Controllability and observability of complex -matrix time-varying impulsive systems
© Fang and Sun; licensee Springer 2013
Received: 20 March 2013
Accepted: 22 April 2013
Published: 6 May 2013
Since many equations of practical systems such as Schrödinger equation, Ginzburg-Landau equation and Orr-Sommerfeld equation are defined in complex number fields, in this paper, the issue of controllability and observability for an -matrix time-varying impulsive system defined in complex fields is addressed. Several sufficient and necessary conditions for state controllability and observability of such a system are established. Meanwhile, corresponding controllability and observability criteria for the -matrix time-invariant impulsive system are also obtained.
Since many evolution processes, optimal control models in economics, stimulated neural networks, frequency-modulated systems and some motions of missiles or aircrafts are characterized by impulsive dynamical behavior, the study of impulsive systems is of great importance. Nowadays, there has been an increasing interest in the analysis and synthesis of impulsive systems, or impulsive control systems, due to their theoretical and practical significance; for example, [1–12] and the references therein.
As the fundamental issues of modern control theory, the controllability and observability have been studied extensively in the context of finite-dimensional linear systems, nonlinear systems, infinite-dimensional systems, n-dimensional systems and hybrid systems using different kinds of approaches [13–17]. In particular, many efforts have been focused on the problem of controllability and observability for various kinds of impulsive systems using different approaches. The geometric analysis of reachability, controllability and observability for impulsive systems in terms of invariant subspaces were presented in [16, 18]. By proposing the rank condition, Guan et al. , Zhao et al.  and Shi et al.  proposed the sufficient and necessary conditions for state controllability and observability of different kinds of linear time-varying impulsive systems, respectively.
However, the common setting adopted in the above-mentioned works except [3, 10, 19] is always in real number fields. In fact, many classical systems such as Schrödinger equation, Ginzburg-Landau equation, Riccati equation and Orr-Sommerfeld equation are considered in complex number fields. But there have been few reports about the analysis and synthesis of complex dynamical systems; for example [20–25] and references therein. More abstract than real system, the control theory of complex-valued dynamical systems has many potential applications in science and engineering. For example, recently research on the control theory of quantum systems has attracted considerable attention [26–29]. Quantum systems are a class of complex dynamical systems which take values in a Banach space in a complex field.
Matrix differential equations are relevant to the description of many phenomena in physics and engineering, ranging from such diverse applications as control theory to game theory . The motivation for considering -matrix differential systems arises from the demand for a level of generality sufficient to deal with the increasingly important matrix linear systems of control theory such as those associated with matrix Riccati differential equations and matrix bilinear control systems [24, 31–35]. In particular, recently research on the control theory of multidimentional systems has attracted attention of quite a few scientists [36, 37]. Multidimentional systems are a class of matrix differential systems which have extensive application in image processing. Due to these reasons, it is important and necessary to study the control theory of complex matrix impulsive systems.
To the best of our knowledge, there is no result so far about the control theory of complex matrix impulsive systems. Inspired by [10, 22], in this paper, we consider the fundamental concepts of controllability and observability of complex -matrix time-varying impulsive systems by an algebraic approach. The main difficulty is to investigate the conditions for controllability and observability of complex -matrix impulsive systems in the context of complex matrices. Explicit characterization for controllability and observability of this kind of a system in terms of the rank conditions is presented by use of the matrix differential theory in a complex field. These questions are meaningful and challenging.
The paper is organized as follows. In Section 2, the complex matrix time-varying impulsive systems to be dealt with are formulated and several new results about the variation of parameters for such systems are presented. Several sufficient and necessary conditions for state controllability and state observability of complex matrix time-varying impulsive systems and corresponding complex matrix time-invariant impulsive systems are established in Sections 3 and 4, respectively. An example is given to explain those results in Section 5. Finally, some conclusions are drawn in Section 6.
2 Notations and preliminaries
In order to make precise the concept of a complex -matrix time-varying impulsive system, we use the terminologies in  and . Let be the set of all block matrices with rows and columns over the ring of all complex matrices. Fix an open interval and let the symbols , and denote, respectively, the spaces of complex-valued Lebesgue measurable functions on I which are locally integrable, locally bounded and locally absolutely continuous on I. Here ‘local’ implies a property holding on all compact subintervals of I. In the same way, denote by all -matrices whose entries are locally integrable on I, and similar notations hold for and other relevant classes of matrix functions on I.
where , , , , , is the state vector, is the initial state, is the control input, , , , is the output, , where , , and the discontinuity points , , which implies that the solution of (1) is left-continuous at .
Definition 1 The -matrix time-varying impulsive system (1) is called state controllable on () if for any given initial state , there exists a piecewise locally bounded controller such that the corresponding solution of (1) satisfies .
Definition 2 System (1) is said to be observable on () if any initial state can be uniquely determined by the corresponding system input and output for .
Let be the conjugated transpose of a complex matrix or a complex block matrix A, and let stand for a matrix product . Next we will present the solution of system (1).
where is given by (5). This implies that Lemma 1 holds for .
which implies that Lemma 1 is true when . According to the mathematical induction, we can immediately conclude that Lemma 1 is true. This completes the proof. □
In this subsequent section, we discuss the controllability criteria of complex-valued -matrix impulsive system (1) using the algebraic method.
Now we present a sufficient condition and a necessary condition for the controllability of -matrix time-varying impulsive system (1).
Theorem 1 System (1) is controllable on () if there exist an and a matrix such that is invertible or (identity matrix).
- (ii)Without loss of generality, suppose that there exist an and a complex matrix satisfying . Then, given an initial state , we design the following control law:(9)
Hence, by the definition of controllability, system (1) is controllable on . This completes the proof. □
where , and are defined in (7).
This contradicts the assumption that , and we conclude that (10) holds. This completes the proof. □
Remark For system (1) with and continuous , , and , the sufficient controllability criteria and necessary controllability criteria obtained in Theorem 1 and Theorem 2 are the existing results in . Furthermore, when system (1) is defined in the real number fields, the sufficient controllability criteria and necessary controllability criteria obtained in Theorem 1 and Theorem 2 are the existing results in . However, since the controllability criteria in Theorem 1 are sufficient conditions, there exists some conservatism in Theorem 1, further research is needed for the controllability of system (1).
Now we present the sufficient and necessary condition for the observability of system (1).
Theorem 3 System (1) is observable on () if and only if complex block matrix defined in (17) is invertible.
It is easy to see that the left-hand side of (18) depends on , . So, if is invertible, then the initial state is uniquely determined by the corresponding complex system output and input for .
From Definition 2, system (1) is not observable on (). This contradicts the assumption of observability. This completes the proof. □
where , .
If , then complex -matrix linear impulsive system (1) is observable on ().
Assume that , . If complex -matrix system (1) is observable, then .
- (ii)If otherwise, assume that complex impulsive system (1) is observable while , then there exists a vector satisfying which reduces from (20) to(23)
So . Because , the matrix is not invertible. Hence complex -matrix impulsive system (1) is not observable from Theorem 3, and it contradicts the assumption of observability. This completes the proof. □
where , and for the convenience of calculation, we take , such that , .
and , so (24) is right.
is invertible, according to Theorem 3, system (1) is observable on . This completes the example.
In this paper, the issue of the controllability and observability criteria for a class of complex -matrix time-varying impulsive systems has been addressed for the first time. Taking advantage of the matrix differential equation theory in complex fields, several sufficient and necessary conditions for state controllability and observability of such systems have been established respectively without imposing extra conditions. Moreover, the corresponding criteria for controllability and observability of complex -matrix time-invariant systems have also been derived.
The authors would like to thank the editor and the reviewers for their constructive comments and suggestions which improved the quality of the paper. This work is supported by the NNSF of China under Grant 61174039 and the ‘Chen Guang’ project supported by Shanghai Municipal Education Commission and Shanghai Education Development Foundation under Grant: 12CG65.
- Zhang S, Sun J: Stability analysis of second-order differential systems with Erlang distribution random impulses. Adv. Differ. Equ. 2013, 2013: 1–10. 10.1186/1687-1847-2013-1View ArticleGoogle Scholar
- Yang SJ, Shi B, Zhang Q: Complete controllability of nonlinear stochastic impulsive functional systems. Appl. Math. Comput. 2012, 218: 5543–5551. 10.1016/j.amc.2011.11.043MathSciNetView ArticleGoogle Scholar
- Fang T, Sun J: Existence and uniqueness of solutions to complex-valued nonlinear impulsive differential systems. Adv. Differ. Equ. 2012., 2012: Article ID 115Google Scholar
- Wan XJ, Sun JT: Adaptive-impulsive synchronization of chaotic systems. Math. Comput. Simul. 2011, 81(8):1609–1617. 10.1016/j.matcom.2010.11.012MathSciNetView ArticleGoogle Scholar
- Li CX, Shi JP, Sun JT: Stability of impulsive stochastic differential delay systems and its application to impulsive stochastic neural networks. Nonlinear Anal. 2011, 74(10):3099–3111. 10.1016/j.na.2011.01.026MathSciNetView ArticleGoogle Scholar
- Song XY, Li A: Stability and boundedness criteria of nonlinear impulsive systems employing perturbing Lyapunov functions. Appl. Math. Comput. 2011, 217: 10166–10174. 10.1016/j.amc.2011.05.011MathSciNetView ArticleGoogle Scholar
- Li DS, Long SJ: Attracting and quasi-invariant sets for a class of impulsive stochastic difference equations. Adv. Differ. Equ. 2011, 2011: 1–9.MathSciNetGoogle Scholar
- Chen WH, Zheng WX: Input-to-state stability for networked control systems via an improved impulsive system approach. Automatica 2011, 47: 789–796. 10.1016/j.automatica.2011.01.050MathSciNetView ArticleGoogle Scholar
- Li CX, Sun JT, Sun RY: Stability analysis of a class of stochastic differential delay equations with nonlinear impulsive effects. J. Franklin Inst. 2010, 347(7):1186–1198. 10.1016/j.jfranklin.2010.04.017MathSciNetView ArticleGoogle Scholar
- Zhao SW, Sun JT: Controllability and observability for impulsive systems in complex fields. Nonlinear Anal., Real World Appl. 2010, 11: 1513–1521. 10.1016/j.nonrwa.2009.03.009MathSciNetView ArticleGoogle Scholar
- Ding XH, Wu KN, Liu MZ: The Euler scheme and its convergence for impulsive delay differential equations. Appl. Math. Comput. 2010, 216: 1566–1570. 10.1016/j.amc.2010.03.007MathSciNetView ArticleGoogle Scholar
- Lakshmikantham V, Bainov DD, Simeonov PS: Theory of Impulsive Differential Equations. World Scientific, Singapore; 1989.View ArticleGoogle Scholar
- Wang Z, Shen B, Shu H, Wei G: Quantized H-infinity control for nonlinear stochastic time-delay systems with missing measurements. IEEE Trans. Autom. Control 2012, 57(6):1431–1444.MathSciNetView ArticleGoogle Scholar
- Dong H, Wang Z, Gao H: Distributed filtering for a class of time-varying systems over sensor networks with quantization errors and successive packet dropouts. IEEE Trans. Signal Process. 2012, 60(6):3164–3173.MathSciNetView ArticleGoogle Scholar
- Shi H, Xie G: Controllability and observability criteria for linear piecewise constant impulsive systems. J. Appl. Math. 2012., 2012: Article ID 182040Google Scholar
- Xie GM, Wang L: Necessary and sufficient conditions for controllability and observability of switched impulsive control systems. IEEE Trans. Autom. Control 2004, 49: 960–966. 10.1109/TAC.2004.829656MathSciNetView ArticleGoogle Scholar
- Guan ZH, Qian TH, Yu XH: Controllability and observability of linear time-varying impulsive systems. IEEE Trans. Circuits Syst. 2002, 49: 1198–1208. 10.1109/TCSI.2002.801261MathSciNetView ArticleGoogle Scholar
- Medina EA, Lawrence DA: Reachability and observability of linear impulsive systems. Automatica 2008, 44: 1304–1309. 10.1016/j.automatica.2007.09.017MathSciNetView ArticleGoogle Scholar
- Fang, T, Sun, J: Stability analysis of complex-valued impulsive system. IET Control Theory Appl. 2013 (accepted)Google Scholar
- Fu XY: Null controllability for the parabolic equation with a complex principal part. J. Funct. Anal. 2009, 257: 1333–1354. 10.1016/j.jfa.2009.05.024MathSciNetView ArticleGoogle Scholar
- Hovhannisyan GR: Asymptotic stability for second-order differential equation with complex coefficients. Electron. J. Differ. Equ. 2004, 85: 1–20.MathSciNetGoogle Scholar
- Everitt WN, Markus L:Controllability of -matrix quasi-differential equations. J. Differ. Equ. 1991, 89: 95–109. 10.1016/0022-0396(91)90113-NMathSciNetView ArticleGoogle Scholar
- Zahreddine Z, Elshehawey EF: On the stability of a system of differential equations with complex coefficients. Indian J. Pure Appl. Math. 1988, 19: 963–972.MathSciNetGoogle Scholar
- Everitt WN: Linear control theory and quasi-differential equations. Z. Angew. Math. Phys. 1987, 38: 193–202. 10.1007/BF00945405MathSciNetView ArticleGoogle Scholar
- Ráb M: The Riccati differential equation with complex-valued coefficients. Indian J. Pure Appl. Math. 1970, 20: 491–503.Google Scholar
- Barreiro JT, et al.: An open-system quantum simulator with trapped ions. Nature 2011, 470: 486–491. 10.1038/nature09801View ArticleGoogle Scholar
- Bushev P, et al.: Multiphoton spectroscopy of a hybrid quantum system. Phys. Rev. B 2010., 82: Article ID 134530Google Scholar
- Schoelkopf RJ, Girvin SM: Wiring up quantum systems. Nature 2008, 451: 664–669. 10.1038/451664aView ArticleGoogle Scholar
- Nielsen MA, Chuang IL: Quantum Computation and Quantum Information. Cambridge University Press, Cambridge; 2000.Google Scholar
- Freiling G, Hochhaus A: On a class of rational matrix differential equations arising in stochastic control. Linear Algebra Appl. 2004, 379: 43–68.MathSciNetView ArticleGoogle Scholar
- Reid WT: A matrix differential equation of Riccati type. Am. J. Math. 1946, 62: 237–246.View ArticleGoogle Scholar
- Everitt WN, Zettl A: Generalized symmetric ordinary differential expressions I: The general theory. Nieuw Arch. Wiskd. 1979, 27: 363–397.MathSciNetGoogle Scholar
- Everitt WN: Linear control theory and differential equations. Z. Angew. Math. Phys. 1987, 38: 193–203. 10.1007/BF00945405MathSciNetView ArticleGoogle Scholar
- Diamandescu A: ψ -bounded solutions for a Lyapunov matrix differential equation. Electron. J. Qual. Theory Differ. Equ. 2009, 17: 1–11.Google Scholar
- Sericola B, Remiche MA: Maximum level and hitting probabilities in stochastic fluid flows using matrix differential Riccati equations. Methodol. Comput. Appl. Probab. 2011, 13: 307–328. 10.1007/s11009-009-9149-zMathSciNetView ArticleGoogle Scholar
- Lin Z: Feedback stabilization of MIMO nD linear systems. IEEE Trans. Autom. Control 2000, 45: 2419–2424. 10.1109/9.895586View ArticleGoogle Scholar
- Shiratori N, Yan S, Shieh HJ, Xu L: State-space formulation of n -variable bilinear transformation for n -D systems. IEEE Int. Symp. Circuits Syst. Proc. 2010, 3: 1009–1012.Google Scholar
- Wonham WM: Linear Multivariable Control: A Geometric Approach. 2nd edition. Springer, New York; 1979.View ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.