Automation and Robotics Part 8 pptx

10 Linear Lyapunov Cone-Systems Przemysław Przyborowski and Tadeusz Kaczorek Warsaw University of Technology – Faculty of Electrical Engineering, Institute of Control and Industrial Electronics, Poland 1. Introduction In positive systems inputs, state variables and outputs take only non-negative values. Examples of positive systems are industrial processes involving chemical reactors, heat exchangers and distillation columns, storage systems, compartmental systems, water and atmospheric pollution models. A variety of models having positive linear behavior can be found in engineering, management science, economics, social sciences, biology and medicine, etc. Positive linear systems are defined on cones and not on linear spaces. Therefore, the theory of positive systems in more complicated and less advanced. An overview of state of the art in positive systems theory is given in the monographs (Farina L. & Rinaldi S., 2000; Kaczorek T., 2001). The realization problem for positive linear systems without and with time delays has been considered in (Benvenuti L. & Farina L., 2004; Farina L. & Rinaldi S.,2000; Kaczorek T., 2004a; Kaczorek T., 2006a; Kaczorek T., 2006b; Kaczorek T. & Busłowicz M, 2004a). The reachability, controllability to zero and observability of dynamical systems have been considered in (Klamka J., 1991). The reachability and minimum energy control of positive linear discrete-time systems have been investigated in (Busłowicz M. & Kaczorek T., 2004). The positive discrete-time systems with delays have been considered in (Kaczorek T., 2004b; Kaczorek T. & Busłowicz M., 2004b; Kaczorek T. & Busłowicz M., 2004c). The controllability and observability of Lyapunov systems have been investigated by Murty Apparao in the paper (Murty M.S.N. & Apparao B.V., 2005). The positive discrete-time and continuous-time Lyapunov systems have been considered in (Kaczorek T., 2007b; Kaczorek T. & Przyborowski P., 2007a; Kaczorek T. & Przyborowski P., 2008; Kaczorek T. & Przyborowski P., 2007e). The positive linear time-varying Lyapunov systems have been investigated in (Kaczorek T. & Przyborowski P., 2007b). The continuous-time Lyapunov cone systems have been considered in (Kaczorek T. & Przyborowski P., 2007c). The positive discrete-time Lyapunov systems with delays have been investigated in (Kaczorek T. & Przyborowski P., 2007d). The first definition of the fractional derivative was introduced by Liouville and Riemann at the end of the 19th century (Nishimoto K., 1984; Miller K. S. & Ross B., 1993; Podlubny I., 1999). This idea by engineers has been used for modelling different process in the late 1960s (Bologna M. & Grigolini P., 2003; Vinagre B. M. et al., 2002; Vinagre B. M. & Feliu V., 2002; Zaborowsky V. & Meylanov R., 2001). Mathematical fundamentals of fractional calculus are given in the monographs (Miller K. S. & Ross B., 1993; Nishimoto K., 1984; Oldham K. B. & Automation and Robotics 170 Spanier J, 1974; Podlubny I., 1999; Oustaloup A., 1995). The fractional order controllers have been developed in (Oldham K. B. & Spanier J., 1974; Oustaloup A., 1993; Podlubny I.,2002). A generalization of the Kalman filter for fractional order systems has been proposed in (Sierociuk D. & Dzieliński D., 2006). Some others applications of fractional order systems can be found in (Ostalczyk P., 2000; Ostalczyk P., 2004a; Ostalczyk P., 2004b; Ferreira N.M.F. & Machado I.A.T., 2003; Gałkowski K., 2005; Moshrefi-Torbati M. & Hammond K.,1998; Reyes-Melo M.E. et al., 2004; Riu D. et al., 2001; Samko S. G. et al., 1993; Dzieliński A. & Sierociuk D., 2006). In (Ortigueira M. D., 1997) a method for computation of the impulse responses from the frequency responses for the fractional standard (non-positive) discrete-time linear systems is proposed. The reachability and controllability to zero of positive fractional systems has been considered in (Kaczorek T.,2007c; Kaczorek T., 2007d). The reachability and controllability to zero of fractional cone-systems has been considered in (Kaczorek T., 2007e). The fractional discrete-time Lyapunov systems has been investigated in (Przyborowski P., 2008a) and the fractional discrete-time cone-systems in (Przyborowski P., 2008b). The chapter is organized as follows, In the Section 2, some basic notations, definitions and lemmas will be recalled. In the Section 3, the continuous-time linear Lyapunov cone-systems will be considered. For the systems, the necessary and sufficient conditions for being the cone-system, the asymptotic stability and sufficient conditions for the reachability and observability will be established. In the Section 4, the discrete-time linear Lyapunov cone- systems will be considered. For the systems, the necessary and sufficient conditions for being the cone-system, the asymptotic stability, reachability, observability and controllability to zero will be established. In the Section 5, the fractional discrete-time linear Lyapunov cone-systems will be considered. For the systems, the necessary and sufficient conditions for being the cone-system, the reachability, observability and controllability to zero and sufficient conditions for the stability will be established. In the Section 6, the considerations will be illustrated by numerical examples. 2. Preliminaries Let nxm R be the set of real nm × matrices , 1nn R R × = and let nxm R + be the set of real nm× matrices with nonnegative entries. The set of nonnegative integers will be denoted by Z + . Definition 1. The Kronecker product AB ⊗ of the matrices [] mxn ij Aa R=∈ and pxq B R ∈ is the block matrix (Kaczorek T.,1998): 1, , 1, , [] mp nq ij i m jn AB aB R × = = ⊗= ∈ (1) Lemma 1. Let us consider the equation: AXB C= (2) where: ,, , mn q p m p nq AR BR CR X R × ××× ∈∈∈ ∈ Linear Lyapunov Cone-Systems 171 Equation (2) is equivalent to the following one: () T ABxc ⊗ = (3) where [][] 12 12 :,: TT nm x xx x c cc c==……, and i x and i c are the i th rows of the matrices X and C respectively. Proof: See (Kaczorek T., 1998) Lemma 2. If 12 ,, n λ λλ … are the eigenvalues of the matrix A and 12 ,, n μ μμ … the eigenvalues of the matrix B , then ij λ μ + for , 1, 2, ,ij n = are the eigenvalues of the matrix: T nn AAI I B=⊗+⊗ Proof: See (Kaczorek T.,1998) Definition 2. Let 1 nn n p P R p × =∈ ⎡⎤ ⎢⎥ ⎢⎥ ⎢⎥ ⎣⎦  be nonsingular and k p be the k th (1,,)kn = … its row. The set: { } 1 () : () 0P: n nn k k i Xt R pX t × = ∈ ≥= ∩ (4) where (), 1, , i X ti n= … is the i th column of the matrix () X t ,is called a linear cone of the state variables generated by the matrix P . In the similar way we may define the linear cone of the inputs: { } 1 () : () 0Q: m mn k k i Ut R qU t × = ∈ ≥= ∩ (5) generated by the nonsingular matrix 1 mm m q QR q × =∈ ⎡⎤ ⎢⎥ ⎢⎥ ⎢⎥ ⎣⎦  and the linear cone of the outputs { } 1 () : () 0V: p pn k k i Yt R vY t × = ∈≥= ∩ (6) generated by the nonsingular matrix 1 p p p v VR v × =∈ ⎡⎤ ⎢⎥ ⎢⎥ ⎢⎥ ⎣⎦  . Automation and Robotics 172 3. Continuous-time linear Lyapunov cone-systems Consider the continues-time linear Lyapunov system (Kaczorek T. & Przyborowski P., 2007a) described by the equations: 01 () () () () X t AXt XtA BUt=++  (7a) () () ()Yt CXt DUt = + (7b) where, () nxn X tR∈ is the state-space matrix, () mxn Ut R∈ is the input matrix, () p xn Yt R∈ is the output matrix, 01 ,,,, nxn nxm pxn pxm AA R B R C R D R∈∈∈ ∈ . The solution of the equation (1a) satisfying the initial condition 00 () X tX= is given by (Kaczorek T. & Przyborowski P., 2007a): 00 10 0 1 0 () () () () 0 () ( ) t Att Att At At t Xt e Xe e BU e d τ τ τ τ −− − − =+ ∫ (8) Lemma 3. The Lyapunov system (7) can be transformed to the equivalent standard continuous-time, nm -inputs and p n -outputs, linear system in the form: () () () x tAxtBut=+    (9a) () () () y tCxtDut=+    (9b) where, 2 () n x tR∈  is the state-space vector, () () nm ut R∈  is the input vector, () () pn yt R∈  is the output vector, 22 2 2 () () ()() ,,, nxn nxnm pnxn pnxnm AR BR CR DR∈∈ ∈ ∈   . Proof: The transformation is based on Lemma 1. The matrices ,, X UYare transformed to the vectors: [][] 12 12 12 ,, T TT nmp xXX X uUU U yYY Y=== ⎡ ⎤ ⎣ ⎦  ……… where ,, iii X UY denotes the i th rows of the matrices ,, X UY, respectively. The matrices of (9) are: 01 (),,, T nn n n n AAII ABBICCIDDI=⊗+⊗ =⊗ =⊗ =⊗   (10) 3.1 Cone-systems Definition 3. The Lyapunov system (7) is called (P,Q,V) -cone-system if () PXt ∈ and () VYt ∈ for every 0 PX ∈ and for every input () QUt∈ , 0 tt≥ . Linear Lyapunov Cone-Systems 173 Note that for ,,PQV nn mn pn RR R × ×× ++ + == = we obtain (, , ) nn mn pn RR R ××× ++ + -cone system which is equivalent to the positive Lyapunov system (Kaczorek T. & Przyborowski P., 2007c). Theorem 1. The Lyapunov system (7) is (P,Q,V) -cone-system if and only if : 1 00 11 ˆˆ ,APAPAA − = = (11) are the Metzler matrices and 111 ˆ ˆˆ ,, . nxm pxn pxm B PBQ R C VCP R D VDQ R −−− +++ =∈ =∈=∈ (12) Proof: Let: ˆˆˆ () (), () (), () ()Xt PXt Ut QUt Yt VYt=== (13) From definition 2 it follows that if () PXt ∈ then ˆ () nn X tR × + ∈ , if () QUt∈ then ˆ () mn Ut R × + ∈ , and if () VYt∈ then ˆ () p n Yt R × + ∈ . From (7) and (13) we have: 11 01 0 1 1 01 ˆˆˆ () () () () () () () ˆ ˆˆˆˆˆ () () () () Xt PXt PAXt PXtA PBUt PAP Xt PP XtA PBQUt AXt XtA BUt −− − == + + = + + +=++   (14a) and 11 ˆ ˆˆˆˆˆˆ () () () () () () () ()YtVYtVCXtVDUtVCPXtVDQUtCXtDUt −− == + = + = + (14b) It is known (Kaczorek T. & Przyborowski P., 2007a) that the system (14) is positive if and only if the conditions (11) and (12) are satisfied. □ 3.2 Asymptotic stability Consider the autonomous Lyapunov (P,Q,V) -cone-system: 0100 () () () , ( )Xt AXt XtA Xt X=+ =  (15) where, () PXt∈ and 1 01 , nn PA P A R − × ∈ are the Metzler matrices. Definition 4. The Lyapunov (P,Q,V) -cone-system (15) is called asymptotically stable if: lim ( ) 0 t Xt →∞ = for every 0 PX ∈ Automation and Robotics 174 Theorem 2. Let us assume that 12 ,, n λ λλ … are the eigenvalues of the matrix 0 A and 12 ,, n μ μμ … the eigenvalues of the matrix 1 A . The system (15) is stable if and only if: Re ( ) 0 ij λ μ + < for , 1, 2, ,ij n = (16) Proof: The theorem results directly from the theorem for asymptotic stability of standard systems (Kaczorek T., 2001), since by Lemma 2 eigenvalues of matrix A  are the sums of eigenvalues of the matrices 0 A and 1 A . □ 3.3 Reachability Definition 5. The state P f X ∈ of the the Lyapunov (P,Q,V) -cone-system (7) is called reachable at time f t , if there exists an input () QUt∈ for 0 [, ] f ttt ∈ , which steers the system from the initial state 0 0X = to the state f X . Definition 6. If for every state P f X ∈ there exists 0f tt> , such that the state is reachable at time f t , then the system is called reachable. Theorem 3. The (P,Q,V) -cone-system (7) is reachable if the matrix: 11 00 0 () ( )() 11 :()() f T ff t PA P t PA P t T f t R e PBQ PBQ e d ττ τ −− −− −− = ∫ (17) is a monomial matrix (only one element in every row and in every column of the matrix is positive and the remaining are equal to zero). The input, that steers the system from initial state 0 0X = to the state f X is given by: 1 01 ( )() () 11 1 () [( ) ] T ff PA P t t A t t T ff U t Q PBQ e R PX e − −− −− − = (18) for 0 [, ] f ttt∈ . Proof: If f R is the monomial matrix, then there exists 1 nn f R R − × + ∈ and the input (18) is well- defined. Using (8) and (18) we obtain: Linear Lyapunov Cone-Systems 175 11 0011 0 11 00 0 () ( )() ()() 1111 () ( )() 11111 () [ ( )( ) ] [()() ] f T ffff f T ff t PA P t PA P t A t A t T f ff t t PA P t PA P t T ff f f t X t P e PBQ PBQ e R PX e e d P e PBQ PBQ e d R PX P PX X ττττ ττ τ τ −− −− −−−− −−−− −− −−−−− == === ∫ ∫ (19) since 11 ()() ff At At n ee I ττ −− = . □ 3.4 Dual Lyapunov cone-systems Definition 7. The Lyapunov system described by the equations: 00 () () () () TTT X tAXtXtACUt=++  (20a) () () () T Yt BXt DUt=+ (20b) is called the dual system with respect to the system (7). The matrices 01 ,,,,, A ABCD (), (), () X tUtYt are the same as in the system (7). 3.5 Observability Definition 8. The state 0 X of the Lyapunov (P,Q,V) -cone- system (7) is called observable at time 0 f t > , if 0 X can be uniquely determined from the knowledge of the output ()Yt and input ()Ut for [0, ] f tt∈ . Definition 9. The Lyapunov (P,Q,V) -cone- system (7) is called observable, if there exists an instant 0 f t > , such that the system (7) is observable at time f t . Theorem 4. The Lyapunov (P,Q,V) -cone-system (7) is observable if the dual system (20) is reachable i.e. if the matrix: 11 00 0 ()() ()() 11 :()() f T ff t PA P t PA P t T f t Oe VCPVCPe d ττ τ −− −− −− = ∫ (21) is a monomial matrix. Proof: The Lyapunov (P,Q,V) -cone-system (7) is observable if and only if the equivalent standard system (9) is observable and this implies that dual system with respect to the system (9) must be reachable thus the dual system (20) with respect to the system (7) also must be reachable. Using Theorem 3. we obtain the hypothesis of the Theorem 4. □ Automation and Robotics 176 4. Discrete-time linear Lyapunov cone-systems Consider the discrete-time linear Lyapunov system (Kaczorek T., 2007b; Kaczorek T. & Przyborowski P., 2007e; Kaczorek T. & Przyborowski P., 2008) described by the equations: 10 1iiii X AX XA BU + = ++ (22a) ii i YCXDU=+ (22b) where, nxn i X R∈ is the state-space matrix, mxn i UR∈ is the input matrix, pxn i YR∈ is the output matrix, 01 ,,,,,. nxn nxm pxn pxm AA R B R C R D R i Z + ∈∈ ∈ ∈∈ The solution of the equation (22a) satisfying the initial condition 0 X is given by (Kaczorek T.,2007b): 1 001 0 11 000 !! , !( )! !( )! j ii kik k ik i ij kjk ij XAXA ABUAiZ ki k k j k − −− − −+ === =+ ∈ −− ∑∑∑ (23) Lemma 4. The Lyapunov system (22) can be transformed to the equivalent standard discrete-time, nm -inputs and p n -outputs, linear system in the form: 1iii x Ax Bu + =+ (24a) ii i yCxDu=+ (24b) where, 2 n i x R∈ is the state-space vector, ()nm i uR∈ is the input vector, ()pn i yR∈ is the output vector, 22 2 2 () () ()() ,, , , nxn nxnm pnxn pnxnm A RBR CR DR iZ + ∈∈ ∈ ∈ ∈ . Proof: The proof is similar to the one of Lemma 3. The matrices of (24) have the form: 01 (),,, T nn n n n AAII ABBICCIDDI=⊗+⊗ =⊗ =⊗ =⊗ (25) 4.1 Cone-systems Definition 10. The Lyapunov system (22) is called (P,Q,V) -cone-system if P i X ∈ and V i Y ∈ for every 0 PX ∈ and for every input Q i U ∈ , iZ + ∈ . Note that for ,,PQV nn mn pn RR R × ×× ++ + == = we obtain (, , ) nn mn pn RR R ××× ++ + -cone system which is equivalent to the positive Lyapunov system (Kaczorek T., 2007b). Linear Lyapunov Cone-Systems 177 Theorem 5. The Lyapunov system (22) is (P,Q,V) -cone-system if and only if : 10 1 1, , 1, , 00 11 1, , 1, , ˆˆ ˆˆ , in in ij ij jn jn APAP a AA a − == == ⎡⎤ ⎡⎤ == == ⎣⎦ ⎣⎦ …… …… (26) are the Metzler matrices satisfying 01 ˆˆ 0,1,, kk ll a a for every k l n+≥ =… (27) and 11 1 ˆ ˆˆ ,, nm pn pm B PBQ R C VCP R D VDQ R − ×−× −× ++ + =∈ =∈ =∈ (28) Proof: Let: ˆˆˆ ,, iii iii XPXUQUYVY=== (29) From definition 2 it follows that if P i X ∈ then ˆ nn i X R × + ∈ , if Q i U ∈ then ˆ mn i UR × + ∈ , and if V i Y ∈ then ˆ pn i YR × + ∈ . From (22) and (29) we have: 11 110 1 0 1 1 01 ˆˆˆ ˆ ˆˆˆˆˆ ii ii i i i iii i X PX PA X PX A PBU PA P X PP X A PBQ U A X X A B U −− ++ − = =++= + + +=++ (30a) and 11 ˆ ˆˆˆˆˆˆ ii i i i i i i Y VY VCX VDU VCP X VDQ U CX DU −− == + = + = + (30b) The Lyapunov system (30) is positive if and only if, the equivalent standard system is positive. By the theorem for the positivity of the standard discrete-time systems, the matrices 01 ˆˆ ˆ ˆˆ (),(),(),() T nn n n n AII A BI CI DI⊗+⊗ ⊗ ⊗ ⊗ have to be the matrices with nonnegative entries , so from (30) follows the hypothesis of the Theorem 5. □ 4.2 Asymptotic stability Consider the autonomous Lyapunov (P,Q,V) -cone-system: 10 1iii XAXXA + = + (31) where, + P, i Z i X ∈ ∈ . Definition 11. The Lyapunov (P,Q,V) -cone-system (15) is called asymptotically stable if: lim 0 i i X →∞ = for every 0 PX ∈ [...]... 0.1 + 0.4 + 0.25 = 0.75 > 0 ˆ ˆ ˆ and the matrices B , C , D have nonnegative entries For the instant i = 100 we have i +1 A + In N + 2 ∑ j =2 ( −1) j ⎛N⎞ ⎜ j ⎟ I n = 0 .82 68 < 1 ⎝ ⎠ 2 so the system is stable in the meaning of the the definition 18  188 Automation and Robotics The system is reachable and observable because the matrix PBQ −1 has n = 2 monomial −1 columns, and the matrix VCP has n = 2 monomial... no 4, 2002, pp 367- 386 Podlubny I., Dorcak L., Kostial I (1997) On fractional derivatives, fractional-order systems and PIλDµ-controllers Proceedings of 36th IEEE Conf Decision and Control, San Diego, CA, 1997, pp 4 985 -4990 Przyborowski P (2008a) Positive Fractional Discrete-time Lyapunov Systems, Archives of Control Sciences,vol. 18( LIV), 20 08, No 1, pp 5- 18 Przyborowski P (2008b) Fractional discrete-time... sides, ideal gas, adiabatic changes and constant viscous friction Other works have been focused in friction parameter identification techniques of cylinder pneumatic (Wang & Wang, 2004), dynamic modelling and simulation (Jozsef & Claude, 192 Automation and Robotics 2003), analytic and experimental research (Henri & Hollerbach, 19 98) and the development of robotic hands using cylinder pneumatics Flexible... Theory and Applications, J Wiley, New York Ferreira N.M.F & Machado I.A.T.(2003) Fractional-order hybrid control of robotic manipulators Proceedings of 11th Int Conf Advanced Robotics, ICAR’2003, Coimbra, Portugal, pp 393-3 98 Gałkowski K (2005) Fractional polynomials and nD systems Proceedings of IEEE Int Symp Circuits and Systems, ISCAS’2005, Kobe, Japan Kaczorek T (19 98) Vectors and Matrices in Automation. .. ( 38) j =1,…, n are the Metzler matrices satisfying ˆ 0 ˆ1 akk + all + N ≥ 0 for every k , l = 1, … , n (39) and n ˆ ˆ ˆ B = PBQ −1 ∈ R+ ×m , C = VCP −1 ∈ R+p×n , D = VDQ −1 ∈ R+p×m (40) Proof: Let: ˆ ˆ ˆ (41) X i = PX i , U i = QU i , Yi = VYi n× n m× n ˆ ˆ From definition 2 it follows that if X i ∈ P then X i ∈ R+ , if U ∈ Q then U i ∈ R+ , i and if p× n ˆ Yi ∈ V then Yi ∈ R+ 182 Automation and Robotics. .. discrete-time Lyapunov systems, Submitted to The Control and Cybernetics Journal Klamka J (1991) Controllability of dynamical systems, Kluwer Academic Publ Dordrcht Miller K S & Ross B (1993) An Introduction to the Fractional Calculus and Fractional Differential Equations Willey, New York 190 Automation and Robotics Moshrefi-Torbati M & Hammond K (19 98) Physical and geometrical interpretation of fractional operators,... eigenvalues: λ1 = −1, λ2 = −3 A1 and has the eigenvalues: μ1 = −4, therefore the system is asymptotically stable, since all the sums of the eigenvalues: (λ1 + μ1 ) = −5, (λ1 + μ2 ) = −2, (λ2 + μ1 ) = −7, (λ2 + μ2 ) = −4 have negative real parts For this system the reachability matrix ⎡e( −t f +τ ) Rf = ∫ ⎢ 0 ⎢ ⎣ 0 tf 2 ⎤ ⎥dτ 3( − t f +τ )2 ⎥ 4e ⎦ 0 186 Automation and Robotics and the observability matrix...1 78 Automation and Robotics Theorem 6 Let us assume that λ1 , λ2 ,… λn the eigenvalues of the matrix are the eigenvalues of the matrix A0 and μ1 , μ2 ,… μn A1 The system (31) is stable if and only if: λi + μ j < 1 for i , j = 1, 2, , n (32) Proof: The theorem results directly from the theorem for asymptotic stability of standard systems (Kaczorek T., 2001), since... Definition 16 The Lyapunov Yi and U i = 0, i ∈ Z + for (P,Q,V) -cone-system (22) is called observable, if there exists a natural number q ≥ 1 , such that the system (22) is observable in q -steps Theorem 9 The Lyapunov (P,Q,V) -cone-system (22) is observable: a) For A1 satisfying the condition XA1 = A1 X , i.e A1 = aI n , a ∈ R , if and only if the matrix: 180 Automation and Robotics ⎡ VCP −1 ⎤ ⎢ (VCP... if and only if the matrix: Rn = [ PBQ −1 contains b) For n A0 ( PBQ −1 ) linearly independent monomial columns, A0n −1 ( PBQ −1 )] A0 = PA0 P −1 + A1 A1 ≠ aI n , a ∈ R , if and only if the matrix PBQ −1 contains n (33) linearly independent monomial columns Proof: From (26),( 28) ,(29) and from the definitions 2 and 12, we have that the discrete-time Lyapunov (P,Q,V) -cone-system (22) is reachable if and . e − −− −− − = ( 18) for 0 [, ] f ttt∈ . Proof: If f R is the monomial matrix, then there exists 1 nn f R R − × + ∈ and the input ( 18) is well- defined. Using (8) and ( 18) we obtain: Linear. ˆ nn i X R × + ∈ , if Q i U ∈ then ˆ mn i UR × + ∈ , and if V i Y ∈ then ˆ pn i YR × + ∈ . Automation and Robotics 182 From (34) and (41) we have: 11 111 1 11 11 1 01 0 1 01 ˆˆ (1) (1) ˆˆ. have negative real parts. For this system the reachability matrix 2 2 () 3( ) 0 e0 04e f f f t t f t R d τ τ τ −+ −+ ⎡ ⎤ ⎢ ⎥ = ⎢ ⎥ ⎣ ⎦ ∫ Automation and Robotics 186 and the observability