Master-Slave Synchronization using. Dynamic Output Feedback. Kardinaal Mercierlaan 94, B-3001 Leuven (Heverlee), Belgium

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1 Master-Slave Synchronization using Dynamic Output Feedback J.A.K. Suykens 1, P.F. Curran and L.O. Chua 1 Katholieke Universiteit Leuven, Dept. of Electr. Eng., ESAT-SISTA Kardinaal Mercierlaan 94, B-1 Leuven (Heverlee), Belgium Tel: /16/ Fax: /16/ johan.suykens@esat.kuleuven.ac.be 145, Electronic and Electrical Eng. University College, Beleld, Dublin 4, Ireland Tel: (5) Fax: (5) pcurran@acadamh.ucd.ie Department of Electrical Engineering and Computer Science University of California at Berkeley, Berkeley, CA 947, USA Tel: +1 (51) 64 9 Fax: +1 (51) chua@fred.eecs.berkeley.edu ( Running title: Dynamic output feedback synchronization ) 1

2 Abstract A method of linear dynamic output feedback for master-slave synchronization of two identical Lur'e systems is introduced. In this scheme, synchronization is obtained using one or at least fewer measurement signals and control signals than the number of state variables of the Lur'e system. A sucient condition for global asymptotic stability of the error system is derived from a quadratic Lyapunov function and is expressed as a matrix inequality. The dynamic controller is designed by solving a constrained nonlinear optimization problem. The method is demonstrated on Chua's circuit and a hyperchaotic circuit consisting of -double scroll cells.

3 1 Introduction Present master-slave synchronization schemes for general classes of nonlinear systems, such as Lur'e systems, often assume full static state feedback, using the dierence between the full state vectors as an error signal to synchronize the slave to the master system [Wu & Chua, 1994; Curran & Chua, 1996; Suykens & Vandewalle, 1996]. Only in more specic cases, such as Chua's circuit, static state feedback has been applied using fewer state variables [Kapitaniak et al., 1994]. A natural question one may raise then is how synchronization could be obtained for a general class of nonlinear systems, based e.g. on a single or fewer measurement signals and fewer control signals. In many practical situations measuring all the state variables of the system is inconvenient or even impossible. A motivating application is in secure communication, where for implementational reasons the use of one single transmission signal is preferable over transmission of the full state vector of the system [Hasler, 1994; Wu & Chua, 1994]. It is well-known that there exists a close relationship between synchronization and control problems [Wu & Chua, 1994]. Therefore, in order to obtain synchronization based on fewer signals, we will adopt the idea of dynamic output feedback from control theory. When one is confronted with the problem of controlling some plant for which not all the state variables are measurable (lack of full state information), one either constructs an observer system to estimate the state of the plant, used in combination with state feedback, or one directly applies a dynamic output feedback law, from the measurable outputs to the actuator inputs. In linear control theory these ideas are particularly well developed [Boyd & Barratt, 1991; Maciejowski, 1989]. An example is the LQG (Linear Quadratic Gaussian) regulator problem, which considers the optimal control of a linear system corrupted by process and measurement noise. The optimal solution by means of output feedback is given by a linear dynamic controller. For this controller the so-called separation principle holds, which means that one can split the controller into two parts: a

4 dynamical observer system and a static state feedback applied to the observer estimated state [Boyd & Barratt, 1991; Maciejowski, 1989]. Although we are dealing in this paper with nonlinear rather than linear systems, we will investigate the application of a linear dynamic output feedback mechanism in order to synchronize two identical nonlinear systems. We consider a class of nonlinear systems which can be represented in Lur'e form [Khalil, 199; Vidyasagar, 199]. Examples of Lur'e systems are Chua's circuit [Chua et al., 1986; Chua, 1994; Madan, 199] and piecewiselinear versions of n-double scroll circuits [Suykens & Vandewalle, 199; Arena et al., 1996]. Also arrays which consist of such circuits as cells with linear coupling between the cells can be represented as Lur'e systems. Examples are the double-double scroll attractor [Kapitaniak & Chua, 1994] and the n-double scroll hypercube CNN [Suykens & Chua, 1997]. Furthermore a Lur'e representation for generalized cellular neural networks has been derived in Guzelis & Chua [199]. In this paper we derive a sucient condition for synchronization of Lur'e systems based on a quadratic Lyapunov function. The condition is expressed as a matrix inequality (see Boyd et al. [1994] for an overview of linear matrix inequalities in systems and control theory) on which the design of the output feedback controller is based by solving a constrained nonlinear optimization problem. The controller design is illustrated both on a chaotic system (Chua's circuit) and a hyperchaotic system (coupled -double scroll cells). The dynamic control laws for achieving synchronization obtained are fairly simple and are of rst and second order respectively. This paper is organized as follows. In Sec. we introduce the master-slave synchronization scheme with dynamic output feedback. In Sec. we derive the error system and a sucient condition for global asymptotic stability based on a quadratic Lyapunov function. In Sec. 4 design of the dynamic output feedback controller, based on the matrix inequality, is explained. Finally, in Sec. 5 the method is illustrated on a chaotic and a hyperchaotic Lur'e system, respectively Chua's circuit and a coupled system with -double scroll cells. 4

5 Synchronization Scheme Let us introduce the following master-slave synchronization scheme M : 8>< >: _x = Ax + B(Cx) p = Hx S : C : 8>< >: 8>< >: _z = Az + B(Cz) + Du q = Hz _ = E + G(p? q) u = M + N(p? q) (1) which consists of a master system M, a slave system S and a controller C (Fig. 1). The master system is a Lur'e system with state vector x R n and matrices A R nn, B R nn h, C R n hn. A Lur'e system is a linear dynamical system, feedback interconnected to a static nonlinearity (:) that satises a sector condition [Khalil, 199; Vidyasagar, 199] (here it has been represented as a recurrent neural network with one hidden layer, activation function (:) and n h hidden units [Suykens et al., 1996]). We assume that (:) : R n h 7! R nh is a diagonal nonlinearity with i (:) belonging to sector [; k] for i = 1; :::; n h. The output vector of the master system is p R l, with l n. The slave system consists of an identical Lur'e system with state vector z R n, but is controlled by means of the control vector u R m through the matrix D R nm. The signal u is the output of a linear dynamic output feedback controller. The input of this controller is the dierence between the outputs of the master and the slave system, i.e. p and q. The linear dynamic controller has the state vector R nc and consists of the matrices E R ncnc, G R ncl, M R mnc, N R ml. In previous work on master-slave synchronization only full static state error feedback u = F s (x? z) or static output feedback u = F o (p? q) have been considered with F s R nn, F o R ml [Wu & Chua, 1994; Kapitaniak et al., 1994; Curran & Chua, 1996; 5

6 Suykens & Vandewalle, 1996]. Error system and global asymptotic stability We dene the error signal e = x? z, which yields the error system: 64 _e _ 75 = 64 A? DNH?DM GH E e B 75 (Ce; z) () with nonlinearity (Ce; z) = (Ce + Cz)? (Cz). Assume that the nonlinearity (Ce; z) belongs to sector [; k] [Curran & Chua, 1996; Suykens & Vandewalle, 1996]: i(c T i e; z) c T i e = i(c T i e + ct i z)? (ct i z) c T i e k; 8e; z; i = 1; :::; n h (c T i e 6= ): () The following inequality then holds [Boyd et al., 1994; Khalil, 199; Vidyasagar, 199]: i (c T i e; z) [ i (c T i e; z)? kc T i e] ; 8e; z; i = 1; :::; n h : (4) It follows from the mean value theorem that for dierentiable (:) the sector condition [; k] on (:) corresponds to [Curran & Chua, 1996]: d d i(; z) k; 8; z; i = 1; :::; n h : (5) We are interested to see under what condition the error system is globally asymptotically stable, which means that whatever the choice of the initial states x(); z(); () the error k [x? z; ] k! as t! 1. Dening the state vector = [e; ] and taking a positive denite quadratic Lyapunov function (which is radially unbounded) V () = T P = [e T T ] 64 P 11 P e 75 ; P = P T > ; (6) P 1 P a sucient condition for global asymptotic stability of Eq. () is derived in the following Theorem. 6

7 Theorem. Let = diagf i g be a diagonal matrix with i for i = 1; :::; n h. Then a sucient condition for global asymptotic stability of the error system of Eq. () is given by the matrix inequality Y = Y T = where 64 Y 11 Y 1 Y 1 : Y Y 75 < (7) : : Y Y 11 = (A? DNH) T P 11 + P 11 (A? DNH) + H T G T P 1 + P 1 GH Y 1 = (A? DNH) T P 1 + H T G T P? P 11 DM + P 1 E Y 1 = P 11 B + kc T Y = E T P + P E? M T D T P 1? P 1 DM Y = P 1 B Y =?: Proof: Taking the time-derivative of the Lyapunov function and applying the S-procedure [Boyd et al., 1994], using the inequalities of Eq. (4), one obtains: _V = _ T P + T P T P + T P _? P i i i ( i? kc T i e) [(A? DNH)e? DM + B] T (P 11 e + P 1 ) + (E + GHe) T (P 1 e + P ) +(e T P 11 + T P 1 )[(A? DNH)e? DM + B] + (e T P 1 + T P )(E + GHe)? T (? kce) T Y < where = [e; ; ]. The latter expression is negative for all non-zero provided Y is negative denite. 7

8 4 Controller Design The dynamic output feedback controller C can be designed on the basis of matrix inequality Eq. (7) by solving the following nonlinear optimization problem: min max [Y (E; G; M; N; P; )]; E;G;M;N;P; such that 8>< >: P = P T > and diagonal where max [:] denotes the maximal eigenvalue of a symmetric matrix. When the maximal eigenvalue of Y is negative a feasible point to the matrix inequality is obtained, resulting in a controller which synchronizes the Lur'e systems. However, the optimization problem of Eq. (8) is non-convex. It may also become non-dierentiable when the two largest eigenvalues of the matrix Y coincide. Convergent algorithms for this kind of non-dierentiable optimization problem have been described e.g. in Polak & Wardi [198]. The constraint P > can be eliminated by considering the parameterization P = Q T Q. A similar idea applies to. The optimization problem becomes then min max [Y (E; G; M; N; Q; )]: (9) E;G;M;N;Q; (8) 5 Examples 5.1 Chua's circuit In this example we consider master-slave synchronization of two identical Chua's circuits using linear dynamic output feedback. We take the following representation of Chua's circuit: 8 > <>: with nonlinear characteristic _x 1 = [x? h(x 1 )] _x = x 1? x + x _x =?x (1) h(x 1 ) = m 1 x (m? m 1 ) (jx 1 + cj? jx 1? cj) (11) 8

9 and parameters = 9, = 14:86, m =?1=7, m 1 = =7 in order to obtain the double scroll attractor [Chua et al., 1986; Chua, 1994; Madan, 199]. The nonlinearity '(x 1 ) = 1 (jx 1 + cj? jx 1? cj) (linear characteristic with saturation) belongs to sector [; 1]. Hence Chua's circuit can be interpreted as the Lur'e system _x = Ax + B'(Cx) where A = 64?m 1 1?1 1??(m? m1) 75 6 ; B = 4 ; C = [1 ]: (1) 75 Suppose now that we measure the rst state variables x 1 and z 1 only in order to synchronize the circuits and that we take just one control signal to control the slave system. This corresponds to the choice H = [1 ], D = [1; ; ] (l = m = 1). Controllers of order 1, and have been designed based on the optimization problem of Eq. (9). We report the results here for the simplest controller with n c = 1. A -norm constraint on the controller parameter vector [E(:); G(:); M(:); N(:)] (< 1) has been used for Eq. (9), where : denotes a columnwise scanning of a matrix. Sequential quadratic programming [Fletcher, 1987] has been applied in order to optimize Eq. (9) using Matlab's optimization toolbox (function constr). As starting point for the iterative procedure, a random controller parameter vector has been chosen according to a normal distribution with zero mean and variance.1. For the matrix Q a square random matrix was chosen according to the same distribution but with variance equal to. The matrix has been initialized as 1 I. Simulation results for master-slave synchronization of the Chua's circuits are shown in Figs. -4 for the resulting controller 64 E G M N 75 = 64?:14 :649?:8758 9:994 The systems have been simulated using a Runge-Kutta integration rule with adaptive step size (ode in Matlab) [Parker & Chua, 1989]. 75 : 9

10 8>< 5. Hyperchaotic system with -double scroll cells In this example we consider master-slave synchronization for a special case of the n-double scroll hypercube CNN, which consist of two -double scroll cells with unidirectional coupling between the cells [Suykens & Chua, 1997]. The system is described by _x 1 = [x? h(x 1 )] _x = x 1? x + x _x =?x _x 4 = [x 5? h(x 4 )] + K(x 4? x 1 ) (1) >: _x 5 = x 4? x 5 + x 6 _x 6 =?x 5 with, as nonlinear function, the piecewise linear characteristic: h(x 1 ) = m n?1 x X n?1 i=1 (m i?1? m i ) (jx 1 + c i j? jx 1? c i j); (14) consisting of (n? 1) breakpoints, where n is a natural number which determines the number of scrolls obtained. An isolated cell behaves as a -double scroll for = 9, = 14:86, m =?1=7, m 1 = =7, m =?4=7, m = m 1, c 1 = 1, c = :15, c = :6. For weak coupling between the two cells, hyperchaos is obtained with two positive Lyapunov exponents, as shown in Suykens & Chua [1997]. We choose K = :1 for the unidirectional coupling constant in Eq. (1). A Lur'e representation _x = Ax + B'(Cx) for the system Eq. (1) is given by:?m 1 b 11 b 1 b 1 1?1 1 A =??m 1 ; B = b 11 b 1 b 1 64?K 1?1 + K 1? (15) 75 1

11 1 1 C = with b 11 =?(m? m 1 ), b 1 =?(m 1? m ), b 1 =?(m? m ). The nonlinearity '(:) : R 6 7! R 6 belongs to sector [; 1] with ' i (x 1 ) = 1(jx 1 + c i j? jx 1? c i j) (i = 1; ; ) and ' i (x 4 ) = 1 (jx 4 + c i? j? jx 4? c i? j) (i = 4; 5; 6). We consider two identical Lur'e systems Eq. (15) for the master and slave system. In order to synchronize the systems by means of linear dynamic output feedback, we dene two outputs and two control signals (l = m = ) as: H = ; D = : (16) We demonstrate the method for a second order controller (n c = ). A -norm constraint on the controller parameter vector [E(:); G(:); M(:); N(:)] (< 8) has been taken into account for Eq. (9). As starting point for sequential quadratic programming in order to optimize Eq. (9), a random controller parameter vector has been chosen according to a normal distribution with zero mean and variance.1. For the matrix Q a square random matrix was chosen according to the same distribution but with variance equal to. The matrix has been initialized as 1 I. Simulation results are shown in Figs. 5-8 for the resulting 11

12 controller 64 E G M N :79?4:755?:5?4: = 8:488?19:59?8:667 6:477 4?7:9?:984 45:491 5:58?8:879 7:9657?16:769 4:817 : 75 6 Conclusion In this paper a linear dynamic output feedback mechanism has been introduced for masterslave synchronization of Lur'e systems. Rather than using the error between the full state vectors to synchronize the systems, the scheme enables one to use fewer measurement signals and fewer control signals than the number of state variables of the Lur'e systems. A sucient condition for global asymptotic stability of the error system has been derived based on a quadratic Lyapunov function. The controller design is achieved by solving a constrained nonlinear optimization problem, based on the derived matrix inequality. The method has been illustrated on Chua's circuit and a hyperchaotic system that consists of coupled -double scroll cells. 1

13 Acknowledgment This research work was carried out at the University of California at Berkeley, in the framework of the Belgian Programme on Interuniversity Poles of Attraction, initiated by the Belgian State, Prime Minister's Oce for Science, Technology and Culture (IUAP-17) and in the framework of a Concerted Action Project MIPS (Modelbased Information Processing Systems) of the Flemish Community. The work is supported in part by the Oce of Naval Research under grant N14-89-J-14 and the Fulbright Fellowship Program. 1

14 References Arena, P., Baglio, P., Fortuna, F. & Manganaro, G. [1996] \Generation of n-double scrolls via cellular neural networks," Int. J. Circuit Theory and Applications, 4, Boyd, S., Barratt, C. [1991] Linear controller design, limits of performance, Prentice- Hall. Boyd, S., El Ghaoui, L., Feron, E. & Balakrishnan, V. [1994] Linear matrix inequalities in system and control theory, SIAM (Studies in Applied Mathematics), Vol.15. Chua, L.O., Komuro, M. & Matsumoto, T. [1986] \The Double Scroll Family," IEEE Trans. Circuits and Systems-I, (11), Chua, L.O. [1994] \Chua's circuit 1 years later," Int. J. Circuit Theory and Applications,, Curran, P.F. & Chua, L.O. [1996] \Absolute stability theory and the synchronization problem," Int. J. Bifurcation and Chaos, to be published. Fletcher, R. [1987] Practical methods of optimization, Chichester and New York: John Wiley and Sons. Guzelis, C. & Chua, L.O. [199]. \Stability analysis of generalized cellular neural networks," International Journal of Circuit Theory and Applications, 1, 1-. Hasler, M. [1994] \Synchronization principles and applications," Circuits & Systems: Tutorials IEEE-ISCAS '94, Kapitaniak, T. & Chua, L.O. [1994] \Hyperchaotic attractors of unidirectionallycoupled Chua's Circuits," Int. J. Bifurcation and Chaos, 4(), Kapitaniak, T., Chua, L.O. & Zhong, G.-Q. [1994] \Experimental synchronization of chaos using continuous controls," Int. J. Bifurcation and Chaos, 4(),

15 Khalil, H.K. [199] Nonlinear Systems, New York: Macmillan Publishing Company. Maciejowski, J.M. [1989] Multivariable feedback design, Addison-Wesley. Madan, R.N. (Guest Editor) [199] Chua's Circuit: A Paradigm for Chaos, Signapore: World Scientic Publishing Co. Pte. Ltd. Parker, T.S. & Chua, L.O. [1989] Practical numerical algorithms for chaotic systems, New York: Springer-Verlag. Polak, E. & Wardi, Y. [198] \Nondierentiable optimization algorithm for designing control systems having singular value inequalities," Automatica, 18(), Suykens, J.A.K. & Vandewalle, J. [199] \Generation of n-double scrolls (n= 1,,,4,:::)," IEEE Trans. Circuits and Systems-I, 4(11), Suykens, J.A.K., Vandewalle, J.P.L. & De Moor, B.L.R. [1996] Articial Neural Networks for Modelling and Control of Non-Linear systems, Boston: Kluwer Academic Publishers. Suykens, J.A.K. & Vandewalle, J. [1996] \Master-slave synchronization of Lur'e systems," to appear in Int. J. Bifurcation and Chaos. Suykens, J.A.K. & Chua, L.O. [1997] \n-double scroll hypercubes in 1-D CNNs," to appear in Int. J. Bifurcation and Chaos. Vidyasagar, M. [199] Nonlinear Systems Analysis, Prentice-Hall. Wu, C.W. & Chua, L.O. [1994] \A unied framework for synchronization and control of dynamical systems," Int. J. Bifurcation and Chaos, 4(4),

16 Captions of Figures Fig. 1. This Figure illustrates the dierence between full static state feedback (Top) and dynamic output feedback (Bottom) for master-slave synchronization of Lur'e systems: (Top) the slave system S is synchronized to the master system M by taking the dierence between the state vectors x; z R n and applying a feedback matrix. (Bottom) output variables p; q R l are dened with l n. A linear dynamic output feedback controller is taken instead of a static feedback controller. For the control vector u R m one has m n. Fig.. This Figure shows master-slave synchronization of two identical Chua's circuits using a rst order linear dynamic output feedback controller. One single output and control signal have been used. (Top) Shown are the state variables of the Chua's circuits with respect to time for a randomly chosen initial condition: (-) master system, (- -) slave system. (Bottom) three-dimensional view on the double scroll attractor generated at the master system. Fig.. Chua's circuit (continued). (Top) error signal x?z with respect to time: (-) x 1?z 1, (- -) x? z, (-.) x? z. (Bottom) state variable of the controller with respect to time. Fig. 4. Chua's circuit (continued). Control signal u with respect to time, applied to the slave system using the rst order linear dynamic output feedback controller. Fig. 5. This Figure shows master-slave synchronization between two identical hyperchaotic systems, that consist of two unidirectionally coupled -double scroll cells, using a second order dynamic output feedback controller. (Top) Shown are the state variables of the 6- dimensional circuit with respect to time for a randomly chosen initial condition: (-) master 16

17 system, (- -) slave system. (Bottom) three-dimensional view on the -double scroll attractor generated at the rst cell. Fig. 6. -double scroll cells (continued). For weak coupling between the two -double scroll cells, a -double scroll square is obtained in the common cell space (x 1 ; x 4 ) as a special case of the n-double scroll hypercube CNN [Suykens & Chua, 1997]. Fig. 7. -double scroll cells (continued). (Top) error signal x? z with respect to time: (-) x i? z i for i = 1; ;, (- -) x i? z i for i = 4; 5; 6. (Bottom) state variables of the controller with respect to time. Fig. 8. -double scroll cells (continued). Control signals u with respect to time, applied to the slave system using the linear dynamic output feedback controller. 17

18 M x + - u S z M p + - u S q C Fig

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Discrete time Generalized Cellular. Johan A.K. Suykens and Joos Vandewalle. Katholieke Universiteit Leuven

Discrete time Generalized Cellular Neural Networks within NL q Theory Johan A.K. Suykens and Joos Vandewalle Katholieke Universiteit Leuven Department of Electrical Engineering, ESAT-SISTA Kardinaal Mercierlaan