State Observer Design for a Class of Takagi-Sugeno Discrete-Time Descriptor Systems
|
|
- Darrell Logan
- 7 years ago
- Views:
Transcription
1 Applied Mathematical Sciences, Vol. 9, 2015, no. 118, HIKARI Ltd, State Observer Design for a Class of Takagi-Sugeno Discrete-Time Descriptor Systems Ilham Hmaiddouch ECPI, Departement GE, ENSEM University Hassan II of Casablanca B.P 8118,Oasis, Casablanca Morocco Boutayna Bentahra ECPI, Departement GE, ENSEM University Hassan II of Casablanca B.P 8118,Oasis, Casablanca Morocco Abdellatif El Assoudi ECPI, Departement GE, ENSEM University Hassan II of Casablanca B.P 8118,Oasis, Casablanca Morocco Jalal Soulami ECPI, Departement GE, ENSEM University Hassan II of Casablanca B.P 8118,Oasis, Casablanca Morocco El Hassane El Yaagoubi ECPI, Departement GE, ENSEM University Hassan II of Casablanca B.P 8118,Oasis, Casablanca Morocco Copyright c 2015 Ilham Hmaiddouch et al. This article is distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
2 5872 I. Hmaiddouch, B. Bentahra, A. El Assoudi, J. Soulami and E. El Yaagoubi Abstract This paper deals with the design of explicit observers for a class of discrete-time dynamical implicit systems described by Takagi-Sugeno TS model in the two cases where the premise variable are measurable and the premise variables are unmeasurable. The idea of the proposed approach is based on the singular value decomposition. The convergence of the state estimation error is studied using the Lyapunov theory and the stability conditions are given in terms of Linear Matrix Inequalities LMIs. Finally, an example is given to illustrate the proposed approach. Keywords: Takagi-Sugeno model, discrete-time system, descriptor system, fuzzy observer, linear matrix inequality LMI, singular value decomposition 1 Introduction It is well known that descriptor systems variously called singular systems, implicit systems, or differential algebraic equations have been receiving a great deal of attention for many decades as a representation of dynamical systems [1], [2], [3]. This formulation includes both dynamic and static relations. Consequently this formalism is much more general than the usual one and can model physical constraints or impulsive behavior due to an improper part of the system. Note that many physical systems are naturally modeled as descriptor systems such as chemical, electrical and mechanical systems. The numerical simulation of such descriptor models usually combines an ODE numerical method together with an optimization algorithm. Over the two last decades, the nonlinear observer synthesis and its application for dynamical systems described by T-S fuzzy models [4], [5] has received a great deal of attention. For continuous and discrete-time nonlinear systems, many results have been reported in observer design [6], [7], [8], [9], [10]. Concerning nonlinear descriptor systems, several research work concerning the problem of observer design and applications exist in the literature see for instance [11], [12], [13], [14], [15], [16], [17], [18], [19]. Based on the singular value decomposition approach, the aim of this paper is to give a fuzzy observer design to a class of T-S discrete-time descriptor systems in the two cases where the premise variable are measurable and the premise variables are unmeasurable permitting to estimate the unknown state without the use of an optimization algorithm. The remainder of the paper is structured as follows. The class of studied systems is defined in section 2 and the main result about fuzzy observer design for T-S discrete-time descriptor systems in the two cases where the premise variable are measurable and the premise variables are unmeasurable is exposed
3 State observer design for a class of T-S discrete-time descriptor systems 5873 in section 3. Section 4 is devoted to a numerical example to demonstrate the validity of our results. 2 Problem statement In this paper, the class of T-S discrete-time descriptor systems that we consider is in the following form: Ex k+1 = µ i ξ k A i x k + B i u k 1 y k = Cx k where x k R n is the state variable, u k R m is the control input, y k R p is the measured output. E R n n is constant matrix with ranke = r. A i R n n, B i R n m, C R p n are real known constant matrices. ξ k represent the premise variable. The µ i ξ k are the weighting functions that ensure the transition between the contribution of each sub model: { Exk+1 = A i x k + B i u k y k = Cx k 2 They depend on measurable or unmeasurable premise variables state of the system, and have the following properties: 0 µ i ξ k 1 µ i ξ k = 1 Then, before giving the main results, let us make the following hypotheses for each sub-model 2, i = 1,..., q: H1 E, A i is regular, i.e. detze A i 0 z C H2 All sub-models are impulse observable, i.e. E A i rank 0 E = n + ranke 0 C 3 H3 All sub-models are detectable, i.e. rank ze Ai C = n z C
4 5874 I. Hmaiddouch, B. Bentahra, A. El Assoudi, J. Soulami and E. El Yaagoubi E H4 rank C = n. Note that under hypothesis H4, there exist a non-singular matrix such that: a b c d { ae + bc = I ce + dc = Fuzzy observer design Based on the singular value decomposition, our aim in this section is to design an explicit fuzzy observer for T-S descriptor system 1 in the two cases where the premise variable are measurable and the premise variables are unmeasurable. 3.1 Case 1: Measurable premise variables In this subsection, the design of fuzzy observer for T-S descriptor system 1 with measurable premise variables is addressed. The proposed observer is in an explicit form, and is defined by the following equations: z k+1 = µ i ξ k N i z k + L 1i y k + L 2i y k + G i u k 5 ˆx k = z k + by k + Kdy k where ˆx k denote the estimated state vector, N i, L 1i, L 2i, G i and K are unknown matrices of appropriate dimensions, which must be determined such that ˆxt will asymptotically converge to xt. Denoting the state estimation error by: e k = x k ˆx k 6 Then by substituting 1, 4 and 5 into 6 we obtain: e k = a + KcEx k z k 7 It follows from 1 and 5 that the dynamic of this observer error is: e k+1 = µ i ξ k a + KcA i x k + B i u k µ i ξ k N i z k + L 1i y k + L 2i y k + G i u k 8
5 State observer design for a class of T-S discrete-time descriptor systems 5875 Using 7, equation 8 can be written as: e k+1 = µ i ξ k a + KcA i x k + B i u k + µ i ξ k N i e k µ i ξ k N i a + KcE + L 1i C + L 2i Cx k + G i u k Provided the matrices G i, K, L 1i, L 2i and N i satisfy: 9 N i a + KcE + L 1i C + L 2i C = a + KcA i 10 G i = a + KcB i 11 Then, from 4 and 10, we have: N i = a + KcA i L 2i C + N i b + Kd L 1i C 12 Take: L 1i = N i b + Kd 13 Then: N i = a + KcA i L 2i C 14 It follows system 9 is equivalent to: e k+1 = µ i ξ k N i e k 15 Theorem 3.1 : There exists an observer 5 for 1 if the hypotheses H1, H2, H3 and H4 hold and there exists symmetric positive matrices P, Q and W i for i = 1,..., q, verifying the following LMI: P P aa i + QcA i W i C T < 0 i = 1,..., q 16 P aa i + QcA i W i C P The observer gains N i, L 1i, L 2i, G i and K are given by: N i = a + P 1 QcA i P 1 W i C L 1i = a + P 1 QcA i P 1 W i Cb + P 1 Qd L 2i = P 1 W i G i = a + P 1 QcB i K = P 1 Q 17 where a, b, c and d are such that equation 4 is satisfied.
6 5876 I. Hmaiddouch, B. Bentahra, A. El Assoudi, J. Soulami and E. El Yaagoubi Proof of theorem 3.1 : To prove the convergence of the estimation error toward zero, let us consider the following quadratic Lyapunov function: V k = e T k P e k, P = P T > 0 18 Estimation error convergence is ensured if the following condition is guaranteed: V = V k+1 V k < 0 19 The variation of V k along the trajectory of 15 is given by: By using 15, 20 can be written as: V = The negativity of V is guaranteed if: V = e T k+1p e k+1 e T k P e k 20 µ i ξ k e T k [Ni T P N i P ]e k 21 N T i P N i P < 0 i {1,..., q} 22 By using 14, 22 can be written as: aa i + KcA i L 2i C T P aa i + KcA i L 2i C P < 0 i {1,..., q} 23 Thus, the LMI conditions of Theorem 3.1 can be obtained by using the Schur complement [20] and the following change of variables: { Q = P K W i = P L 2i 24 This completes the proof of theorem Case 2: Unmeasurable premise variables In this section, our aim is to design an explicit fuzzy observer for nonlinear descriptor system 1 with unmeasurable decision variables. The proposed observer is in the following form: z k+1 = µ i ˆξ k N i z k + L 1i y k + L 2i y k + G i u k 25 ˆx k = z k + by k + Kdy k where ˆx k denote the estimated state vector, N i, L 1i, L 2i, G i and K are unknown matrices of appropriate dimensions, which must be determined such that ˆx k
7 State observer design for a class of T-S discrete-time descriptor systems 5877 will asymptotically converge to x k. Let e k = x k ˆx k, then as in above subsection 3.1 see equations 6 to 14, the error dynamics is given by: e k+1 = Note that: µ i ˆξ k N i e k + µ i ξ k µ i ˆξ k a + KcA i x k + B i u k 26 µ i ξ k µ i ˆξ k A i = µ i ξ k µ i ˆξ k B i = Then, the equation 26 becomes: µ i ξ k µ j ˆξ k A i A j µ i ξ k µ j ˆξ k B i B j 27 e k+1 = µ i ˆξ k N i e k + µ i ξ k µ j ˆξ k a + Kc A ij x k + B ij u k 28 where A ij = A i A j and B ij = B i B j. Multiplying by µ i ξ k, equation 28 can be reduces to the equation: e k+1 = µ i ξ k µ j ˆξ k N j e k + Φ ij x k + Γ ij u k 29 where Φ ij = a + Kc A ij Γ ij = a + Kc B ij i, j {1,..., q} 30 Let us define the augmented state ē k = [e T k x T k ] T, we have: Ēē k+1 = µ i ξ k µ j ˆξ k A ij ē k + B ij u k 31 where Ē = A ij = B ij = I 0 0 E Nj Φ ij 0 A i Γij B i 32
8 5878 I. Hmaiddouch, B. Bentahra, A. El Assoudi, J. Soulami and E. El Yaagoubi Theorem 3.2 : There exists an observer 25 for 1 if the hypotheses H1, H2, H3 and H4 hold and there exists symmetric positive matrices P 1, P 2, matrices Q, R, W j, Z j and S j for j = 1,..., q, verifying the following LMI: S ij = m 11 m T 21 m T 31 m 21 m 22 m T 32 m 31 m 32 m 33 < 0 i, j {1,..., q} 33 where: m 11 = A T j a T P 1 aa j + A T j a T QcA j + A T j c T Q T aa j A T j a T W j C P 1 C T Wj T aa j + A T j c T RcA j A T j c T Sj T C C T S j ca j + C T Z j C m 22 = A T ija T P 1 a A ij + A T ija T Qc A ij + A T ijc T Q T a A ij + A T ijc T Rc A ij + A T i P 2 A i E T P 2 E m 33 = Bija T T P 1 a B ij + Bija T T Qc B ij + Bijc T T Q T a B ij + Bijc T T Rc B ij + Bi T P 2 B i m 21 = A T ija T P 1 aa j + A T ija T QcA j A T ija T W j C + A T ijc T Q T aa j + A T ijc T RcA j A T ijc T Sj T C m 31 = Bija T T P 1 aa j + Bija T T QcA j + Bijc T T Q T aa j Bija T T W j C + Bijc T T RcA j Bijc T T Sj T C m 32 = Bija T T P 1 a A ij + Bija T T Qc A ij + Bijc T T Q T a A ij + Bijc T T Rc A ij + Bi T P 2 A i 34 with a, b, c and d are such that equation 4 is satisfied. The observer gains N j, L 1j, L 2j, G j and K are given such that equations 11, 13, 14 and 42 are satisfied. Proof of theorem 3.2 : To prove the convergence of the estimation error toward zero, let us consider the following quadratic Lyapunov function: V k = ē T k ĒT P Ēē k, Ē T P Ē 0, P = P T > 0 35 with P = P1 0 0 P 2 36 Its difference V = V k+1 V k along the error dynamics 31 is given by: V = ē T k+1ēt P Ēē k+1 ē T k ĒT P Ēē k 37 By using 31, 37 can be written as: V = µ i ξ k µ j ˆξ k A ij ē k + B ij u k T P A ij ē k + B ij u k ē T k ĒT P Ēē k 38
9 State observer design for a class of T-S discrete-time descriptor systems 5879 Multiplying by V = µ i ξ k µ j ˆξ k, equation 38 can be reduces to the equation: µ i ξ k µ j ˆξ k [ ē T k u T k ] Sij [ ēk u k ] 39 where S ij = A T ij P A ij ĒT P Ē AT ijp B ij BijP T A ij BijP T B ij i, j {1,..., q} 40 The negativity of V is guaranteed if: S ij < 0 i, j {1,..., q} 41 Then, the use of the changes of variables: Q = P 1 K R = K T P 1 K W j = P 1 L 2j Z j = L T 2jP 1 L 2j S j = L T 2jP 1 K 42 and from 14, 30, 32 and 36 we establish the LMIs conditions given in 33 in the theorem 3.2. This completes the proof of theorem Numerical example In this section, we consider the following T-S discrete-time descriptor model with unmeasurable premise variables to illustrate the efficiency of the proposed fuzzy observer given by system 25: where A 1 = Ex k+1 = 4 h j x k A j x k + Bu k j=1 43 y k = Cx k , A 2 =
10 5880 I. Hmaiddouch, B. Bentahra, A. El Assoudi, J. Soulami and E. El Yaagoubi A 3 = E = , B = The membership functions are given by:, A 4 = , C = h 1 x k = 1 x2 4k 1x 2 4k h 2 x k = 1 x2 4k 11 + x 2 4k 5 16 h 3 x k = x2 4kx 2 4k h 4 x k = x2 4k1 + x 2 4k 5 16 A fuzzy observer for system 43 permitting to estimate the unknown states x 1k, x 2k, x 3k and x 4k can be designed using theorem 3.2. It takes the following form: 4 z k+1 = µ i ˆx k N i z k + L 1i y k + L 2i y k + G i u k 44 ˆx k = z k + by k + Kdy k where b and d satisfying the equation 4 are as follows: b = , d = In order to illustrate the performances of the fuzzy observer 44, we solve the LMIs given in the theorem 3.2. The observer gains N j, L 1j, L 2j, G j and K are given by: N 1 = N 2 =
11 State observer design for a class of T-S discrete-time descriptor systems 5881 L 11 = N 3 = N 4 = L 13 = L 21 = L 23 = G 1 = G 4 =, L 12 =, L 14 =, L 22 =, G 2 = , L 24 = , K = The initial conditions of the T-S model 43 are: x 0 = [ ] T , G 3 = The initial conditions of the fuzzy observer 44 are: ˆx 0 = [ ] T
12 5882 I. Hmaiddouch, B. Bentahra, A. El Assoudi, J. Soulami and E. El Yaagoubi The simulation results are given in figure 1 where the dotted lines denote the state variables estimated by the fuzzy observer 44. This simulation shows that the estimation states converge to their corresponding state variables. Figure 1 : : T-S model,... : Fuzzy observer
13 State observer design for a class of T-S discrete-time descriptor systems Conclusion Based on the singular value decomposition method and solving a system of LMIs for the determination of the observer parameters, two state observers design for a class of T-S discrete-time descriptor systems with measurable and unmeasurable premise variables are proposed in this paper. This work is an extension of the result developed in [15] and [19] which extend the method of the observer developed for linear systems in [21] to nonlinear T-S systems. To illustrate the proposed methodology with unmeasurable premise variables, a numerical example of a T-S discrete-time descriptor model is considered. The effectiveness of the proposed fuzzy observer used for the on-line estimation of unknown states in proposed model is verified by numerical simulation. References [1] S. L. Campbell, Singular Systems of Differential Equations, Pitman, London, UK, [2] F. L. Lewis, A survey of linear singular systems, Circuits, Systems and Signal Processing, , no. 1, [3] L. Dai, Singular Control Systems, Springer, Berlin, Germany, [4] T. Takagi, M. Sugeno, Fuzzy identification of systems and its application to modeling and control, IEEE Trans. Syst., Man and Cyber., SCM , [5] T. Taniguchi, K. Tanaka, H. Ohtake and H. Wang, Model construction, rule reduction, and robust compensation for generalized form of Takagi- Sugeno fuzzy systems, IEEE Transactions on Fuzzy Systems, , no. 4, [6] P. Bergsten, R. Palm, and D. Driankov, Fuzzy observers, IEEE International Conference on Fuzzy Systems, Melbourne Australia, [7] K. Tanaka, and H. O. Wang, Fuzzy Control Systems Design and Analysis: A Linear Matrix Inequality Approach, John Wiley & Sons, [8] D. Ichalal, B. Marx, J. Ragot, and D. Maquin, Design of observers for Takagi-Sugeno systems with immeasurable premise variables: an
14 5884 I. Hmaiddouch, B. Bentahra, A. El Assoudi, J. Soulami and E. El Yaagoubi L 2 approach, Proceedings of the 17th world congress, the international federation of automatic control, Seoul, Korea, July 6-11, [9] D. Ichalal, B. Marx, J. Ragot, and D. Maquin, State and unknown input estimation for nonlinear systems described by Takagi- Sugeno models with unmeasurable premise variables, 17th Mediterranean Conference on Control and Automation, 2009, [10] H. Ghorbel, M. Souissi, M. Chaabane, F. Tadeo, Robust fault detection for Takagi-Sugeno discrete models: Application for a three-tank system, International Journal of Computer Applications, , no. 18, [11] T. Taniguchi, K. Tanaka, and H. O. Wang, Fuzzy Descriptor Systems and Nonlinear Model Following Control, IEEE Transactions on Fuzzy Systems, , no. 4, [12] C. Lin, Q. G. Wang, and T. H. Lee, Stability and Stabilization of a Class of Fuzzy Time-Delay Descriptor Systems, IEEE Transactions on Fuzzy Systems, , no. 4, [13] B. Marx, D. Koenig and J. Ragot, Design of observers for Takagi-Sugeno descriptor systems with unknown inputs and application to fault diagnosis, IET Control Theory and Applications, , no. 5, [14] Kilani Ilhem, Jabri Dalel, Bel Hadj Ali Saloua and Abdelkrim Mohamed Naceur, Observer Design for Takagi-Sugeno Descriptor System with Lipschitz Constraints, International Journal of Instrumentation and Control Systems, , no. 2, [15] M. Essabre, J. Soulami, and E. Elyaagoubi, Design of State Observer for a Class of Non linear Singular Systems Described by Takagi-Sugeno Model, Contemporary Engineering Sciences, , no. 3, [16] H. Hamdi, M. Rodrigues, Ch. Mechmech and N. Benhadj Braiek, Observer based Fault Tolerant Control for Takagi-Sugeno Nonlinear Descriptor systems, International Conference on Control, Engineering & Information Technology CEIT 13, Proceedings Engineering & Technology, ,
15 State observer design for a class of T-S discrete-time descriptor systems 5885 [17] M. Essabre, J. Soulami, A. El Assoudi, E. Elyaagoubi and E. El Bouatmani, Fuzzy Observer Design for a Class of Takagi-Sugeno Descriptor Systems, Contemporary Engineering Sciences, , no. 4, [18] Kilani Ilhem, Hamza Rafika, Saloua Bel Hadj Ali, Abdelkrim Mohamed Naceur, Observer Design for Descriptor Takagi Sugeno System, International Journal of Computer Applications, , no. 26, [19] I. Hmaiddouch, B. Bentahra, A. El Assoudi, J. Soulami and E. El Yaagoubi, An explicit fuzzy observer design for a class of Takagi-Sugeno descriptor systems, Contemporary Engineering Sciences, , no. 28, [20] S. Boyd, L. Ghaoui, E. Feron, V. Balakrishnan, Linear Matrix Inequalities in System and Control Theory, in: SIAM Studies in Applied Mathematics, Vol. 15, SIAM, Philadelphia, [21] M. Darouach and M. Boutayeb, Design of observers for descriptor systems, IEEE Transactions on Automatic Control, , Received: April 14, 2015; Published: September 23, 2015
Lecture 13 Linear quadratic Lyapunov theory
EE363 Winter 28-9 Lecture 13 Linear quadratic Lyapunov theory the Lyapunov equation Lyapunov stability conditions the Lyapunov operator and integral evaluating quadratic integrals analysis of ARE discrete-time
More information1 Introduction to Matrices
1 Introduction to Matrices In this section, important definitions and results from matrix algebra that are useful in regression analysis are introduced. While all statements below regarding the columns
More informationCONTROLLABILITY. Chapter 2. 2.1 Reachable Set and Controllability. Suppose we have a linear system described by the state equation
Chapter 2 CONTROLLABILITY 2 Reachable Set and Controllability Suppose we have a linear system described by the state equation ẋ Ax + Bu (2) x() x Consider the following problem For a given vector x in
More informationLecture 7: Finding Lyapunov Functions 1
Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science 6.243j (Fall 2003): DYNAMICS OF NONLINEAR SYSTEMS by A. Megretski Lecture 7: Finding Lyapunov Functions 1
More informationLinear-Quadratic Optimal Controller 10.3 Optimal Linear Control Systems
Linear-Quadratic Optimal Controller 10.3 Optimal Linear Control Systems In Chapters 8 and 9 of this book we have designed dynamic controllers such that the closed-loop systems display the desired transient
More information19 LINEAR QUADRATIC REGULATOR
19 LINEAR QUADRATIC REGULATOR 19.1 Introduction The simple form of loopshaping in scalar systems does not extend directly to multivariable (MIMO) plants, which are characterized by transfer matrices instead
More informationSAMPLE OF THE STUDY MATERIAL PART OF CHAPTER 3. Symmetrical Components & Faults Calculations
SAMPLE OF THE STUDY MATERIAL PART OF CHAPTER 3 3.0 Introduction Fortescue's work proves that an unbalanced system of 'n' related phasors can be resolved into 'n' systems of balanced phasors called the
More informationModern Optimization Methods for Big Data Problems MATH11146 The University of Edinburgh
Modern Optimization Methods for Big Data Problems MATH11146 The University of Edinburgh Peter Richtárik Week 3 Randomized Coordinate Descent With Arbitrary Sampling January 27, 2016 1 / 30 The Problem
More informationOn the D-Stability of Linear and Nonlinear Positive Switched Systems
On the D-Stability of Linear and Nonlinear Positive Switched Systems V. S. Bokharaie, O. Mason and F. Wirth Abstract We present a number of results on D-stability of positive switched systems. Different
More informationCITY UNIVERSITY LONDON. BEng Degree in Computer Systems Engineering Part II BSc Degree in Computer Systems Engineering Part III PART 2 EXAMINATION
No: CITY UNIVERSITY LONDON BEng Degree in Computer Systems Engineering Part II BSc Degree in Computer Systems Engineering Part III PART 2 EXAMINATION ENGINEERING MATHEMATICS 2 (resit) EX2005 Date: August
More informationPID Controller Design for Nonlinear Systems Using Discrete-Time Local Model Networks
PID Controller Design for Nonlinear Systems Using Discrete-Time Local Model Networks 4. Workshop für Modellbasierte Kalibriermethoden Nikolaus Euler-Rolle, Christoph Hametner, Stefan Jakubek Christian
More informationDATA ANALYSIS II. Matrix Algorithms
DATA ANALYSIS II Matrix Algorithms Similarity Matrix Given a dataset D = {x i }, i=1,..,n consisting of n points in R d, let A denote the n n symmetric similarity matrix between the points, given as where
More informationMATRIX ALGEBRA AND SYSTEMS OF EQUATIONS. + + x 2. x n. a 11 a 12 a 1n b 1 a 21 a 22 a 2n b 2 a 31 a 32 a 3n b 3. a m1 a m2 a mn b m
MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS 1. SYSTEMS OF EQUATIONS AND MATRICES 1.1. Representation of a linear system. The general system of m equations in n unknowns can be written a 11 x 1 + a 12 x 2 +
More informationMixedÀ¾ нOptimization Problem via Lagrange Multiplier Theory
MixedÀ¾ нOptimization Problem via Lagrange Multiplier Theory Jun WuÝ, Sheng ChenÞand Jian ChuÝ ÝNational Laboratory of Industrial Control Technology Institute of Advanced Process Control Zhejiang University,
More informationa 11 x 1 + a 12 x 2 + + a 1n x n = b 1 a 21 x 1 + a 22 x 2 + + a 2n x n = b 2.
Chapter 1 LINEAR EQUATIONS 1.1 Introduction to linear equations A linear equation in n unknowns x 1, x,, x n is an equation of the form a 1 x 1 + a x + + a n x n = b, where a 1, a,..., a n, b are given
More informationChapter 7. Matrices. Definition. An m n matrix is an array of numbers set out in m rows and n columns. Examples. ( 1 1 5 2 0 6
Chapter 7 Matrices Definition An m n matrix is an array of numbers set out in m rows and n columns Examples (i ( 1 1 5 2 0 6 has 2 rows and 3 columns and so it is a 2 3 matrix (ii 1 0 7 1 2 3 3 1 is a
More informationT ( a i x i ) = a i T (x i ).
Chapter 2 Defn 1. (p. 65) Let V and W be vector spaces (over F ). We call a function T : V W a linear transformation form V to W if, for all x, y V and c F, we have (a) T (x + y) = T (x) + T (y) and (b)
More informationAdaptive Control Using Combined Online and Background Learning Neural Network
Adaptive Control Using Combined Online and Background Learning Neural Network Eric N. Johnson and Seung-Min Oh Abstract A new adaptive neural network (NN control concept is proposed with proof of stability
More informationAn Efficient RNS to Binary Converter Using the Moduli Set {2n + 1, 2n, 2n 1}
An Efficient RNS to Binary Converter Using the oduli Set {n + 1, n, n 1} Kazeem Alagbe Gbolagade 1,, ember, IEEE and Sorin Dan Cotofana 1, Senior ember IEEE, 1. Computer Engineering Laboratory, Delft University
More informationMATHEMATICAL METHODS OF STATISTICS
MATHEMATICAL METHODS OF STATISTICS By HARALD CRAMER TROFESSOK IN THE UNIVERSITY OF STOCKHOLM Princeton PRINCETON UNIVERSITY PRESS 1946 TABLE OF CONTENTS. First Part. MATHEMATICAL INTRODUCTION. CHAPTERS
More informationLecture 4: Partitioned Matrices and Determinants
Lecture 4: Partitioned Matrices and Determinants 1 Elementary row operations Recall the elementary operations on the rows of a matrix, equivalent to premultiplying by an elementary matrix E: (1) multiplying
More informationHøgskolen i Narvik Sivilingeniørutdanningen STE6237 ELEMENTMETODER. Oppgaver
Høgskolen i Narvik Sivilingeniørutdanningen STE637 ELEMENTMETODER Oppgaver Klasse: 4.ID, 4.IT Ekstern Professor: Gregory A. Chechkin e-mail: chechkin@mech.math.msu.su Narvik 6 PART I Task. Consider two-point
More information(Quasi-)Newton methods
(Quasi-)Newton methods 1 Introduction 1.1 Newton method Newton method is a method to find the zeros of a differentiable non-linear function g, x such that g(x) = 0, where g : R n R n. Given a starting
More informationMATRIX ALGEBRA AND SYSTEMS OF EQUATIONS
MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS Systems of Equations and Matrices Representation of a linear system The general system of m equations in n unknowns can be written a x + a 2 x 2 + + a n x n b a
More informationFactorization Theorems
Chapter 7 Factorization Theorems This chapter highlights a few of the many factorization theorems for matrices While some factorization results are relatively direct, others are iterative While some factorization
More informationMore than you wanted to know about quadratic forms
CALIFORNIA INSTITUTE OF TECHNOLOGY Division of the Humanities and Social Sciences More than you wanted to know about quadratic forms KC Border Contents 1 Quadratic forms 1 1.1 Quadratic forms on the unit
More informationA SURVEY ON CONTINUOUS ELLIPTICAL VECTOR DISTRIBUTIONS
A SURVEY ON CONTINUOUS ELLIPTICAL VECTOR DISTRIBUTIONS Eusebio GÓMEZ, Miguel A. GÓMEZ-VILLEGAS and J. Miguel MARÍN Abstract In this paper it is taken up a revision and characterization of the class of
More informationLeast-Squares Intersection of Lines
Least-Squares Intersection of Lines Johannes Traa - UIUC 2013 This write-up derives the least-squares solution for the intersection of lines. In the general case, a set of lines will not intersect at a
More informationExample 4.1 (nonlinear pendulum dynamics with friction) Figure 4.1: Pendulum. asin. k, a, and b. We study stability of the origin x
Lecture 4. LaSalle s Invariance Principle We begin with a motivating eample. Eample 4.1 (nonlinear pendulum dynamics with friction) Figure 4.1: Pendulum Dynamics of a pendulum with friction can be written
More informationEquations, Inequalities & Partial Fractions
Contents Equations, Inequalities & Partial Fractions.1 Solving Linear Equations 2.2 Solving Quadratic Equations 1. Solving Polynomial Equations 1.4 Solving Simultaneous Linear Equations 42.5 Solving Inequalities
More informationA Direct Numerical Method for Observability Analysis
IEEE TRANSACTIONS ON POWER SYSTEMS, VOL 15, NO 2, MAY 2000 625 A Direct Numerical Method for Observability Analysis Bei Gou and Ali Abur, Senior Member, IEEE Abstract This paper presents an algebraic method
More informationFunctional Optimization Models for Active Queue Management
Functional Optimization Models for Active Queue Management Yixin Chen Department of Computer Science and Engineering Washington University in St Louis 1 Brookings Drive St Louis, MO 63130, USA chen@cse.wustl.edu
More informationAN INTRODUCTION TO NUMERICAL METHODS AND ANALYSIS
AN INTRODUCTION TO NUMERICAL METHODS AND ANALYSIS Revised Edition James Epperson Mathematical Reviews BICENTENNIAL 0, 1 8 0 7 z ewiley wu 2007 r71 BICENTENNIAL WILEY-INTERSCIENCE A John Wiley & Sons, Inc.,
More information1 2 3 1 1 2 x = + x 2 + x 4 1 0 1
(d) If the vector b is the sum of the four columns of A, write down the complete solution to Ax = b. 1 2 3 1 1 2 x = + x 2 + x 4 1 0 0 1 0 1 2. (11 points) This problem finds the curve y = C + D 2 t which
More informationOn using numerical algebraic geometry to find Lyapunov functions of polynomial dynamical systems
Dynamics at the Horsetooth Volume 2, 2010. On using numerical algebraic geometry to find Lyapunov functions of polynomial dynamical systems Eric Hanson Department of Mathematics Colorado State University
More informationContinued Fractions and the Euclidean Algorithm
Continued Fractions and the Euclidean Algorithm Lecture notes prepared for MATH 326, Spring 997 Department of Mathematics and Statistics University at Albany William F Hammond Table of Contents Introduction
More informationLOOP TRANSFER RECOVERY FOR SAMPLED-DATA SYSTEMS 1
LOOP TRANSFER RECOVERY FOR SAMPLED-DATA SYSTEMS 1 Henrik Niemann Jakob Stoustrup Mike Lind Rank Bahram Shafai Dept. of Automation, Technical University of Denmark, Building 326, DK-2800 Lyngby, Denmark
More informationZeros of Polynomial Functions
Zeros of Polynomial Functions The Rational Zero Theorem If f (x) = a n x n + a n-1 x n-1 + + a 1 x + a 0 has integer coefficients and p/q (where p/q is reduced) is a rational zero, then p is a factor of
More informationBy choosing to view this document, you agree to all provisions of the copyright laws protecting it.
This material is posted here with permission of the IEEE Such permission of the IEEE does not in any way imply IEEE endorsement of any of Helsinki University of Technology's products or services Internal
More informationOPTIMAl PREMIUM CONTROl IN A NON-liFE INSURANCE BUSINESS
ONDERZOEKSRAPPORT NR 8904 OPTIMAl PREMIUM CONTROl IN A NON-liFE INSURANCE BUSINESS BY M. VANDEBROEK & J. DHAENE D/1989/2376/5 1 IN A OPTIMAl PREMIUM CONTROl NON-liFE INSURANCE BUSINESS By Martina Vandebroek
More informationDynamic Eigenvalues for Scalar Linear Time-Varying Systems
Dynamic Eigenvalues for Scalar Linear Time-Varying Systems P. van der Kloet and F.L. Neerhoff Department of Electrical Engineering Delft University of Technology Mekelweg 4 2628 CD Delft The Netherlands
More information1 Sets and Set Notation.
LINEAR ALGEBRA MATH 27.6 SPRING 23 (COHEN) LECTURE NOTES Sets and Set Notation. Definition (Naive Definition of a Set). A set is any collection of objects, called the elements of that set. We will most
More informationAn Integrated Production Inventory System for. Perishable Items with Fixed and Linear Backorders
Int. Journal of Math. Analysis, Vol. 8, 2014, no. 32, 1549-1559 HIKARI Ltd, www.m-hikari.com http://dx.doi.org/10.12988/ijma.2014.46176 An Integrated Production Inventory System for Perishable Items with
More informationLS.6 Solution Matrices
LS.6 Solution Matrices In the literature, solutions to linear systems often are expressed using square matrices rather than vectors. You need to get used to the terminology. As before, we state the definitions
More informationComputing a Nearest Correlation Matrix with Factor Structure
Computing a Nearest Correlation Matrix with Factor Structure Nick Higham School of Mathematics The University of Manchester higham@ma.man.ac.uk http://www.ma.man.ac.uk/~higham/ Joint work with Rüdiger
More informationFUZZY CLUSTERING ANALYSIS OF DATA MINING: APPLICATION TO AN ACCIDENT MINING SYSTEM
International Journal of Innovative Computing, Information and Control ICIC International c 0 ISSN 34-48 Volume 8, Number 8, August 0 pp. 4 FUZZY CLUSTERING ANALYSIS OF DATA MINING: APPLICATION TO AN ACCIDENT
More informationLyapunov Stability Analysis of Energy Constraint for Intelligent Home Energy Management System
JAIST Reposi https://dspace.j Title Lyapunov stability analysis for intelligent home energy of energ manageme Author(s)Umer, Saher; Tan, Yasuo; Lim, Azman Citation IEICE Technical Report on Ubiquitous
More informationSimilarity and Diagonalization. Similar Matrices
MATH022 Linear Algebra Brief lecture notes 48 Similarity and Diagonalization Similar Matrices Let A and B be n n matrices. We say that A is similar to B if there is an invertible n n matrix P such that
More informationMATH 304 Linear Algebra Lecture 9: Subspaces of vector spaces (continued). Span. Spanning set.
MATH 304 Linear Algebra Lecture 9: Subspaces of vector spaces (continued). Span. Spanning set. Vector space A vector space is a set V equipped with two operations, addition V V (x,y) x + y V and scalar
More informationIntroduction to Matrix Algebra
Psychology 7291: Multivariate Statistics (Carey) 8/27/98 Matrix Algebra - 1 Introduction to Matrix Algebra Definitions: A matrix is a collection of numbers ordered by rows and columns. It is customary
More information1.2 Solving a System of Linear Equations
1.. SOLVING A SYSTEM OF LINEAR EQUATIONS 1. Solving a System of Linear Equations 1..1 Simple Systems - Basic De nitions As noticed above, the general form of a linear system of m equations in n variables
More informationBrief Paper. of discrete-time linear systems. www.ietdl.org
Published in IET Control Theory and Applications Received on 28th August 2012 Accepted on 26th October 2012 Brief Paper ISSN 1751-8644 Temporal and one-step stabilisability and detectability of discrete-time
More information1. Introduction. Consider the computation of an approximate solution of the minimization problem
A NEW TIKHONOV REGULARIZATION METHOD MARTIN FUHRY AND LOTHAR REICHEL Abstract. The numerical solution of linear discrete ill-posed problems typically requires regularization, i.e., replacement of the available
More informationA characterization of trace zero symmetric nonnegative 5x5 matrices
A characterization of trace zero symmetric nonnegative 5x5 matrices Oren Spector June 1, 009 Abstract The problem of determining necessary and sufficient conditions for a set of real numbers to be the
More informationCONTROL SYSTEMS, ROBOTICS AND AUTOMATION Vol. XVI - Fault Accomodation Using Model Predictive Methods - Jovan D. Bošković and Raman K.
FAULT ACCOMMODATION USING MODEL PREDICTIVE METHODS Scientific Systems Company, Inc., Woburn, Massachusetts, USA. Keywords: Fault accommodation, Model Predictive Control (MPC), Failure Detection, Identification
More informationIMPROVED NETWORK PARAMETER ERROR IDENTIFICATION USING MULTIPLE MEASUREMENT SCANS
IMPROVED NETWORK PARAMETER ERROR IDENTIFICATION USING MULTIPLE MEASUREMENT SCANS Liuxi Zhang and Ali Abur Department of Electrical and Computer Engineering Northeastern University Boston, MA, USA lzhang@ece.neu.edu
More informationDecember 4, 2013 MATH 171 BASIC LINEAR ALGEBRA B. KITCHENS
December 4, 2013 MATH 171 BASIC LINEAR ALGEBRA B KITCHENS The equation 1 Lines in two-dimensional space (1) 2x y = 3 describes a line in two-dimensional space The coefficients of x and y in the equation
More informationMathematical finance and linear programming (optimization)
Mathematical finance and linear programming (optimization) Geir Dahl September 15, 2009 1 Introduction The purpose of this short note is to explain how linear programming (LP) (=linear optimization) may
More informationSolution to Homework 2
Solution to Homework 2 Olena Bormashenko September 23, 2011 Section 1.4: 1(a)(b)(i)(k), 4, 5, 14; Section 1.5: 1(a)(b)(c)(d)(e)(n), 2(a)(c), 13, 16, 17, 18, 27 Section 1.4 1. Compute the following, if
More informationApplied Linear Algebra I Review page 1
Applied Linear Algebra Review 1 I. Determinants A. Definition of a determinant 1. Using sum a. Permutations i. Sign of a permutation ii. Cycle 2. Uniqueness of the determinant function in terms of properties
More informationCongestion Control of Active Queue Management Routers Based on LQ-Servo Control
Congestion Control of Active Queue Management outers Based on LQ-Servo Control Kang Min Lee, Ji Hoon Yang, and Byung Suhl Suh Abstract This paper proposes the LQ-Servo controller for AQM (Active Queue
More informationMulti-variable Calculus and Optimization
Multi-variable Calculus and Optimization Dudley Cooke Trinity College Dublin Dudley Cooke (Trinity College Dublin) Multi-variable Calculus and Optimization 1 / 51 EC2040 Topic 3 - Multi-variable Calculus
More informationAXIOMS FOR INVARIANT FACTORS*
PORTUGALIAE MATHEMATICA Vol 54 Fasc 3 1997 AXIOMS FOR INVARIANT FACTORS* João Filipe Queiró Abstract: We show that the invariant factors of matrices over certain types of rings are characterized by a short
More informationSolution of Linear Systems
Chapter 3 Solution of Linear Systems In this chapter we study algorithms for possibly the most commonly occurring problem in scientific computing, the solution of linear systems of equations. We start
More informationControllability and Observability
Controllability and Observability Controllability and observability represent two major concepts of modern control system theory These concepts were introduced by R Kalman in 1960 They can be roughly defined
More informationOrthogonal Diagonalization of Symmetric Matrices
MATH10212 Linear Algebra Brief lecture notes 57 Gram Schmidt Process enables us to find an orthogonal basis of a subspace. Let u 1,..., u k be a basis of a subspace V of R n. We begin the process of finding
More informationα = u v. In other words, Orthogonal Projection
Orthogonal Projection Given any nonzero vector v, it is possible to decompose an arbitrary vector u into a component that points in the direction of v and one that points in a direction orthogonal to v
More informationProximal mapping via network optimization
L. Vandenberghe EE236C (Spring 23-4) Proximal mapping via network optimization minimum cut and maximum flow problems parametric minimum cut problem application to proximal mapping Introduction this lecture:
More informationSF2940: Probability theory Lecture 8: Multivariate Normal Distribution
SF2940: Probability theory Lecture 8: Multivariate Normal Distribution Timo Koski 24.09.2015 Timo Koski Matematisk statistik 24.09.2015 1 / 1 Learning outcomes Random vectors, mean vector, covariance matrix,
More information13 MATH FACTS 101. 2 a = 1. 7. The elements of a vector have a graphical interpretation, which is particularly easy to see in two or three dimensions.
3 MATH FACTS 0 3 MATH FACTS 3. Vectors 3.. Definition We use the overhead arrow to denote a column vector, i.e., a linear segment with a direction. For example, in three-space, we write a vector in terms
More informationOverview of Violations of the Basic Assumptions in the Classical Normal Linear Regression Model
Overview of Violations of the Basic Assumptions in the Classical Normal Linear Regression Model 1 September 004 A. Introduction and assumptions The classical normal linear regression model can be written
More informationSF2940: Probability theory Lecture 8: Multivariate Normal Distribution
SF2940: Probability theory Lecture 8: Multivariate Normal Distribution Timo Koski 24.09.2014 Timo Koski () Mathematisk statistik 24.09.2014 1 / 75 Learning outcomes Random vectors, mean vector, covariance
More informationLQG-Balanced Truncation Low-Order Controller for Stabilization of Laminar Flows
MAX PLANCK INSTITUTE May 20, 2015 LQG-Balanced Truncation Low-Order Controller for Stabilization of Laminar Flows Peter Benner and Jan Heiland Workshop Model Order Reduction for Transport Dominated Phenomena
More informationResearch Article Stability Analysis for Higher-Order Adjacent Derivative in Parametrized Vector Optimization
Hindawi Publishing Corporation Journal of Inequalities and Applications Volume 2010, Article ID 510838, 15 pages doi:10.1155/2010/510838 Research Article Stability Analysis for Higher-Order Adjacent Derivative
More informationIntersection of a Line and a Convex. Hull of Points Cloud
Applied Mathematical Sciences, Vol. 7, 213, no. 13, 5139-5149 HIKARI Ltd, www.m-hikari.com http://dx.doi.org/1.12988/ams.213.37372 Intersection of a Line and a Convex Hull of Points Cloud R. P. Koptelov
More informationInner Product Spaces
Math 571 Inner Product Spaces 1. Preliminaries An inner product space is a vector space V along with a function, called an inner product which associates each pair of vectors u, v with a scalar u, v, and
More informationTHREE DIMENSIONAL REPRESENTATION OF AMINO ACID CHARAC- TERISTICS
THREE DIMENSIONAL REPRESENTATION OF AMINO ACID CHARAC- TERISTICS O.U. Sezerman 1, R. Islamaj 2, E. Alpaydin 2 1 Laborotory of Computational Biology, Sabancı University, Istanbul, Turkey. 2 Computer Engineering
More informationA Study on the Comparison of Electricity Forecasting Models: Korea and China
Communications for Statistical Applications and Methods 2015, Vol. 22, No. 6, 675 683 DOI: http://dx.doi.org/10.5351/csam.2015.22.6.675 Print ISSN 2287-7843 / Online ISSN 2383-4757 A Study on the Comparison
More information4 Lyapunov Stability Theory
4 Lyapunov Stability Theory In this section we review the tools of Lyapunov stability theory. These tools will be used in the next section to analyze the stability properties of a robot controller. We
More informationOn closed-form solutions to a class of ordinary differential equations
International Journal of Advanced Mathematical Sciences, 2 (1 (2014 57-70 c Science Publishing Corporation www.sciencepubco.com/index.php/ijams doi: 10.14419/ijams.v2i1.1556 Research Paper On closed-form
More informationZeros of Polynomial Functions
Zeros of Polynomial Functions Objectives: 1.Use the Fundamental Theorem of Algebra to determine the number of zeros of polynomial functions 2.Find rational zeros of polynomial functions 3.Find conjugate
More informationThe Australian Journal of Mathematical Analysis and Applications
The Australian Journal of Mathematical Analysis and Applications Volume 7, Issue, Article 11, pp. 1-14, 011 SOME HOMOGENEOUS CYCLIC INEQUALITIES OF THREE VARIABLES OF DEGREE THREE AND FOUR TETSUYA ANDO
More informationAlgebra 2 Chapter 1 Vocabulary. identity - A statement that equates two equivalent expressions.
Chapter 1 Vocabulary identity - A statement that equates two equivalent expressions. verbal model- A word equation that represents a real-life problem. algebraic expression - An expression with variables.
More informationInner Product Spaces and Orthogonality
Inner Product Spaces and Orthogonality week 3-4 Fall 2006 Dot product of R n The inner product or dot product of R n is a function, defined by u, v a b + a 2 b 2 + + a n b n for u a, a 2,, a n T, v b,
More informationLinearly Independent Sets and Linearly Dependent Sets
These notes closely follow the presentation of the material given in David C. Lay s textbook Linear Algebra and its Applications (3rd edition). These notes are intended primarily for in-class presentation
More informationVector and Matrix Norms
Chapter 1 Vector and Matrix Norms 11 Vector Spaces Let F be a field (such as the real numbers, R, or complex numbers, C) with elements called scalars A Vector Space, V, over the field F is a non-empty
More informationSimilar matrices and Jordan form
Similar matrices and Jordan form We ve nearly covered the entire heart of linear algebra once we ve finished singular value decompositions we ll have seen all the most central topics. A T A is positive
More informationSOLVING LINEAR SYSTEMS
SOLVING LINEAR SYSTEMS Linear systems Ax = b occur widely in applied mathematics They occur as direct formulations of real world problems; but more often, they occur as a part of the numerical analysis
More informationNonlinear Programming Methods.S2 Quadratic Programming
Nonlinear Programming Methods.S2 Quadratic Programming Operations Research Models and Methods Paul A. Jensen and Jonathan F. Bard A linearly constrained optimization problem with a quadratic objective
More informationAC 2012-4561: MATHEMATICAL MODELING AND SIMULATION US- ING LABVIEW AND LABVIEW MATHSCRIPT
AC 2012-4561: MATHEMATICAL MODELING AND SIMULATION US- ING LABVIEW AND LABVIEW MATHSCRIPT Dr. Nikunja Swain, South Carolina State University Nikunja Swain is a professor in the College of Science, Mathematics,
More informationSOLVING COMPLEX SYSTEMS USING SPREADSHEETS: A MATRIX DECOMPOSITION APPROACH
SOLVING COMPLEX SYSTEMS USING SPREADSHEETS: A MATRIX DECOMPOSITION APPROACH Kenneth E. Dudeck, Associate Professor of Electrical Engineering Pennsylvania State University, Hazleton Campus Abstract Many
More informationA new evaluation model for e-learning programs
A new evaluation model for e-learning programs Uranchimeg Tudevdagva 1, Wolfram Hardt 2 Abstract This paper deals with a measure theoretical model for evaluation of e-learning programs. Based on methods
More informationTHREE DIMENSIONAL GEOMETRY
Chapter 8 THREE DIMENSIONAL GEOMETRY 8.1 Introduction In this chapter we present a vector algebra approach to three dimensional geometry. The aim is to present standard properties of lines and planes,
More informationSECOND DERIVATIVE TEST FOR CONSTRAINED EXTREMA
SECOND DERIVATIVE TEST FOR CONSTRAINED EXTREMA This handout presents the second derivative test for a local extrema of a Lagrange multiplier problem. The Section 1 presents a geometric motivation for the
More informationStability criteria for large-scale time-delay systems: the LMI approach and the Genetic Algorithms
Control and Cybernetics vol 35 (2006) No 2 Stability criteria for large-scale time-delay systems: the LMI approach and the Genetic Algorithms by Jenq-Der Chen Department of Electronic Engineering National
More informationSupport Vector Machines with Clustering for Training with Very Large Datasets
Support Vector Machines with Clustering for Training with Very Large Datasets Theodoros Evgeniou Technology Management INSEAD Bd de Constance, Fontainebleau 77300, France theodoros.evgeniou@insead.fr Massimiliano
More informationFuzzy regression model with fuzzy input and output data for manpower forecasting
Fuzzy Sets and Systems 9 (200) 205 23 www.elsevier.com/locate/fss Fuzzy regression model with fuzzy input and output data for manpower forecasting Hong Tau Lee, Sheu Hua Chen Department of Industrial Engineering
More informationNotes on Determinant
ENGG2012B Advanced Engineering Mathematics Notes on Determinant Lecturer: Kenneth Shum Lecture 9-18/02/2013 The determinant of a system of linear equations determines whether the solution is unique, without
More information1 VECTOR SPACES AND SUBSPACES
1 VECTOR SPACES AND SUBSPACES What is a vector? Many are familiar with the concept of a vector as: Something which has magnitude and direction. an ordered pair or triple. a description for quantities such
More information