Discrete Dynamics in Nature and Society
Volume 2016 (2016), Article ID 3713864, 10 pages
http://dx.doi.org/10.1155/2016/3713864

Research Article

Estimation and Global Stability Analysis of PDAEs with Singular Time Derivative Matrix and Wetland Conservation Application

Yushan Jiang1,2 and Qingling Zhang¹

¹Institute of System Science, Northeastern University, Wenhua Road, Shenyang 110819, China
²School of Mathematics and Statistics, Northeastern University at Qinhuangdao, Taishan Road, Qinhuangdao 066004, China

Received 3 August 2015; Accepted 8 December 2015

Academic Editor: Carmen Coll

Copyright © 2016 Yushan Jiang and Qingling Zhang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Some partial differential algebraic equations (PDAEs) system with singular time derivative matrices is analyzed. First, by PDE spectrum theory, this system is formulated as infinite-dimensional singular systems. Second, the state space and its properties of the system are built according to descriptor system theory. Third, the admissible property of the PDAEs is given via LMIs. Finally, the developed energy estimation method is proposed to investigate the global stability of PDAEs. The proposed approach is evaluated by an application in numerical simulations on some wetland conservation system with social behavior.

1. Introduction

Singular systems have abilities in representing dynamical systems in the areas of electrical circuits and multibody systems, chemical engineering and economic systems, and mechanical structure and biological systems. Singular systems are also referred to as descriptor systems, differential algebraic equations (DAEs) systems, implicit systems, or generalized state space systems [1]. A large number of fundamental ideas and results based on state space systems have been successfully extended to singular systems, such as controllability observability, pole assignment, stability, and stabilization [1–6]. In recent years, partial differential algebraic equations (PDAEs) have become an independent field of research. The applications and mathematical theory on PDAEs have attracted increasing attention to academics. The PDAEs research areas can be classified into three groups:

(i)Index analysis and solvability of PDAEs (see [7–13]). (ii)Innovative and improved numerical methods to solve PDAEs (see [14–16]). (iii)Control problems and optimization described by PDAEs (see [17–23]).

The structure analysis and solvability on PDAEs can be traced back to [24]. In [24], with the same method of line (MOL), there exist different spatial indexes between different numerical methods. In order to relate properties of the PDAEs to those of the resulting DAEs, it is necessary to have a concept of the index of possibly constrained PDAEs. Firstly, in [9], the perturbation index was defined on a class of liner time invariant PDAEs. On infinite domain, the wave solutions of PDAEs were also considered by [25]. In [26, 27], there was a systematic analysis about three different types of indexes (model, perturbation, and algebraic). In [28], a consistent representation of the solution of an initial boundary value problem for PDAEs was proposed. The index involved in the problem is characterized by means of Fourier and Laplace transformations. The differentiation index [29] of some general nonlinear PDAEs on hyper-plane domains is a generalization of the differentiation index of DAEs. It provides a way to determine Cauchy data on domain surface which must be consistent with the PDAEs. For the first time the perturbation index for a singular PDE of mixed parabolic-hyperbolic type was computed by [10]. In [5], the index of PDAEs with the sequence of matrices method was proposed. It is an effective method for the finite systems except for the question of the unbounded operator in PDAEs. However, it generalizes the Kronecker index in a rather functional analytic manner. Other researchers also investigated the index structure of PDAEs systems in the area of coupling nonlinear PDAEs systems [7], mixed index [12], and index determination algorithm [30].

Despite the complexity analysis of index on PDAEs, research in the area of control problem for PDAEs is relatively scarce. This approach was first applied to design energy based controller of a coupled wave-heat equation systems in [31]. Stabilizing the coupled PDE-ODE systems with boundary control was considered in [21]. For the averaged model of the switched power converter system [20], by using Lyapunov function, a nonlinear saturating control law was designed. Based on the infinite-dimensional state space representation theory, [19] addressed the linear quadratic regulator control of the PDAEs. The optimal control problem is treated using Operator Riccati Equation (ORE) approach. The previous methods [22, 30] are thought to be derived from DAEs theory. Other optimal control problems [18, 23, 32] are considered with parabolic type PDAEs.

For all above coupled PDE-ODE systems, parabolic distributed PDEs systems, or hyperbolic systems, the time state variables matrices in those systems are reversible. Nevertheless, some singular system like parabolic-elliptic PDAEs cannot be direct application of the above theoretical study. The present work focuses on the development of a generalization stability method for a class of PDAEs with singular time derivative coefficient matrix. Our study derived from the article [13] in which there is a search for series solutions of PDAEs with method on DAEs. Moreover, motivated by the technique in [17, 33], our singular PDAEs system is considered in high-dimensional bounded domain.

The organization of this study is as follows. The problem statement for some singular time derivative PDAEs is given in Section 2. In Section 3, the original PDAEs are described as infinite-dimensional singular systems. And the state variables expression is built with Kronecker-Weierstrass form. Furthermore, the spectrum analysis is proposed to show the convergence property of the PDAEs solution. In Section 4, some dynamical stability property is analyzed with energy estimation method. Finally, in Section 5, as an application, we build some PDAEs model on some wetland conservation system with social behaviours. The applicability of the proposed approach is confirmed by the numerical simulation.

Notations. , , and are the set of real numbers, the -dimensional Euclidean space, and the set of all real matrices, respectively. denotes the Euclidean norm for vectors. For a vector , . if . For a symmetric matrix , means that it is positive (negative) definite. is the identity matrix. The superscript is used for the transpose. Matrices, if not explicitly stated, are assumed to have compatible dimensions. For the convenience, we define the following Hilbert space:

with inner product and -norm, respectively, defined by

2. Description of PDAEs

We consider the linear partial differential algebra equations (PDAEs) system in -dimensional bounded spatial domain with a state space description of the form

subject to the boundary conditions (BCs)

and the initial conditions (ICs)

where , , , is the vector of state variables, is the spatial domain of the process, is the spatial coordinate, is the time, is the Laplace operator, is the manipulated input vector function, is the measure output, and is the measurement disturbance. and are given constants, is the outward normal vector of on , , , and is the spatial initial state function.

Remark 1. For system (3)–(6) we show that at least one of the matrices and is singular. The special case leads to DAEs. Therefore in this study we assume that is not a zero matrix. Another special case, , leads to the PDE-ODE systems which some researchers investigated [20]. In contrast to the problems with nonsingular matrices and , the BCs (5) and ICs (6) have to meet certain consistency conditions. Thus for given PDAEs (3) the subsets and are determined by consistency conditions (see [28]). In general, the BCs for and the ICs for must be determined with the help of the PDAEs (3). With different and values the BCs can be three boundary types: Dirichlet, Neumann, and Robin boundary conditions. We assume that the BCs are homogeneous for simplicity and thought that our proposed method is applicable to the general inhomogeneous boundary conditions. Moreover, we assume that the process in (3)–(6) evolves on a compact set; that is, for all , where is a compact set containing the origin.

3. Decomposition of PDAEs and Spectrum Analysis

3.1. Decomposition of PDAEs and Infinite Singular Systems

In this section, the PDAEs (3)–(6) will be rebuilt as a large infinite-dimensional system. Consider the linear elliptic operator

in with homogeneous BCs (5). We denote by the spectrum of , that is, the set of for which is not invertible. More specifically, for the operator , the eigenvalue problem is defined as

where denotes the th eigenvalue and denotes the corresponding orthogonal eigenfunction. Obviously, the eigenfunctions form an orthogonal basis for eigenvalue problem (8). Furthermore, in the next subsection, we show that has discrete spectrum consisting only of real eigenvalues with at most a finite number of positive eigenvalues which is a generalization of [17]. Applying for PDE eigenvalue theory, we can obtain the following infinite-dimensional differential algebraic equations (IDAEs) systems:

with the initial condition

where

Remark 2. One cannot guarantee that each system is solvable without considering the regularity of the matrix pencil set . Thus in this study we assume that for each the matrix pencil with respect to system (9) and (10) is regular. Furthermore, all systems have the uniform differential time index [13]; that is, for all , the pencil is regular and has the Riesz index (independent of ).

3.2. Eigenvalue Estimation and Property

For the unforced PDAEs

with the corresponding IDAEs

For every , firstly, let us denote by the generalized eigenvalue set of systems (14), where is defined by (8). We also write is the usual eigenvalue set. The subsequent theorem concerning the eigenvalues estimation of for (14) corresponds to systems (3)–(6). Before that, we give two lemmas about the spectrum structural property of the scalar elliptic operator.

Lemma 3 (see [34]). With the strongly scalar elliptic operator ( is strongly elliptic in the sense that there exists such that, for all , ) the operator defined by in the bounded domain with homogeneous boundary conditions of the form

has discrete spectrum consisting only of real eigenvalues. If is bounded in , can have at most a finite number of positive eigenvalues.

Lemma 4 (the Bauer-Fike Theorem). Assume that is a diagonalizable matrix and is the nonsingular eigenvector matrix such that , where is a diagonal matrix. Let be an eigenvalue of ; then there exists such that .

Theorem 5. For the eigenvalue problem (8) the operator has discrete spectrum consisting only of real eigenvalues. And there is a finite number of positive eigenvalues; that is, if all eigenvalues are ordered such that , then there exists a finite number so that and is a small positive number.

Proof. For every component of state vector , let denote the eigenvalue of the Sturm-Liouville (S-L) problem:

where denotes the eigenvalue of (16). It is true that all satisfy Lemma 3. And from the assumption that one can get that, for every given , if, for all , , then all eigenvalue of is negatively defined. Therefore, by Lemma 4, , holds for every where is some given constant independent form . Noticing that , the conclusion holds.

Remark 6. It should be pointed out that the existing one-dimensional result [17] cannot be directly generalized to high-dimensional case because of the mathematical complexity property of the S-L problem on high-dimensional spatial space. Additionally, zero eigenvalue yields the trivial constant state response , which is outside the scope of interest of our study.

From Theorem 5 there exists some such that the operator with respect to (8) has discrete spectrum structure:

and . Now for the matrix pencil with respect to IDAEs (9), if and , then from the definition of eigenvalue, the relationship among , and is Immediately we have the following spectrum structure theorem.

Theorem 7. Assume is the spectrum set of the eigenvalue problem (8) with respect to systems (9). is the eigenvalue of ; then there exists some positive number such that, for every , , the sign of the real part of is reversed with . If the matrix pencil family set is admissible, then must be a nonnegative matrix.

3.3. State and Output Response of PDAEs

In what follows we shall assume that each pencil with respect to (9) is regular and impulse-free. Intuitively, the Kronecker-Weierstrass equivalent form of DAEs [1] can be applied to our systems. However, considering the nonnegative diffusion matrix , this equivalent transformation leads to an unexpected matrix. And the spectrum analysis theorem above is no longer valid. Thus the Jordan transformation is introduced to solve the problem.

For the IDAEs (9), there exists nonsingular matrix such that can be transformed into the following form:

where is a nilpotent matrix with index and is a nonsingular matrix. It follows immediately from the linear singular systems theory [6] that the state response of such system is

where are given by

and are the components of corresponding to the partition of the vector into the regular and nilpotent parts. Thus the PDAEs (3)–(6) have the state response

and the output response

where and are defined by (12).

The above state and output responses discussion provides a generalized systems theoretical approach to the PDAEs. From (20) we know that if there exists an eigenvalue of with negative real part then the solution grows exponentially which coincides with the “explosive solution” from a mathematical perspective. It should be noted that our results generalize the existing works [19, 21] in the aspect of derivative matrix .

For the orthogonal property of the eigenfunction set some dynamical properties of the PDAEs including stability, stabilizability, and detectability can be given through a direct application of the LMI technique about the generalized systems theory [6, 35]. The following theorem shows the admissibility of PDAEs via LMIs.

Theorem 8 (admissibility via LMIs). The PDAEs (13) are admissible if and only if there exists a matrix such that the first subsystem of IDAEs (14) is admissible; that is,

where is the first eigenvalue of the spatial differential operator (7).

Proof. Noticing that all eigenvalues are ordered as , it can be easily deduced from the above that

holds for every . Then the desired result follows immediately by the admissible theory of the pencil (see [36] for details).

4. Stability Analysis

In view of Theorems 7 and 8 above, the systems’ stability can be determined by spectrum analysis and LMIs. This validity of the above stability analysis relies on the convergence properties about the IDAEs. In the following, we propose some exponential stability property on the PDAEs by the energy estimation theory. For the following considerations, it will be simplest to assume homogeneous Neumann boundary conditions.

Lemma 9 (see [34]). Let ; then if is the smallest positive eigenvalue of on (with the appropriate boundary conditions), the following Poincaré inequalities hold:

where .

Theorem 10. Assume that is a bounded solution of (3) and (4) with homogeneous Neumann boundary conditions. Assume that , , is the smallest positive eigenvalue of on , and is the smallest positive eigenvalue of and

Then

for a positive constant , and

where is a positive constant and is the spatial average function; that is, the state variable vector generated by the PDAEs is exponentially stable and asymptotically converges to its spatial average.

Proof. By introducing the energy integral (Lyapunov function),

and compute

Noticing the Neumann BCs with the application of divergence theorem, we have

The above equality can be estimated as

where is the smallest positive eigenvalue of .
According to Lemma 9, (33) implies that

From the assumption that , taking into account the fact that

we obtain that

where

By Lemma 9, (36) implies (29).
Thus, under the conditions of the theorem, the time state response oscillations decay exponentially. The time-space state solution asymptotically approaches the spatial average .

5. Application to the Coastal Wetland Conservation System with Social Behaviour

In this section, we illustrate through computer simulations the application of the theoretical development given above to some wetland conservation system with social behaviour on some plane rectangle domain (see Figure 1).

Figure 1: The idealized spatial domain is a rectangular domain with the sea oriented direction and coast line direction . The wetland conservation is closed with no flux boundary conditions imposed.

Our model derived from [37] in which a reaction-diffusion system incorporating one prey and two competing predator species under homogeneous Neumann boundary conditions was considered. In this model, human, bird (predators), and fish (prey) are chosen as the research objects. The spatiotemporal dynamics between bird and fish with human activity affected in a protected environment can be described by the following PDAEs:

where and represent the population of prey (birds) and predator (fish) species at time and spatial position , respectively; represents the human population distribution; and stand for the spatial diffusion effects of fish and bird, respectively.

Fish population follows the logistic growth in the absence of bird with the intrinsic growth rate and the carrying capacity ; is the death rate of bird with the carrying capacity ; and represent the strength of relative effect of the interaction on the two species.

For a wetland ecosystem, the influence of human can be regarded as an invasive species and is not affected by other species. Thus the influence of human activities (e.g., the economic interest) on fish and bird is represented by and , respectively (in the first two equations of system (38)). Human population in a state of free distribution can be described by

Since the local human population distribution can reach a time independent dynamic balance in a short time, thus the above parabolic equation degenerates to the following elliptic equation:

Moreover, considering the geographical location effect of sea oriented direction we assume that human population is independent with the coast line direction. And the parameters , and    are positive real numbers.

Systems (38) can be rewritten as the following PDAEs:

where

It is obvious that the nonnegative equilibrium of system (38) is the solution of the following nonlinear equations:

The nonnegative equilibrium is

where .

Now we consider the local stability of the equilibrium. We choose . To the consistency conditions the initial conditions are taken as and . The remaining system parameters are chosen as , , , , , and . By direct computing we have the Jacobi matrix of the linearized system at the equilibrium:

According to the stability analysis of Theorem 10 in Section 4, we consider the local stability of these PDAEs at the equilibrium in different parameter values (see Table 1). Due to the particular choice of the system parameters and the state variables and show some different dynamical properties. By increasing the value of , the value of can be changed from positive to negative.

Table 1: Values of the simulation parameters.

Case

1.0069
1.9931

,
3.2841
−0.7159

Simulation results for system (38) in domain at are depicted in Figures 2 and 3. In Figures 2(a) and 2(b) the state variables spatial-temporal show the corresponding exponential stability. It shows great difference with the case in Figure 3. Figure 4 shows the different spatial convergence property of the system. Ecologically, the stable prey-predator relationship stands when the diffusion rates of prey and predator are very high with low human effect rates and which correspond to the experience.

(a)

(b)

(c)

Figure 2: Numerical results for the state estimation analysis. (a) Time spatial response in domain. (b) Time spatial response in domain. (c) The phase trajectory about   (, ).

(a)

(b)

(c)

Figure 3: Numerical results for the state estimation analysis. (a) Time spatial response in domain. (b) Time spatial response in domain. (c) The phase trajectory about   (, ).

Figure 4: Numerical results on spatial convergence of .

6. Conclusion

In this study we have studied the problem of some singular time derivative PDAEs in high-dimensional domain. With the decomposition ideas derived from PDE theory we built the IDAEs for reconstruction of PDAEs. Some admissible results of the IDAEs (9) corresponding the PDAEs (3) are proposed. An exponentially stable result on the PDAEs is presented through the energy estimation about the state variables under the homogenous Neumann boundary conditions for the positive diffusion matrix. Finally, as an application, we built some wetland conservation model with social behaviour. The numerical results show the effectiveness of this development.

Conflict of Interests

The authors declare that they have no conflict of interests regarding the publication of this paper.

Acknowledgments

The research is supported by N.N.S.F. of China under Grant no. 61273008 and no. 61104003. The research is also supported by the Key Laboratory of Integrated Automation of Process Industry (Northeastern University). The authors are grateful to the anonymous referee for a careful checking of the details and for helpful comments that allow them to improve the paper.

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