eigenvalues of A are distinct, i.e. There exists a coordinate transform (See Sec. 4.4) such that System in z-coordinate becomes Homogeneous solution of the above state equation is Modern Contral Systems

Modern Contral Systems How to construct coordinate transformation matrix for diagonalization All the Eigenvalues of A are distinct, i.e. The coordinate matrix for diagonalization Consider diagonalized system Modern Contral Systems

Modern Contral Systems Transfer function is H(s) has pole-zero cancellation. ∑ Modern Contral Systems

Modern Contral Systems Kalman Canonical Decomposition Modern Contral Systems

Modern Contral Systems Kalman Canonical Decomposition: State Space Equation (5.X) Modern Contral Systems

Modern Contral Systems Example Plant: is uncontrollable. is unobservable. The same reasoning may be applied to mode 1 and 2. Modern Contral Systems

Modern Contral Systems Pole-zero Cancellation in Transfer Function From Sec. 5.2, state equation may be transformed to Hence, the T.F. represents the controllable and observable parts of the state variable equation. Modern Contral Systems

Modern Contral Systems Example Plant: Transfer Function Modern Contral Systems

Modern Contral Systems Example 5.6 Plant: Transfer Function Modern Contral Systems

Modern Contral Systems Minimum Realization Realization: Realize a transfer function via a state space equation. Example Realization of the T.F. Method 1: Method 2: ※There is infinity number of realizations for a given T.F. . Modern Contral Systems

Modern Contral Systems Minimum realization: Realize a transfer function via a state space equation with elimination of its uncontrollable and unobservable parts. Example 5.8 Realization of the T.F. Modern Contral Systems