Open access peer-reviewed chapter

Nonlinear Unknown‐Input Observer‐Based Systems for Secure Communication

By Robert N.K. Loh and Manohar K. Das

Submitted: December 14th 2016Reviewed: April 14th 2017Published: November 22nd 2017

DOI: 10.5772/intechopen.69239

Downloaded: 838


Secure communication employing chaotic systems is considered in this chapter. Chaos‐based communication uses chaotic systems as its backbone for information transmission and extraction, and is a field of active research and development and rapid advances in the literature. The theory and methods of synchronizing chaotic systems employing unknown input observers (UIOs) are investigated. New and novel results are presented. The techniques developed can be applied to a wide class of chaotic systems. Applications to the estimation of a variety of information signals, such as speech signal, electrocardiogram, stock price data hidden in chaotic system dynamics, are presented.


  • chaotic secure communication
  • underwater acoustic communication
  • chaos
  • unknown input observers
  • nonlinear observers
  • reduced‐order observers

1. Introduction

With the advances in computing and communication technologies, among others, underwater acoustic communication (UAC) techniques [16] have emerged as the predominant mode of underwater communication because of its one key advantage over conventional electromagnetic communication, namely, relatively low attenuation of acoustic waves in water. However, their performance is severely affected by a number of factors, including limited channel bandwidth, time‐varying channel characteristics, complex ambient noise, and multipath distortion that results from multiple reflections of sound waves from top and bottom surfaces of water, especially in a relatively shallow waterbody.

Over the past decade, chaos‐based underwater acoustic communication (CUAC) techniques have attracted a lot of interest from a number of researchers [712], because such techniques are potentially more cost‐effective (for example, requiring lesser number of component modules) compared with conventional communication schemes. The CUAC techniques proposed to date can be broadly divided into two categories, namely, coherent detection based CUAC methods [7], and non‐coherent detection based CUAC techniques. The coherent detection based methods rely on synchronization to reconstruct a copy of the transmitted signal at the receiver end, whereas non‐coherent detection methods [812] utilize a variety of data recovery methods without requiring any synchronized reconstruction of the transmitted message.

In this chapter, we focus our attention on the synchronization based CUAC techniques, especially on observer‐based synchronization methods, because the underlying theory is very well understood and has proven to be reliable and robust in many control applications. Also, such methods may potentially turn out to be easier to implement, as compared with many non‐coherent CUAC techniques.

At the outset, we should point out that the main goal of this chapter is to present the fundamental concepts of observer‐based chaotic synchronization and their applications to secure chaotic communication. With this in mind and owing to space limitation, we omit discussion of the robustness issues [1323] of such techniques here. However, we should point out that the theory of robust observer design in the presence of noise and uncertainties has been well researched in control literature, and these ideas are deemed to be useful for synchronized based CUAC as well [1823].

The methodologies used for CUAC have many things in common with chaos‐based wired and wireless communication. Research and development in these fields have been advancing rapidly in the literature [716]. In contrast to conventional communication systems which use sinusoidal carriers to transmit information, chaos‐based communication uses chaotic systems as its backbone for information transmission and recovery. The advantages of employing chaos‐based systems include, among others, (i) the communication is difficult to detect and decrypt; (ii) the transmission is hidden from unauthorized receivers; (iii) the communication is more resistant to jamming and interferences because of the broadband characteristics of the chaos‐based carriers. The advantages above are due to the following characteristics: (i) a chaotic system is dissipative; (ii) chaotic systems have unstable equilibrium points; (iii) its trajectories are aperiodic and bounded; and (iv) its trajectories have a sensitive dependence on their initial conditions, i.e., trajectories originated from slightly different initial conditions will soon become totally different. We remark that some of these characteristics may, in fact, be undesirable.

The organization of this chapter is as follows. Section 2 introduces three nonlinear chaotic systems that are utilized for designing chaotic communication systems in follow‐up sections. Next a general discussion of unknown input observers is presented in Section 3. Section 4 presents the theory and design of unknown input observers for chaotic secure communication. Finally, the conclusions and plan for future research are provided in Section 5.


2. Nonlinear systems with application in chaotic communication

Consider a general nonlinear system described by


where xRnis the system state vector, yRmthe output measurement, dRran unknown disturbance vector which can be treated as a message vector that carries useful information; f:Rn×RrRnis a smooth vector field, h:Rn×RrRma smooth function, f(0,0)=0and h(0,0)=0.

The unknown disturbance din (1) is assumed to be generated by the exosystem


where mRris the message state, MRr×ris a “picking matrix” that picks the appropriate components miof mto form d, fm:Rr×RnRris a smooth vector field, and fm(0,0)=0.

Eqs. (1) and (2) is widely used for the design of linear and nonlinear observers, unknown input observers (UIO), and unknown input observers for secure communication [2446]. When applied to the design of unknown input observers (UIOs) for secure communication based on chaotic systems, (1) and (2) can be combined and expressed as

{x˙=f(x,m)=f(x)+Bm(x)Mm=Ax+g(x)+Bm(x)Mm,m˙=fm(m,x)=Amm+Ψx,   y=h(x,m),   (d=Mm), E3

where Axis the linear part of f(x), g(x)Rn×1and Bm(x)Rn×r, while m(d=Mm)is now treated as the message signal, and fm(m,x)=Amm+Ψx, where AmRr×ris a constant matrix. The linear model in the second equation is commonly used in the literature, see for example [25]. In many applications, the message model can be simplified by setting Am=0and Ψ=0. Further, (3) may become a system with state‐dependentor multiplicativeand/or additivemessage signals depending on Bm(x). If Bm(x)=Bm, where Bmis a constant matrix, then (3) is a system with only additivemessages.

The following three chaotic systems in the form of (3) will be utilized for designing chaotic communication systems in this chapter.

(1) Rossler system [47]

The Rossler system described by


can be modified by chaotic parameter modulation resulting in a system with state‐dependent (multiplicative)and additivemessages as


where Ψ=0, and the chaotic parameters are given by {a, b, c}={0.2, 0.2, 5.7}[43] or {0.398, 2, 4}[42]. Note that the Rossler system (4) contains only one nonlinear term. See also [48] for more details.

(2) Genesio‐Tesi system [49]

The Genesio‐Tesi system given by


can be modified in the form of (3) with state‐dependent and additive message signals as,


where Ψ=0, a, band care the chaotic parameters satisfying ab<cand are given by {a, b, c}={1.2, 2.92, 5.7} [49]. Note that, without the nonlinear term x12, the Genesio‐Tesi system (6) is a linear time‐invariant (LTI) system and is a state‐space realization of the transfer function G(s)=1/(s3+as2+bs+c).

(3) Chua circuit [50]

The Chua circuit


may be modulated in a form with state‐dependent and additive messages as


where α=10, β=16and c= −0.14 are the chaotic parameters. A different modification scheme is given in Ref. [51].

It is noted that, although chaotic systems are sensitive to variations of their chaotic parameters p={pi}, most systems do accommodate suitable modifications of some of these parameters. This property has precisely been exploited for the designs of UIOs for secure communication and many control‐based synchronization schemes in the literature.


3. General unknown‐input observers (UIOs)

Consider (3), which can be expressed more compactly as,



w=[xm], fw(w)=[f(x,m)fm(m,x)], and h(w)=h(x,m).

Consider a Luenberger‐like nonlinear observer for (3) given by [2731, 34],


or more compactly as, with (10),


where w^=[x^m^]is an estimate of w=[xm], fw(w^)=[f(x^,m^)fm(m^,x^)], and Lo(·)=[L1o(·)L2o(·)]is the observer gain matrix to be determined such that the observer has desirable properties, such as generating an estimate w^(t)that can track (or converge to) w(t)asymptotically in the face of unknown disturbances.

Although (11) and (12) provide a more intuitive form for a Luenberger‐like observer, a linear and nonlinear UIO can be expressed in an alternate form as [31, 52],

{q˙=fq(q,y),   q(0)=qo,w^=φ(q,y),   w^=[x^m^],E13

where qRn, fq:Rn×RmRnis a smooth vector field, φa smooth function, fq(0,0)=0and φ(0,0)=0.

Three classes of UIOs can be distinguished from the extended state estimate w^, namely, (i) if w^=[x^m^], then (13) is a full‐order UIOthat addresses the estimation of the entire system vector xand message vector m; (ii) if w^=[x^2m^], where x^=[x1x^2], x1is known and x^2is an estimate of x2, then (13) is a reduced‐order UIOthat deals with partial‐state and message estimations; and (iii) if w^=m^, where the complete state vector x(t)is known for all t, then (13) is an UIO for only message estimation.

The design of all the three classes of UIOs discussed above for secure communication will be addressed in Section 4.


4. Unknown‐input observers (UIOs) for chaotic secure communication

The analysis and design of UIOs for secure communication using a drive‐response scheme in this section will be based on (10)–(13). Hence, (3) or (10) will serve as the drive system, while (11), (12) or (13) as the response system.

In the drive‐response chaotic communication theory and applications, one of the most important issues is synchronization, which is closely related to the stability of the UIO. Synchronization is a property of the estimation error w˜given by


where x˜=xx^and m˜=mm^.

Definition 1: Synchronization

The drive system(3) or (10) and the UIO response system(11), (12) or (13) are said to be synchronized if the estimation errorw˜given by(14) satisfieslimt||w˜(t)||=limt||w(t)w^(t)||=0,

i.e., the UIO is capable of generating an estimatew^(t)that tracksw(t)asymptotically ast. ∎

Remark 1:The condition limt||w˜(t)||=0is similar to the design of linear and nonlinear observers where it is crucial to ensure the asymptotic stability of the observers. ∎

To proceed, the estimation error (14) satisfies, with (10) and (12),

w˜˙=fw(w)fw(w^)Lo(·)[yh(w^)]   =fw(w)fw(ww˜)Lo(·)[h(w)h(ww˜)]   fw(w˜,x,m,y).E15

It follows that w˜=0is an equilibrium point of (15), i.e., fw(w)fw(w)Lo(·)[h(w)h(w)]=fw(0,x,m,y)=0for all x, mand y. Further, if a gain Lo(·)can be found such that (15) is asymptotically stable, then limt[w^(t)]=limt[w(t)], see for example [29].

The results above are stated in the following theorem.

Theorem 1:Consider the error Eq.(15) with an equilibrium point atw˜=0. If a gain matrixLo(·)exists such that(15) is asymptotically stable,then w^(t)w(t)ast. 

The next task is to determine the gain Lo(·)so that the candidate observer (12) or (13) becomes an asymptotically or exponentially stable observer. The matrix can take on various forms depending on the type of systems being considered and/or the design techniques. For a general nonlinear system, Lo(·)can be determined as a function of the estimate x^, i.e., Lo(·)=Lo(x^)[27, 28]; for nonlinear systems under Jacobian linearization, Lo(·)can be obtained as a constant matrix Lo[29, 30]; for extended Kalman‐Bucy filtering using Jacobian linearization, the filter gain matrix can be approximated by its steady‐state value Lo. We shall focus on Jacobian linearization in Section 4.1 below with applications to full‐order UIOs for state and message estimations using constant gain Lo(·)=Lo. Section 4.2 addresses the design of reduced‐order UIOs for message estimation, while the design of reduced‐order UIOs for partial‐state and message estimations is considered in Section 4.3.

4.1. Jacobian linearization: full‐order UIO

Linearization of (3) or (10) about the equilibrium point wo=0yields



A=fx(x,m)|wo=0, BmM=fm(x,m)|wo=0,   (Bm=Bm(0)), Ψ=fmx(m,x)|wo=0, Am=fmm(m,x)|wo=0, C=hx(x,m)|wo=0, Cm=hm(x,m)|wo=0.

The resulting linearized system is given by


The following assumption is crucial to the construction of UIOs.

Assumption 1: Observability

The pair [A¯,C¯]in (17) is observable, i.e.,


where Ois the observability matrix


An observer can be constructed for (17) if and only if [A¯,C¯]is an observable pair. Hence when the Jacobian linearization method yields a pair [A¯,C¯]that is not observable, then the Jacobian linearization method is not applicable to the system under consideration. However, other methods may work, such as feedback linearization [53, 54].

Using (17), a linear UIO for full‐state and message estimation can be constructed as

{[x^˙m^˙]=A¯[x^m^]+[L1oL2o](yC¯[x^m^])      =(A¯LoC¯)[x^m^]+Loy,   [x^(0)m^(0)]=[x^om^o],y=C¯[xm],E18

where Lo=[L1oL2o]is the constant UIO gain matrix to be determined. Note that L1oRn×mand L2oRr×m, and (18) is simply a Luenberger observer [57]. Since [A¯,C¯]is an observable pair by Assumption 1, then Locan be determined, for example, by pole‐placement, such that (A¯LoC¯)is Hurwitz, i.e., all the eigenvalues of (A¯LoC¯)are located in the open left half‐complex plane.

Using (17) and (18), the estimation errors x˜=xx^and m˜=mm^satisfy

[x˜˙m˜˙]=(A¯LoC¯)[x˜m˜],   [x˜(0)m˜(0)]=[x˜om˜o],E19

which is exponentially stable, i.e., limt[x˜(t)]=0and limt[m˜(t)]=0exponentially for all x˜(0)and m˜(0). It follows that x^(t)x(t)and m^(t)m(t)exponentially.

Once a constant Lohas been determined, it can then be substituted into (12), whereby the resulting nonlinear UIO has the form

{w^˙=fw(w^)+Lo[yh(w^)],   w^(0)=w^o,y=h(w),E20

where w^(0)is an arbitrary initial condition. Further, (15) becomes

w˜˙=fw(w)fw(w^)Lo[yh(w^)]   =fw(w)fw(ww˜)Lo[h(w)h(ww˜)],E21

which can be linearized about w˜=0to give (19). Hence the dynamics of (21) close to the origin are well described by (19) for sufficiently small ||w^(0)||[30].

In summary, we have the following theorem.

Theorem 2:Let[A¯,C¯]be an observable pair so that there exists a constant gainLosuch that(A¯LoC¯)in(19) is Hurwitz. Then(20) is an exponentially stable dynamical system for sufficiently small||w^(0)||. Further,x^i(t)xi(t)and m^i(t)mi(t)imply that the drive system (10) and the UIO response system (20) are synchronized. ∎

Using (10) and (20), the overall chaotic system‐based UIO for full‐state and message estimations under the Jacobin linearization scheme can be implemented as

{w˙=fw(w),   w(0)=wo,w^˙=fw(w^)+Lo[yh(w^)],   w^(0)=w^o,y=h(x,m).E22

A block diagram for (22) is shown in Figure 1.

Figure 1.

Chaotic secure communication system under Jacobian linearization.

Example 1: Rossler system [47]

Consider the Rossler system with state‐dependent and additive messages described by (5), with the output arranged as y=[x1+m1x3+m1]T,

{x˙=[x2x3x1+ax2(cm1)x3+x1x3+(b+m2)]=[0111a000c]Ax+[00m1x3+x1x3+b]g(x,m1)+[000001]Bmm,m˙=Amm,y=[x1+m1x3+m1]=C¯[xm],   (C¯=[CCm], C=[100001], Cm=[1010]).E23

The preceding equation can be expressed as


where Am=0for simplicity. Note that [A¯,C¯]is an observable pair for all Am.

It can be shown that the Rossler system x˙=f(x)given by (4) has two equilibrium points, for c24ab0,


The stability status of x1oand x2ocan be determined by checking the eigenvalues of the Jacobian matrices A1o=fx(x1o)and A2o=fx(x2o). We obtain,


It follows that A, A1oand A2oare unstable matrices, since their eigenvalues have positive real parts. Since A, A1oand A2ocan all be used for the design of an UIO for full‐state and message estimations, we shall choose Ain the sequel. Therefore, using (18) and (24), it follows that the UIO for full‐state and message estimations has the form

{[x^˙m^˙]=A¯[x^m^]+[g(x^,m^1)0]+[L1oL2o](yC¯[x^m^]),        =(A¯LoC¯)[x^m^]+[g(x^,m^1)0]+Loy,   [x^(0)m^(0)]=[x^om^o],y=C¯[xm],E25

where the gain Lois to be determined such that (A¯LoC¯)is Hurwitz. The next task is then to find Lo, which may be obtained by using the pole‐placement or Kalman‐Bucy filter design method. We shall use the Kalman‐Bucy filter technique. We note that in the design of a Kalman‐Bucy filter [55, 56], the known covariance matrices of the system noise and measurement noise are given by Qand R, respectively, where Q0and R>0. However, for the UIO design governed by (24) and (25), there are no system and measurement noises. Hence, the elements of the Qand Rmatrices can be treated as free design parameters to be chosen and adjusted such that the performance of the UIO is satisfactory. A general method for choosing Qand Ris to set them as diagonal matrices Q=qiiInand R=riiIr, where Inand Irare unit matrices, and adjust the values of the diagonal elements qiiand riiuntil satisfactory responses are obtained. In general, given a set of {rii}, larger values of {qii}will lead to larger observer gains that will place the observer poles deeper in the left half‐complex plane.

The overall UIO for full‐state and message estimations can be implemented as (see (22))

{x˙=Ax+g(x,m1)+Bmm,   x(0)=xo,x^˙=Ax^+g(x^,m^1)+Bmm^+L1o(yC¯y^),   x^(0)=x^o,m^˙=Amm^+L2o(yC¯y^),   m^(0)=m^o,y=[x1+m1x3+m1]=C¯[xm],E26

where the messages m1and m2are injected into the Rossler system directly (see Figure 1 also), thereby the message model m˙=Amis omitted in (26); however, the model matrix Amis needed in the message observer equation (third equation).

The key task now is the determination of a suitable UIO gain Lobased on (24) that yields acceptable performance. The design can be accomplished by using Matlab’s LQRcommand as

[L P Eo]=lqr(Ab',Cb',Q,R),   Lo=L',

where Aband Cbdenote A¯and C¯, respectively; Lo=PC¯TR1; Eo=λ(A¯LoC¯); and Pis the solution of the algebraic Riccati equation (ARE)


where Am=0in A¯(see (24)). The parameter matrices Qand Rthat produced a suitable Lowere found to be given by, respectively, (note that the adjustment of Qwas nontrivial),

Q=diag([0, 50, 1000, 5*1071012]), R=diag([0.01, 0.01]).

We obtain

Lo = 0.99979−0.99977Eo =−1e+005
 −0.03355−0.02882−1884 + 1876.6i
 −3688.83830.5−1884 − 1876.6i
 5181048122−0.4 + 0.8i
 −6.8055e+0067.33E+06−0.4 − 0.8i

Note that the eigenvalues λ(A¯LoC¯)are spread apart widely and have two complex conjugate poles.

The performance of the UIO is displayed in Figures 2 and 3. The initial conditions used in the simulations were: x(0)=x^(0)=[]Tand m^=[00]T. The signals to be estimated are: (a) a voice message m1(t)injected into the drive system at t =100, and (b) the electrocardiogram (ECG) m2(t)of a person. Figure 2(a) shows m1(t)and its estimate m^1(t), and Figure 2(b) shows m2(t)and m^2(t). The estimation errors were small, as can be seen from Figure 2(c) and (d), where the plots of m1vs. m^1, and m2vs. m^2are displayed. The clean 45‐degree trace in Figure 2(c) shows that the estimate m^1(t)of m1(t)is almost perfect, while Figure 2(d) shows that the estimation error m˜2(t)=m(t)m^2(t)was small. The synchronization of the drive‐response systems is shown in Figure 3(a)(d), where {x1(t),x^1(t)}and {x2(t),x^2(t)}are shown; the clean 45‐degree traces of x1(t) vs. x^1(t)and x2(t) vs. x^2(t)show that the synchronization was nearly perfect. Hence, we conclude that the overall synchronization of the drive‐response systems and the message estimation ranges from satisfactory to excellent.

Figure 2.

Responses of Rossler system: (a)m1 andmˆ1; (b)m2 andmˆ2; (c)m1 vs.mˆ1; and (d)m2 vs.mˆ2. Figures 2(c) and 2(d) indicate negligible estimation errorm˜1m1mˆ1 and smallm˜2m2mˆ2, respectively.

Figure 3.

Responses of Rossler system: (a)x1 andxˆ1; (b)x2 andxˆ2; (c)x1 vs.xˆ1; and (d)x2 vs.xˆ2. Figures 3(c) and 3(d) indicate negligible estimation errorsx˜1x1xˆ1 andx˜2x2xˆ2, respectively.

4.2. Reduced‐order UIO for message estimation for completely known x(t)

The objective here is to estimate the unknown message signal vector m(t)by assuming that the entire state vector xis accessible by direct measurement, i.e., full‐state measurement, and does not have to be estimated. Hence, without loss of generality, the output can be assumed to be given by y=x. This leads to the construction of a reduced‐order UIO for message (disturbance) estimation. In general, a reduced‐order observer based on full‐state or partial‐state measurement has an interesting structure and is an active area of research in the literature for system controls and disturbance estimation, see for example [24–26, 5760]. The reduced‐order UIO designed in this section for message estimation will be based on a derived measurementderived from y=xand y˙=x˙; the results will be extended to partial‐state and message estimations in Section 4.3.

Before launching into the design of UIOs for message estimation, let us consider a general disturbance estimation problem described by


where xRnis the state vector, uRa known control input vector, dRran unknown disturbance vector, and ythe measured or known output vector; f(x), B1(x)and B2(x)are known function and matrices of compatible dimensions. The unknown disturbance dis assumed to be generated by


where fd(0,0)=0.

The objective is to estimate the unknown disturbance dusing the output y=x. The following lemma shows that dcan be estimated based on a derived measurementinstead of y.

Lemma 1:Estimation of dbased on derived measurement

Consider the systems described by(27) and(28). A Luenberger‐like nonlinear observer can be constructed for disturbance estimation as


whered^is an estimate ofd, andLo(x)Rr×nis the observer gain to be determined such thatd^(t) d(t)asymptotically.

Proof:Define a derived measurementequation derived from the output y=xas


Since xis known, y˙=x˙can be obtained from its time derivative; hence yd(t)is known for all t0for known f(x)and B1(x)u. Combing (28) and (30) yields, with (27),


which constitutes a standard form or “pattern” for constructing a nonlinear observer. Hence, a candidate Luenberger‐like observer can be constructed for estimating dbased on yd(or simply by “pattern recognition”) as


Substituting (30) into (32) yields (29). ∎

Remark 2:When fd(d^,x)=0, (29) is identical to the observer proposed in Ref. [61] (Eq. (3.2), p. 44) under a versatile disturbance observer‐based control (DOBC)technique applicable to both linear and nonlinear systems. However, it is not clear how and why the derivative term x˙shows up in their Eq. (3.2). In contrast, the formulation in Lemma 1 based on the method of derived measurementprovides a clear insight, specifically, it clearly shows how x˙finds its way into (29). Furthermore, since it is, in general, difficult to access the entire system state vector xfor measurement, the derived measurement formulation will pave the way for the design of reduced‐order observers for joint partial‐state and disturbance estimations (see Section 4.3) using only those state variables that are available by direct measurement, thereby extending the DOBC technique and applications. ∎

Remark 3:The presence or origin of y˙in a linear Luenberger observer is well known in the literature [57, 58]. It occurs in the construction of reduced‐order linear observers, where the elimination of y˙leads to the design of improved or enhanced reduced‐order observers. As shown in (29), the derivative y˙also occurs in constructing enhanced reduced‐order nonlinear observers. ∎

To continue further, the derivative y˙in (29) can be eliminated by moving the term L(x)y˙to the left side of the equation to yield


where z˙=d^˙Lo(x)y˙. Defining [61],


where p(x)Rr×nis to be determined. If fd(d^,x)=0, then (33) can be expressed as


which is identical to the enhanced observer presented in Ref. [61] (see for example, Eq. (3.5), p. 44).

We now return to message estimation in chaotic systems. We can start with (3), which can be expressed as, with full‐state measurement given by y=x,

{x˙=f(x)+Bm(x)Mm,m˙=Amm+Ψx,   y=x.E36

Since the entire state vector xis known for all t0, (36) can be rearranged as



ydy˙f(x),   (y˙=x˙),E38

is the derived‐measurementin the form of (30). Most importantly, ydcan serve as the output equation for m˙=Amm+Ψx,so that (37) provides a standard form or pattern for observer design. Accordingly, a candidate Luenberger‐like observer can be constructed based on ydas

m^˙=Amm^+Ψx+Lo(x)[ydBm(x)Mm^]   =[AmLo(x)Bm(x)M]m^+Ψx+Lo(x)[y˙f(x)],   m^(0)=m^o,E39

where Lo(x)Rr×nis the observer gain matrix to be determined.

To proceed, it follows from (37) and (39) that the estimation error defined by m˜=mm^satisfies

m˜˙=[AmLo(x)Bm(x)M]m˜,   m˜(0)=m˜o,E40

which shows that if Lo(x)is a suitable stabilizing gain, then m˜(t)can be made to converge to zero asymptotically for arbitrary  m˜(0), thereby m^(t)m(t).

The results above are summarized in the following theorem.

Theorem 3:Consider (36)–(39). If there exists a gainLo(x)such that (40) is asymptotically stable for allx, thenm^(t)m(t)asymptotically. 

Note that since Bm(x)is a function of x, it complicates the determination of Lo(x)to achieve asymptotic stability. However, if Bm(x)=Bm, where Bm is a constant matrix, then Lo(x)can be determined as a constant Lo, and can be computed by simple methods, such as pole placement, provided that [Am,Bm]is an observable pair (see Example 2 below).

Using (36), it follows that (35) takes on the form,


A main task in applying (41) is the determination of p(x). If we set p(x)as a linear function of x, i.e.,

p(x)=Lox,   (x=y),E42

where Lois a constant matrix, then we obtain from (34), L(x)=Lo. Further, if Bm(x)=Bmand [Am,BmM]is an observable pair, then Locan be determined readily by, for example, the pole‐placement method, such that (AmLoBmM)is Hurwitz. Moreover, (40) becomes,

m˜˙=(AmLoBmM)m˜,   m˜(0)=m˜o,E43

which shows that m˜(t)0, thereby m^(t)m(t)exponentially for arbitrary m˜(0). In addition, in this case, the enhanced UIO (41) reduces to


It can be shown that the preceding equation can be obtained by using the linearized system (17) and setting p(x)=LoxLo=p(x)/x.

Once a suitable gain has been determined, such as Lo(x)=Lo, it can then be substituted into (41), and the overall chaotic system‐based UIO for message estimation can be implemented as, with (36),

{x˙=f(x)+Bm(x)Mm,   x(0)=xo,m˙=Amm+Ψx,   m(0)=mo,   z˙=(AmLoBm(x)M)z+[(AmLoBm(x)M)Lox+ΨxLof(x)],   z(0)=zo,m^=z+Loy,y=x.E45

We remark that the UIO governed by the third equation in (45) is a nonlinear observer with its gain Lo(x)replaced by a constant Lo. Other methods may be used to determine a suitable Lo, such as linear matrix inequality (LMI), see for example Ref. [34].

Example 2:Genesio‐Tesi system [49]

Consider the Genesio‐Tesi system described by (7) with additive messages and output y=x


Using (37) with Ψ=0, the preceding equation can be arranged in the form of an LTI system as


where yd=y˙f(x)is the derived measurement, and it can be shown that [Am,Bm]is an observable pair for all Am, i.e., rank[BmTAmTBmT]=2.

An observer for (47) can be constructed as

m^˙=Amm^+Lo(ydLoBmm^)   =(AmLoBm)m^+Lo[y˙f(x)],E48

which is obtainable from (39). Since [Am,Bm]is an observable pair, a constant gain Locan be determined such that (AmLoBm)is Hurwitz. Further, eliminating the derivative term Loy˙in (48) yields


To determine the gain Lo, let the poles of (AmLoBm)be selected as po=[6132]. Using Matlab’s pole‐placement command,


we obtain, for Am=0,

Lo = 0610

The final result for implementation can be obtained by combing Eqs. (46) and (49) as

{x˙=f(x)+Bmm,   x(0)=xo,z˙=(AmLoBm)z+[(AmLoBm)LoyLof(x)],   z(0)=zo,m^=z+Loy,y=x.E50

Since m1(t)and m2(t)are injected directly into the Genesio‐Tesi system (46), the message model m˙=Ammis not needed and is omitted in (50); however, the model matrix Amis required in the estimation equation (second equation in (50)). The signal m1(t)is the nine‐term Fourier series of a square wave, and m2(t)is a mix signal consisting of a trapezoid, sine wave, and ramp and exponential functions. It would be difficult to generate these rather complicated signals, in particular m2(t), by using the simple model m˙=Amm, and/or a more general model m˙=Amm+δproposed in Refs. [2426], where the elements δi(t)of δ(t)are unknown sequences of random delta functions. For simulation studies, the mix signal m2(t)can easily be generated by the following Matlab codes and injected into (50):

Mix signal m2(t):





The performance of the UIO is displayed in Figures 4 and 5. The initial condition of the Genesio‐Tesi system used in the simulation was x(0)=[]T, while z(0)was calculated by using z(0)=m^(0)Loy(0)=Lox(0), which yields z(0)=[24.46.4]Twhere m^(0)=0. Figure 4(a) shows m1(t)and its estimate m^1(t), and Figure 4(b) exhibits m2(t)and m^2(t). The estimation errors were negligible, as can be seen from Figure 4(c) and (d), where the plots of m1(t)vs. m^1(t), and m2(t)vs. m^2(t)are displayed. Note also the Gibb’s phenomenon (the “twin‐towers”) in Figure 4(a). The Genesio‐Tesi attractor is shown in Figure 5. We conclude that the performance of the reduced‐order UIO for message estimation was satisfactory.

Figure 4.

Responses of Genesio-Tesi system: (a)m1 andmˆ1; (b)m2 andmˆ2; (c)m1 vs.mˆ1; and (d)m2 vs.mˆ2. Figures 4(c) and 4(d) indicate negligible estimation errorm˜1m1mˆ1 andm˜2m2mˆ2, respectively.

Figure 5.

Genesio‐Tesi attractor obtained by using (46).

4.3. Reduced‐order UIO for partial‐state and message estimations

The objective in this section is to extend the design of reduced‐order UIO for message estimation to the design of UIO for joint partial‐state and message estimations. The results obtained are believed to be new and novel.

Consider a general nonlinear system described by (3) which is expressed here without the output as (see also (1) and (2))

{x˙=f(x)+Bm(x)Mm,m˙=Amm, E51

where Ψ=0. The design will be based on a derived measurement formulation.



where x1Rmand x2Rnmare, respectively, accessible and inaccessible for direct measurement, and yis the output. Using (52), we assume that (51) can be partitioned as

{x˙1=f1(y)+B1m(y)Mm,   (y=x1),x˙2=f2(y,x2,m)+B2m(y,x2)Mm,m˙=Amm,E53

which can be rearranged to give


where yd=y˙f1(y)denotes the derived measurement.

Eq. (54) constitutes a standard form that can be used to construct an observer for estimating the inaccessible partial‐state x2and the unknown message mbased on the derived measurement yd. Hence, a candidate Luenberger‐like observer can be constructed as


where L1o(·)and L2o(·)are the gain matrices to be determined such that the observer has desirable performance characteristics, in particular, x^2(t)x2(t)and m^(t)m(t)as t.

Remark 4:In (39), the reduced‐order UIO was derived using the output y=x, while the reduced‐order UIO (55) above was constructed by using y=x1with x1serving the role of x. Hence (55) is an extension of the DOBC technique, and is now applicable to partial‐state and message estimations by using only x1instead of the entire state x.

The estimation error w˜=[x˜2Tm˜T]T, where x˜2=x2x^2 and m˜2=mm^, satisfies, with (54) and (55),


The preceding error equation is a version of (15). Hence from Theorem 1, the origin (0,0)is an equilibrium point of the unforced equation in (56) for all y=x1. Further, x^2(t)x2(t)and m^(t)m(t)if L1o(·)and L2o(·)are stabilizing gains.

The next task is to eliminate y˙in yd=y˙f1(y)in (55) by moving Lo(·)y˙to the left side of the equation and defining



z=w^p(x1)z˙=w^˙p(x1)x1x˙1,   (x˙1=y˙),E58

where p(x1)=[p1(x1)p2(x1)], p1(x1)R(nm)×(nm)and p2(x1)Rr×(nm)are to be determined. It follows that


Using (55), (57), (58) and (59), can be expressed as


which can further be reduced to a form given by, for example (44), once the specific structure of the chaotic system under consideration is known and p(x1)has been determined (see Example 3 for more details).

Using (51) and (60), the main results for the construction of UIO for partial‐state and message estimations are stated in the following theorem.

Theorem 4:Consider the augmented system (54), whereyd=B1m(y)Mmis the derived measurement. A candidate UIO for partial‐state and message estimations is given by

{ z˙=[f2(y,x^2,m^)Amm^]+[B2m(y,x^2)Mm^0][L1o(x1)L2o(x1)][f1(y)+B1m(y)Mm^],w^=z+p(x1),y=x1.E61

If the gainsL1o(x1)andL2o(x1)exist such that(61) is asymptotically stable, thenx^2(t)x2(t)andm^(t)m(t)ast. ∎

Example 3:Chua’s circuit [50]

Consider the Chua circuit described by (8), modified here with an additive message mas,


where α=10, β=16and c=0.14are the chaotic parameters [50]. A different modification scheme is given in Ref. [51].

Using (52), let the output be chosen as


where x3constitutes the unknown partial state, and the derived measurement can be obtained as


Using (62) and (63), the combined partial‐state and message system has the form


where Aw=0if Am=0, g(y2)=[βx20]T, and [Aw,Bw]is an observable pair for all Aw, i.e., rank[BwTAwTBwT]=2.

Using (61) or (65) and (62), a UIO for partial‐state and message estimations can be constructed based on ydgiven by (64) and implemented as

{x˙=f(x)+Bm(x)m,   x(0)=xo,z˙=(AwLoBw)z+g(y2)+[(AwLoBw)LoyLofw(y1,y2)],   z(0)=zo,w^=z+Loy,   (Aw=0,   p(x1)=Loy,   y=x1).E66

The gain Loused for the simulations was obtained by choosing the UIO poles as po=[1000500]. Using Matlab’s pole‐placement command Lo=place(Aw',Bw',po)', we obtain, with Aw=0,

Lo = 01000

The message m(t)in (66) is a stock price data consisting of 50 data points where the value of m(0)is m(0)=37. To minimize the effect of m(t)on the chaotic nature of the Chua circuit, it is scaled down to a small signal as m¯(t)=0.01m(t); this yields m¯(0)=0.37. The scaled down signal was then injected into (62) directly. To enhance the estimate z(t)=w^(t)Loy(t)=[x^3(t)m¯^(t)]TLo[x1(t)x2(t)]T, its initial value z(0)was calculated by using z(0)=[x^3(0)m¯^(0)]TLo[x1(0)x2(0)]T, which gave z(0)=[498999.63]T, where x^3(0)=x3(0)=2and m¯^(0)=m¯(0)=0.37. We remark that, since the initial condition x(0)=[20.52]Tof the Chua circuit is known, we can always set x^3(0)=x3(0), while in the event that the value of m¯(0)is not known, then it can be set as m¯(0)=0resulting in small mismatches between m¯^(t)and m¯(t)during the transient period. The performance of the reduced‐order UIO is shown in Figures 6 and 7. Figure 6(a) shows x3(t)and its estimate x^3(t), while Figure 6(c) shows the plot of x3(t)vs. x^3(t), which indicates an excellent match. Figure 6(b) displays the message m(t)and its estimate m^(t), while the plot of m(t)vs. m^(t)in Figure 6(d) shows a clean 45‐degree line indicating an almost perfect match. The plots of {xi(t),  m¯(t)0}vs. {xi(t),  m¯(t)=0}are depicted in Figure 7(a)(c), showing that the small signal m¯(t)has little effect on the chaotic nature of the Chua circuit. We conclude that the performance of the reduced‐order UIO for partial‐state and message estimations was satisfactory. Further, it is emphasized that no Jacobian linearization was employed in this example.

Figure 6.

Responses of Chua system: (a)x3 and x^3; (b) m¯ and m¯^; (c)x3 vs. x^3; and (d)m¯ vsm¯^ . Figures 6(c) and 6(d) indicate negligible estimation errorsx˜2x2xˆ2 andm≃ =m¯m¯^ , respectively.

Figure 7.

Plots ofxi (m¯0) vs.  xi (m¯=0)showing thatm¯has little effects on the chaotic nature of the Chua system.


5. Conclusions and plan for future research

In this paper, we showed that secure communication employing chaotic systems can be achieved by synchronizing the dynamics of the drive and response systems. The results are obtained by using unknown‐input observers (UIOs), which serve as the response systems. Three classes of UIOs have been designed, namely, (i) full‐order UIO for estimating all the state variables (full state) and messages in the drive system; (ii) reduced‐order UIO for message estimation based on a derived measurement technique, where the formulation is based on the disturbance observer‐based control (DOBC) theory (recall that the DOBC technique is only applicable to disturbance estimation based on the assumption that all the state variables (full state) in a system are known; and (iii) reduced‐order UIO for partial‐state and message estimations based on partial‐state measurement using the derived‐measurement technique. The reduced‐order UIO for partial‐state and message estimations is novel, and is an extension of the DOBC theory, thereby expanding the technique and applications of DOBC. Our future research and development will be focused on wireless secure communication, robust synchronization in the presence of channel noise and various channel induced distortions, and the designs and applications of disturbance cancellation nonlinear control systems using the well‐known disturbance accommodation control (DAC) theory, thereby unifying the DAC and DOBC approaches and techniques.

© 2017 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution 3.0 License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite and reference

Link to this chapter Copy to clipboard

Cite this chapter Copy to clipboard

Robert N.K. Loh and Manohar K. Das (November 22nd 2017). Nonlinear Unknown‐Input Observer‐Based Systems for Secure Communication, Advances in Underwater Acoustics, Andrzej Zak, IntechOpen, DOI: 10.5772/intechopen.69239. Available from:

chapter statistics

838total chapter downloads

1Crossref citations

More statistics for editors and authors

Login to your personal dashboard for more detailed statistics on your publications.

Access personal reporting

Related Content

This Book

Next chapter

Denoising Methods for Underwater Acoustic Signal

By Vijaya Baskar Veeraiyan

Related Book

First chapter

Introductory Chapter: Pattern Recognition as Cognitive Process

By Andrzej Zak

We are IntechOpen, the world's leading publisher of Open Access books. Built by scientists, for scientists. Our readership spans scientists, professors, researchers, librarians, and students, as well as business professionals. We share our knowledge and peer-reveiwed research papers with libraries, scientific and engineering societies, and also work with corporate R&D departments and government entities.

More About Us