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Input-to-state stability

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Input-to-state stability (ISS)[1][2] is a stability notion widely used to study stability of nonlinear control systems with external inputs. Roughly speaking, the system is ISS if it is globally asymptotically stable in the absence of external inputs and if its trajectory stays for large enough times below certain bound, which depends on the norm of the input. Importance of ISS concept is due to the fact, that it has bridged the gap between the input–output and the state-space methods, widely used within the control systems community. The notion of ISS has been introduced by Eduardo Sontag in 1989[3].

Definition

Consider a time-invariant system of ordinary differential equations of the form

where is a Lebesgue measurable essentially bounded external input and is a Lipschitz continuous function w.r.t. the first argument uniformly w.r.t. the second one. This ensures that there exists a unique absolutely continuous solution of the system (1).

To define ISS and related properties, we exploit the following classes of comparison functions. We denote by the set of continuous increasing functions with . The set of unbounded functions we denote by . Also we denote if for all and is continuous and strictly decreasing to zero for all .

System (1) is called globally asymptotically stable at zero (0-GAS) if the corresponding system with zero input

is globally asymptotically stable, that is there exist so that for all initial values and all times the following estimate is valid for solutions of (WithoutInputs)

System (1) is called input-to-state stable (ISS) if there exist functions and so that for all initial values , all admissible inputs and all times the following inequality holds

The function in the above inequality is called the gain.

Clearly, ISS system is 0-GAS as well as BIBO stable (if we put output equal to the state of the system). The converse implication is in general not true.

It can be also proved that if , when , then , .

Characterizations of input-to-state stability property

For understanding of ISS its restatements in terms of other stability properties are of great importance.

System (1) is called globally stable (GS) if there exist such that , and it holds that

System (1) satisfies asymptotic gain (AG) property if there exists : , it holds that

The following statements are equivalent [4]: 1. (1) is ISS

2. (1) is GS and possesses AG property

3. (1) is 0-GAS and possesses AG property

The proof of this result as well as many other characterizations of ISS property can be found in papers [4] and [5]

ISS-Lyapunov functions

An important tool for verification of ISS property are ISS-Lyapunov functions.

A smooth function is called an ISS-Lyapunov function for (1), if , and positive definite function , such that:

and it holds:

The function is called Lyapunov gain.

If a system (1) is without inputs (i.e. ), then the last implication reduces to the condition

which tells us that is a "classical" Lyapunov function.

An important result due to E. Sontag and Y. Wang is that a system (1) is ISS if and only if there exists a smooth ISS-Lyapunov function for it.[5]

Examples

Consider a system

Define a candidate ISS-Lyapunov function by

Choose Lyapunov gain by

.

Then we obtain that for it holds

This shows that is an ISS-Lyapunov function for a considered system with a Lyapunov gain .

Interconnections of ISS systems

One of the main features of ISS framework is a possibility to study stability properties of interconnections of input-to-stable systems.

Consider the system given by

Here , and are Lipschitz continuous w.r.t. uniform with respect to inputs of -th subsystem.

For the -th subsystem of (WholeSys) the definition of an ISS-Lyapunov function can be written as follows.

A smooth function is an ISS-Lyapunov function (ISS-LF) for the -th subsystem of (WholeSys), if there exist functions , , , , and a positive definite function , such that:

and it holds

Cascade interconnections

Cascade interconnection is a special type of interconnection, where the dynamics of the -th subsystem doesn't depend on the states of the subsystems . Formally the cascade interconnection can be written as

If all subsystems of the above system are ISS, then the whole cascade interconnection is also ISS[3], [2].

In contrast to cascades of ISS systems, the cascade interconnection of 0-GAS systems is in general not 0-GAS. The following example illustrates this fact. Consider a system given by

Both subsystems of this system are 0-GAS, but for large enough initial states and for certain finite time it holds for , i.e. the system (Ex_GAS) exhibits finite escape time, and thus is not 0-GAS.

Feedback interconnections

The interconnection structure of subsystems is characterized by the internal Lyapunov gains . The question, whether the interconnection (WholeSys) is ISS, depends on the properties of the gain operator defined by

The following small-gain theorem establishes the sufficient condition for ISS of the interconnection of ISS systems. Let be an ISS-Lyapunov function for -th subsystem of (WholeSys) with corresponding gains , . If the nonlinear small-gain condition

holds, then the whole interconnection is ISS[6],[7].

Small-gain condition (SGC) holds iff for each cycle in (that is for all , where ) and for all it holds

The small-gain condition in this form is called also cyclic small-gain condition.

Integral ISS (iISS)

System (1) is called integral input-to-state stable (ISS) if there exist functions and so that for all initial values , all admissible inputs and all times the following inequality holds

In contrast to ISS systems, if the system is integral ISS, the trajectory may be unbounded. To see this put for all and take . Then the estimate (3) takes form

and the right hand side grows to infinity when .

As in ISS framework, the Lyapunov methods play central role in iISS theory.

A smooth function is called an iISS-Lyapunov function for (1), if , and positive definite function , such that:

and it holds:

An important result due to D. Angeli, E. Sontag and Y. Wang is that system (1) is integral ISS if and only if there exists an iISS-Lyapunov function for it.

Note that in the formula above is assumed to be only positive definite. It can be easily proved[8], that if is an iISS-Lyapunov function with , then is actually an ISS-Lyapunov function for a system (1).

This shows in particular, that every ISS system is integral ISS. The converse implication is not true, as the following example shows. Consider a system

This system is not ISS, since for large enough inputs the trajectories are unbounded. However, it is integral ISS with an iISS-Lyapunov function defined by

Local ISS (LISS)

An important role plays also local version of ISS property. A system (1) is called locally ISS (LISS) if there exist a constant and functions

and so that for all , all admissible inputs and all times it holds that

An interesting observation is that 0-GAS implies LISS[9].

Other stability notions

Many other related to ISS stability notions have been introduced: incremental ISS, input-to-state dynamical stability (ISDS)[10], input-to-state practical stability (ISpS), input-to-output stability (IOS)[11] etc.

ISS of time-delay systems

Consider time-invariant time-delay system

Here is the state of the system (TDS) at time , and satisfies certain assumptions to guarantee existence and uniqueness of solutions of the system (TDS).

System (TDS) is ISS if and only if there exist functions and such that for every , every admissible input and for all , it holds that

In ISS theory for time-delay systems two different Lyapunov-type sufficient conditions have been proposed: via ISS Lyapunov-Razumikhin functions[12] and by ISS Lyapunov-Krasovskii functionals[13]. For converse Lyapunov theorems for time-delay systems see[14].

ISS of other classes of systems

Input-to-state stability of the systems based on time-invariant ordinary differential equations is a quite developed theory. However, ISS theory of other classes of systems is also being investigated: time-variant ODE systems[15], hybrid systems[16][17]. In the last time also certain generalizations of ISS concepts to infinite-dimensional systems have been proposed[18][19][1][20].

References

  1. ^ a b Iasson Karafyllis and Zhong-Ping Jiang. Stability and stabilization of nonlinear systems. Communications and Control Engineering Series. Springer-Verlag London Ltd., London, 2011.
  2. ^ a b E. D. Sontag. Input to state stability: basic concepts and results. In Nonlinear and optimal control theory, volume 1932 of Lecture Notes in Math., pages 163–220, Berlin, 2008. Springer
  3. ^ a b Eduardo D. Sontag. Smooth stabilization implies coprime factorization. IEEE Trans. Automat. Control, 34(4):435–443, 1989.
  4. ^ a b Eduardo D. Sontag and Yuan Wang. New characterizations of input-to-state stability. IEEE Trans. Automat. Control, 41(9):1283–1294, 1996.
  5. ^ a b Eduardo D. Sontag and Yuan Wang. On characterizations of the input-to-state stability property. Systems Control Lett., 24(5):351–359, 1995.
  6. ^ Zhong-Ping Jiang, Iven M. Y. Mareels, and Yuan Wang. A Lyapunov formulation of the nonlinear small-gain theorem for interconnected ISS systems. Automatica J. IFAC, 32(8):1211–1215, 1996.
  7. ^ Sergey Dashkovskiy, Björn S. Rüffer, and Fabian R. Wirth. An ISS Lyapunov function for networks of ISS systems. In Proceedings of the 17th International Symposium on Mathematical Theory of Networks and Systems (MTNS), Kyoto, Japan, July 24-28, 2006, pages 77–82, 2006
  8. ^ See Remark 2.4. in Eduardo D. Sontag and Yuan Wang. On characterizations of the input-to-state stability property. Systems Control Lett., 24(5):351–359, 1995
  9. ^ Lemma I.1, p.1285 in Eduardo D. Sontag and Yuan Wang. New characterizations of input-to-state stability. IEEE Trans. Automat. Control, 41(9):1283–1294, 1996
  10. ^ Lars Grüne. Input-to-state dynamical stability and its Lyapunov function characterization. IEEE Trans. Automat. Control, 47(9):1499–1504, 2002.
  11. ^ Z.-P. Jiang, A. R. Teel, and L. Praly. Small-gain theorem for ISS systems and applications. Math. Control Signals Systems, 7(2):95–120, 1994.
  12. ^ Andrew R. Teel. Connections between Razumikhin-type theorems and the ISS nonlinear small gain theorem. IEEE Trans. Automat. Control, 43(7):960–964, 1998.
  13. ^ P. Pepe and Z.-P. Jiang. A Lyapunov-Krasovskii methodology for ISS and iISS of time-delay systems. Systems Control Lett., 55(12):1006–1014, 2006.
  14. ^ Iasson Karafyllis. Lyapunov theorems for systems described by retarded functional differential equations. Nonlinear Analysis: Theory, Methods & Applications, 64(3):590 – 617,2006.
  15. ^ Y. Lin, Y. Wang, and D. Cheng. On nonuniform and semi-uniform input-to-state stability for time-varying systems. In IFAC World Congress, Prague, 2005.
  16. ^ C. Cai and A.R. Teel. Characterizations of input-to-state stability for hybrid systems. Systems & Control Letters, 58(1):47–53, 2009.
  17. ^ D. Nesic and A.R. Teel. A Lyapunov-based small-gain theorem for hybrid ISS systems. In Proceedings of the 47th IEEE Conference on Decision and Control, Cancun, Mexico, Dec. 9-11, 2008, pages 3380–3385, 2008.
  18. ^ Bayu Jayawardhana, Hartmut Logemann, and Eugene P. Ryan. Infinite-dimensional feedback systems: the circle criterion and input-to-state stability. Commun. Inf. Syst., 8(4):413–414, 2008.
  19. ^ Dashkovskiy, S. and Mironchenko, A. Input-to-state stability of infinite-dimensional control systems. In Mathematics of Control, Signals, and Systems (MCSS),2013
  20. ^ F. Mazenc and C. Prieur. Strict Lyapunov functions for semilinear parabolic partial differential equations. Mathematical Control and Related Fields, 1:231–250, June 2011.