Abstract
We research a control problem for an ecological model given by a reaction–diffusion system. The ecological model is given by a nonlinear parabolic PDE system of three equations modelling the interaction of three species by considering the standard Lotka-Volterra assumptions. The optimal control problem consists of the determination of a coefficient such that the population density of predator decreases. We reformulate the control problem as an optimal control problem by introducing an appropriate cost function. Then, we introduce and prove three types of results. A first contribution of the paper is the well-posedness framework of the mathematical model by considering that the interaction of the species is given by a general functional responses. Second, we study the differentiability properties of a cost function. The third result is the existence of optimal solutions, the existence of an adjoint state, and a characterization of the control function. The first result is proved by the application of semigroup theory and the second and third result are proved by the application of Dubovitskii and Milyutin formalism.
1. Introduction
In recent decades, there has been an increasing interest in the mathematical modelling of several biological phenomena: pattern formation, epidemic disease transmission, blood circulation, atherosclerosis, species competency, migration of species, and so on, see for instance [1,2,3,4,5,6]. In particular, in this paper we are interested in a reaction-diffusion system with initial and boundary value conditions of the following type
where is a bounded set with boundary ; is a fixed time denoting the total time of the process; the coefficients () are all positive constants; are some given functions modelling the functional responses or interactions; is the outward unit normal to , the boundary of ; and are some given functions from to modelling the initial conditions. The system (1) arose in the mathematical model of an ecological system under the considerations of Lotka-Volterra competence assumptions for three species, for instance u, v and w denotes the population densities of herbivorous, carnivorous and plants or pest, predator and plants, respectively. The boundary conditions (1d) are considered to model the case of an isolated environment, i.e., there is neither immigration nor emigration of species during the process.
The maintenance of an adequate number of individuals of each species in the ecosystem is crucial to preserve the harmony and stability between living beings and the environment in which they inhabit. Therefore, understanding the mechanism of control of species is necessary to maintain the equilibrium of the ecosystem. For instance in the case of agriculture, the control of density for pest individuals can be done by incorporation of a predator and by using a chemical pesticide. Now, the usage of pesticides can dangerous for some species in the environment and should be optimized. In particular, considering mainly the following two assumptions: the density of eradicated pests is proportional to the density of living pests and the pesticide is uniformly distributed in the ecosystem, and to the determination of the proportional factor introducing the following control problem
where from to is the control function.
The control problem (2) can be recast as an optimal control problem. Let us consider the notations: ; the topological dual of H; and the set of functions such that and , which is a Banach space with the norm . Moreover, we consider the notation
For a major details on Sobolev space we refer to [7]. We define the cost functional as follows
We notice that the cost function J is constructed in a appropriate sense, for instance in the case of pest-predator-plants ecosystem, we have that the first term minimizes the total density of pests and maximizes the total density of plants, and the second term is introduced to reduce the exposition to pesticides. Thus, we observe that (2) can be rewritten as the optimal control problem
or equivalently, in the context of Dubovitskii and Milyutin formalism, as the generic optimization problem
where the operator is defined by
if and only if
We observe that the system (2) is a particular case of the system (8) when for
The main contributions of this paper are the introduction of appropriate assumption and a functional framework such that we can prove three kinds of results: (i) the existence and uniqueness of a positive solution of system (2) (see Theorem 1); (ii) the explicit calculus of descent and dual cones of J and other differentiability properties of the operators M defined on (7) and (8) (see Lemma 1); and (iii) the existence of solutions for (5), the existence of solutions for the adjoint system for system (2), and the characterization of control function (see Theorem 2).
We remark that there are several optimal control problems for reaction-diffusion equations in the recent literature, for instance [8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28]. Some relevant results for semilinear parabolic differential equations are presented in [26]. In [14,15,22] the authors study the solution of inverse problems related with the reconstruction of coefficients in reaction-diffusion systems arising in epidemiology, from observations of the state solution in a fixed time, by application of some results of standard optimal control theory. A closer study of the problem (5) is the analysis introduced by [10] for the particular case of functional responses given by and . In a broad sense, the author of [10] assume the dependence of the state variables on a given and instead of (5) she study the optimization problem
where the notation to emphasize the dependence of the state variables on . The cost functional is called the reduced cost functional [26]. Other relevant works for optimal control problems for Lotka-Volterra-like systems using the reduced form are [20,28]. Moreover, the application of Dubovitskii and Milyutin formalism to control problems arising in heat and solidification phenomena was conducted in [13,21], respectively. However, to the best of our knowledge, there is not yet in the literature an application of Dubovitskii and Milyutin to study optimal control problems in reaction-diffusion systems arising in the competition of organisms or species in an ecosystem.
The article is organized as follows. In Section 2 we introduce some additional notation; we precise the mathematical assumptions used on the paper for the physical domain, the coefficients, the functional responses, the control function and the initial conditions; and we introduce the statements of main results. In Section 3 we recall some useful results related with differential equations on Banach spaces and the Dubovitskii and Milyutin formalism. In Section 4 we introduce the proofs of main results. Finally, in Section 5 we give the conclusions and the guidance for future work.
2. Assumptions and Statements of Main Results
2.1. Assumptions
Hereinafter, we consider the following assumptions:
Assumption 1.
The set is a bounded domain with a boundary of class with .
Assumption 2.
The coefficients and are strictly positive.
Assumption 3.
The initial condition is belong the set defined as follows
and also the functions and are strictly positive on
Assumption 4.
The functions are assumed satisfying the properties: ; both functions are locally Lipschitz, bounded and positive on ; and also
Assumption 5.
The function
2.2. Statements of Main Results
Theorem 1.
Consider that the Assumptions 1–5 are satisfied, then there is at most one strictly positive global strong solution of the system (2) belong to such that
for a. a. and for some generic positive constant C (independent of and ζ).
Lemma 1.
Consider that the hypotheses of Theorem 1 are satisfied, and the operators J and M defined on (4), (7) and (8), respectively. Then the following assertions are satisfied
- (a)
- The setsdefines the descent and dual cones of J.
- (b)
- The application M is Gâteaux differentiable and the derivative of M in is defined by if and only if
- (c)
- The application M is strictly differentiable and is a surjective operator.
- (d)
- The following setsare the tangent and dual cones to the set at , respectively. Moreover, is a vector space.
Theorem 2.
Consider that the Assumptions 1–4 are satisfied. Then, the following assertions are satisfied
Remark 1.
The switching function considered in (17) depends of the optimal control solution. However, we observe that it can rewritten in a more simply sense by getting precise bounds of the controlled and adjoint systems. For instance, one way to get that is detailed below. From Theorem 1, we have that is strictly positive and bounded on . To fix ideas, let us consider the positive constants and such that on , i.e., we have the bounds of
Then, if we were able to deduce upper and lower bounds for , we would rewrite (17).
3. Preliminaries
For completeness of the presentation, we recall some results related with the theory of differential equations on Banach spaces, the Dubovitskii and Milyutin formalism and differential calculus on Banach spaces.
Theorem 3
([10], Theorem 2.1). Let X a Banach space; the linear operator be the infinitesimal generator of a -semigroup of contractions on X; and be a measurable function in t and Lipschitz in , uniformly with in . Then, for any , the following Cauchy problem
has a unique mild solution belong . Moreover, if X is a Hilbert space, A is a self-adjoint dissipative operator on X, , the mild solution is a strong solution and is belong
To introduce the results of the differential calculus on Banach Spaces, we consider the normed vector spaces X and Y, U a neighborhood of , and an application, for more details we refer to the book of Brezis [29].
Definition 1.
We say that F has a derivative in the direction at if there exists the Y-limit
Definition 2.
The first variation of the application F at is the application defined by when exists for all .
Definition 3.
Let us denote by the space of linear continuous operators from X to Y. Consider that there exists satisfying the relation . We call to Λ the Gâteaux derivative of F at and is appropriately denoted by . Then, satisfies the relation when for each .
Definition 4.
Consider that F satisfy the following relation
at some neighborhood of . Then the application F is called Fréchet differentiable at and Λ, usually denoted by , is called the Fréchet derivative (or only the derivative) of the application F in .
Definition 5.
The application F is called strictly differentiable at if there exists the operator such that
for all satisfying the restrictions and .
Remark 2.
If F is an application which is Gâteaux differentiable for each and also is a continuous application in , then F is strictly differentiable in U.
The terminology and results related with the Dubovitskii and Milyutin formalism are presented below, for major details consult [30,31]. Firstly, we consider the generic optimization problem
where J from X to is a functional and M from X to Y is an operator, with X and Y Banach spaces.
Definition 6.
The vector h belong the Banach space X is said a descent direction of the functional at if there is a neighborhood U of h and such that the inequality is satisfied for all and for any . Additionally, we said that the functional is regularly decreasing at if the set of all descent directions at is a convex set.
Definition 7.
Let h belong the Banach space X and with the set defining the i-th inequality restriction. The vector h is said a feasible direction at if for all and for any with U of a neighborhood of h. Additionally, the inequality restriction set is called regular at if the set of all feasible directions on at is a convex set.
Definition 8.
Let h belong the Banach space X and with the set defining the i-th inequality restriction. The vector h is said a tangent direction at for if for each there is such that for some neighborhood U of zero, for any small enough, or equivalently Additionally, if the the set of all possible tangent directions is a vectorial subspace it is said a tangent space, and also the inequality restriction is called regular at if the set of all possible tangent directions for at define a convex set.
Definition 9.
A set , with X a real Banach space, is said a cone with vertex at zero if for all and . Moreover, we call the dual cone for K to the set denoted by and defined as follows
Proposition 1.
The descent, feasible and tangent directions generate cones with vertex at zero. Moreover, the cones generated by the descent and feasible directions are open sets.
Theorem 4
(Dubovitskii and Milyutin theorem). Consider the optimization problem (19). Assume that has a local minimum at , is regularly decreasing at , with descent directions cone , , are regular at , with feasible directions cone , and is regular at , with tangent directions cone . Then, there exist continuous linear functionals , not all identically zero, such that
Theorem 5
([32]). (Lyuternik theorem) Let X and Z be real Banach spaces with norms and , respectively; a given mapping; and . If satisfy the following assumptions: h is Fréchet differentiable on a neighborhood of be continuous on and be surjective, then
4. Proofs of Main Results
4.1. Proof of Theorem 1
We observe that we can rewrite the system (2) as the Cauchy problem (18) in the the Hilbert space by introducing the notation
However, we cannot directly apply the Theorem 1 since does not satisfies the Lipschitz condition. Then, to study the existence and uniqueness for (18) for the particular case of functions and spaces given in (20), we proceed in three steps: we prove the existence and uniqueness of a positive local solution, we prove that the local solution is a global solution, and we deduce the estimates (10)–(12).
- Step 1:
- Local solutions via a truncated problem. Let us consider the truncated Cauchy problemwhere and
We observe that the Assumption 4 implies that satisfies the Lipschitz hypothesis required by Theorem 3. Then we can deduce that the Cauchy problem (21) has a unique strong solution belong
We prove that the solution of (21) is bounded as follows. Let us consider the uncoupled Cauchy problems
where
The strong solution of (22) satisfy the relation
Using the definition of and we deduce that each component of and for any are negative. Then, from (23) we have that each component of is negative on . Analogously, we can prove that each component of is positive on as consequence of the facts that each components of and are positive for any . Thus, noticing that are solutions of (22), we can deduce the bound
Then, and the upper bound is which clearly does not depends of N.
On the other hand, we observe that satisfy the system
Let us consider and defined by and for such that . Testing (25a) by , integrating by parts on , and using the boundary conditions (25b), we have that
Now, using the Hypotheses (A2), (A3) and (A4), we deduce the following bounds
for some positive constants . Then, by application of the integral form of Gronwall’s inequality, we have that for , i.e., on or on . Thus, from the arbitrariness of and the fact that the components of are positive on Ω, we get that the components of are strictly positive on .
To conclude the step 1, we deduce the existence and uniqueness of a positive local solution of (2) as follows. We observe that, if we select , we get that for some Now, from the estimate (24) we deduce that on for Then, from the definition of we have that for and is a solution of (2) on .
- Step 2:
- The local solution is a global solution. To prove that the local solution on is a global solution it suffices to prove that is bounded on . Indeed, from (2c), the positivity of on deduced on Step 1 together with the positivity of g on given by Assumption 4, the strictly positivity of assumed in Assumption 2, and the fact that and is strictly positive on Ω (considered on Assumption 3), we deduce that for . Then, .
On the other hand, from (2a) and (2b) and Assumption 4, we have that and on with solution of the following uncoupled system
Then, we deduce that since . We follow that are defined on and are such that and w are positive on , and is belong Thus, we can deduce (9).
- Step 3:
Then using the fact that are bounded and positive and the Assumptions 3–5, we follow the estimate (10). The estimates (11) and (12) can be proved by similar arguments, starting from (2b) and (2c), respectively.
4.2. Proof of Lemma 1
Proof of item (a). From definition of J given in (4), we deduce that J is linear and consequently we get
We notice that is a convex and continuous operator. Then from ([30], Theorem 7.3), we get that J is regularly decreasing for all and the set
is the descent cone for J. Moreover, from result of ([30], pp. 69) we obtain that the set
is the dual dual cone of
Proof of item (b). Using the definition of M given on (7) and (8), the system (13), and simplifying, we deduce the following relation
Here, we notice that
and similarly
Consequently, we can deduce that
and conclude the proof of item (b).
Proof of item (c). To prove that the operator is surjective, we notice that the system (13) can rewritten as an abstract Cauchy problem of the form (18) with and
We notice that the relations and the inclusion , imply that and consequently we get that implies that ; and also A and satisfy that the hypotheses of Theorem 1. Then, we can deduce that the system (13) has a unique (global) strong solution belong to such that
for a. a. and for some generic positive constant C (independent of and ζ). Hence is surjective.
To prove that the application is strictly differentiable is sufficient to verify that the function is continuous or equivalently that it will suffice to prove that is bounded in since is linear. From (13), we get
which implies that for are bounded by application of estimates (10)–(12) and (32)–(34). Then, by using the norm of we have that
Therefore is bounded in and thus the proof is complete.
Proof of item (d). From Lemma 1-(b), we have that the application M is is Gâteaux differentiable and in a neighborhood of and by Lemma 1-(c) we have that is continuous in a neighborhood of and surjective. Consequently, the hypothesis of Theorem 5 are satisfied and the tangent cone to at is the kernel of the differential operator given on (14). Moreover, by linear algebra result we deduce that is a vector space, since the kernel of a linear operator is a vector space. Now, the proof of (15) is deduced by the definition.
4.3. Proof of Theorem 2
Proof of item (a). We can develop the proof of existence of at least one solution for the optimization problem (6) via the standard method of minimizing sequences. Let us consider that a sequence in such that
From application of Theorem 1 we deduce the following bounds
for a. a. and for some generic positive constant C (independent of and n). We observe that the sequence is uniformly bounded in and in .
The compactness of in for any is proved as follows. From (35c) we deduce that
for any which implies that there is such that
Then by the Ascoli-Arzela Theorem there is at least one subsequence of (also labeled by ) and there are such that in uniformly in . Now, from the compact embedding of in we deduce that is compact in for any . Then by the Ascoli-Arzela Theorem is compact in and there is at least one subsequence of (also labeled by ) and there are such that in uniformly in . Hence, we have that there is subsequence (also labeled by ) and there is such that
We notice that and by the convergence properties of the sequence , we can take the limits in the system (35) and deduce that satisfy a system associated with . Using the lower semi-continuity of cost functional we deduce that
Thus, is a solution of the problem (6).
Proof of item (b). Let us consider the change of variable and for and . Then, we have that is solution of (16) if and only if is solution of the following system
We observe that the system (37) as the Cauchy problem (18) with and by considering that
Thus, we conclude the proof by application of Theorem 1.
Then by Theorem 4, we have follow the existence of two continuous functionals and , not both identically zero, satisfying the Euler–Lagrange equation
Now, in order to prove the relation (17) we consider arbitrarily and assume that is a solution of the following system
The existence of solutions of (39) can be developed similarly to the system (37). From Lemma 1, we follow that and consequently Now, from (38) and some , we follow that
We note that , since if we assume that , we have that and from (38) we deduce that , which is a contradiction with the fact deduced by Theorem 4: there exist continuous functionals and , not both identically zero. Then, from (40) we get
by dividing by λ and also without loss of generality we can fix .
On the other hand, by multiplying the first, second, and third equations of system (16) by and respectively and integrating by parts over , we obtain
Now, adding (42)–(44) and rearranging terms, we obtain
Using (39) in (45),we obtain
Since is arbitrary, we can chose with . Then (47) becomes
If we assume that , the inequality (48) implies the relation on for all . Similarly, starting by considering that , from (48) we deduce that on for all . Thus, from a definition of the set , we deduce that on when and on when . Hence, the optimal control can be expressed by the expression on (17). This completes the proof.
5. Conclusions and Future Work
This paper presents a the application of Dubovitskii and Milyutin formalism to study a control problem for a reaction-diffusion system arising in the competency of three species in a bounded ecosystem. The control problem is reformulated as an optimal control problem by incorporating a cost functional which maximizes the total density of beneficial species to the ecosystem and minimizes the pests and the exposition to the human intervention by incorporation of some substances to control pests. The main assumptions considered in order to deduce the results are the following: the ecosystem is modeled by a set which is bounded and has sufficiently smooth boundaries, the coefficients and initial conditions are strictly positive, the initial conditions satisfies a consistent relationship with the boundary conditions, the functions modelling the interaction of species are assumed positive and locally Lipschitz, and the admissible control set is assumed to be the square integrable functions. The main results obtained in the paper are: (i) the existence of a strictly positive global solution for controlled system which is deduced by using the theory of differential equations on Banach spaces; (ii) the construction of descent and dual cones of cost function and the differentiability of the operator defining the restriction set of the optimization problem is based on the application of differential calculus on Banach spaces; (iii) the existence of of at least one optimal control solution; (iv) the existence of solutions for the adjoint system; and (v) a characterization of control function by a function of “bang-bang” type with the switching function depending of the controlled and adjoint systems.
In our future work, we plan to extend the present research in at least three ways as follows. First, we plan to study other cost functions, for instance the incorporation of observations and/or attainable states in the L2-norm. Second, we will study the construction of some explicit bounds for the controlled and adjoint systems, such that the switching function can be determined only in terms of coefficients, initial conditions and the geometry of the ecosystem (see Remark 1). This is an important issue to be researched in order to develop numerical simulations of the control problem without solving the optimization problem. Thus, an another idea to study the switching function by follow similar ideas to those given in [33]. Third, we plan to develop a numerical study of the optimal control problem by applying a finite volume method and make some simulations of published experimental results.
Author Contributions
Conceptualization, M.R.-M.; Formal analysis, F.H.; Investigation, A.C.; Methodology, F.H. and M.R.-M.; Project administration, A.C.; Software, E.L.; Writing–original draft, E.L. All authors have read and agreed to the published version of the manuscript.
Funding
A.C., F.H. and E.L. acknowledge the partial support of Universidad del Bío-Bío (Chile) through the research project of Posdoctoral Program as a part of the project “Instalación del Plan Plurianual UBB 2016–2020” and Universidad Tecnológica Metropolitana through the project supported by the Competition for Research Regular Projects, year 2020, code LPR20-06. MR acknowledge the partial support of CAPES-PRINT 88887.311962/2018-00 (Brazil) and Project UTA-Mayor, 4753-20, Universidad de Tarapacá (Chile).
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Conflicts of Interest
The authors declare no conflict of interest.
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