Abstract
This paper addresses the approximate controllability results for Hilfer fractional stochastic differential inclusions of order . Stochastic analysis, cosine families, fixed point theory, and fractional calculus provide the foundation of the main results. First, we explored the prospects of finding mild solutions for the Hilfer fractional stochastic differential equation. Subsequently, we determined that the specified system is approximately controllable. Finally, an example displays the theoretical application of the results.
Keywords:
approximate controllability; hilfer fractional derivative; multi-valued maps; stochastic theory; fixed point theorem MSC:
34A08; 34A25; 37L55; 47H10; 93B05
1. Introduction
Controllability, a core topic in mathematical control theory, is closely related to other concepts like structural decomposition, engineering, observer design, and pole assignment. From a mathematical standpoint, it is important to differentiate between exact and approximate controllability. A system with approximate controllability can be directed to any arbitrarily small neighborhood of the final state, whereas a system with exact controllability can be steered to any specific final state. Controllability issues arise in various fields of physics and engineering, including heat flow in materials with memory, viscoelasticity, and other physical phenomena, often modeled as abstract differential systems in infinite-dimensional spaces. For additional information on control theory, we recommend consulting the article and the references listed [1,2,3,4,5,6,7,8,9,10].
The capability of fractional calculus to capture inherited characteristics has garnered substantial interest recently from diverse scientific and technical fields, such as chaotic behavior, fractional differential equations, thermal physics, and biology. To address various phenomena in practical applications, different fractional derivatives have been developed, including the Riemann–Liouville, Caputo, Hadamard, and Grunwald–Letnikov fractional derivatives. In addition, a generalized version of the Riemann–Liouville (R-L) fractional derivative (R-LFD), called the Hilfer fractional (HF) derivative (HFD), was introduced by Hilfer [11]. It encompasses the R-L and Caputo fractional (CF) derivatives (CFD). Theoretical studies of dielectric relaxation in glass-forming materials gave rise to this operator. Since, several scholars have investigated fractional differential equations with HFD (see [12,13]). The authors of [14,15] studied the existence results for HFD with order in Hilbert and Banach spaces. The existence of HF differential inclusions with order has been studied by [16]. We direct interested readers to the monographs and papers listed here [17,18,19,20,21,22] and the references therein for further information.
Moreover, both artificial and natural systems are inevitably subjected to noise or stochastic issues. As a result, stochastic differential systems have gained substantial interest due to their extensive applications in modeling complex dynamic systems within biological, physical, and medical fields [23,24]. In [25], the authors evaluated controllability results of CF stochastic differential inclusions with nonlocal conditions. Currently, the authors [26] studied the approximate controllability results for HF stochastic differential equations with order . For further references, see these articles [27,28,29,30,31,32,33].
The research based on the approximate controllability of Hilfer fractional differential inclusions when order is an untreated topic and motivated by articles [14,26], our goal is to fill this gap. Examine the Hilfer fractional stochastic differential inclusions system, together with its control term:
where is the HFD with an order and type such that and . The strongly continuous cosine family is represented by the infinitesimal generator , where takes values in separable Hilbert space . is a bounded linear operator from into , where is a Hilbert space, and is the control function. Let be another real separable Hilbert space with and the norm . Assume that is a -valued Brownian motion or Wiener process with a finite-trace nuclear covariance operator . The R-L integral of order (where ) is denoted by the symbol . The function f and will be defined later. The initial conditions include
We organized the paper as follows:
2. Preliminaries
Given a normal filtration , where , let be a complete probability space. Throughout the paper, we make use of the following:
- Take the real separable Hilbert spaces , .
- A Wiener process with linear bounded covariance operator such that is represented as .
- From to , the set of bounded linear operators is represented by .
- Let us assume that there is a complete orthonormal system in , a bounded sequence series of non-negative real numbers ∋, and a sequence of independent Brownian motions such that
- Let represent the space of all Hilbert–Schmidt operators with the inner product from to .
- Letting be a Banach space containing all strongly measurable, square-integrable, and -valued random variables, we may define the norm as follows: , where defines the expectation.
- Let
- .
- is the space of all -adapted, -valued measurable square integrable processes on .
- For any continuous map from into , let be the Banach space and its norm be
- ; with norm . Here, is a Banach space.
Definition 1
([34]). The R-L fractional integral is defined as follows:
where is the Gamma function.
Definition 2
([34]). The R-LFD is defined as follows:
Definition 3
([34]). The CFD is defined as follows:
Definition 4
([11]). The HFD is defined as follows:
where .
Remark 1.
Based on Definition 4, it follows that
- (i)
- If , we have
- (ii)
- If , we have
Definition 5
([35]). If is continuous, and is absolutely continuous, then
where
We will provide an overview of multi-valued analytical facts in the following pages. More details can be found in [36,37].
Definition 6.
(i) A multivalued map is convex (closed) valued for a Hilbert space , if is convex (closed) for any . If , then is bounded on bounded sets. For every bounded set of (that is, , is bounded in .
- (ii)
- If, for every , the set is a nonempty, closed subset of , and if, ∀ open set of containing , ∃ an open neighbourhood of such that , then is termed upper semicontinuous (u.s.c.) on .
- (iii)
- When is a relatively compact bounded subset of every bounded subset , then is considered to be completely continuous.
- (iv)
- When a multivalued map has nonempty compact values and is completely continuous, it can only be said to be u.s.c. if it has a closed graph, meaning that , , and imply .
- (v)
- If there is an in ∋, then has a fixed point.
- (vi)
- The multivalued map is considered measurable if the function , defined byis measurable. Here, is the set of all nonempty bounded, closed and convex subsets of .
Definition 7
([37]). Let be a -Caratheodory if
- For each is measurable;
- For all is u.s.c.;
- For each there exists , such that
Definition 8
([38]). Suppose is a Hilbert space and is a compact interval. If is a linear continuous mapping from to , and is a Carathodory multivalued map with , then the operator
is a closed graph operator in , where is the set of all nonempty bounded, closed and convex subsets of , and is known as the selector set from , which is given by
Definition 9
([39]). Consider the space , and a bounded linear operator is considered a strongly continuous cosine family if:
- for all
- ;
- is continuous in χ on ∀ fixed point .
One parameter family, the sine function , is defined by
where is a strongly continuous cosine family in .
The operator is the infinitesimal generator of a strongly continuous cosine family . It is defined by
Here,
Lemma 1
([39]). Strongly continuous cosine family on satisfying in , for all and some , , and is the infinitesimal generator of . Then, for , and
In this paper, since is the infinitesimal generator of a strongly continuous cosine family of uniformly bounded linear operators in , there exists a constant such that and for .
Definition 10
([14]). is an -adapted stochastic process; it is said to be the mild solution of the Cauchy problem (1), if , and such that , and
for where
where
is a function of a Wright type and
Lemma 2
([14]). The operator has the following properties:
- For all the operators are uniformly continuous;
- The inequality below is true for any and ,
Definition 11.
To examine the approximate controllability of system (1), we present the following operators.
- The controllability Grammian is defined bywhere and denotes the adjoint operator of and .
- The resolvent operator
We now assume the following hypothesis:
- :
- as in the strong topology.
Consider the following deterministic linear system associated with (1)
is approximately controllable on if and only if condition is satisfied; refer to [40,41].
Lemma 3
([42]). Let be a nonempty subset of that is bounded, closed, and convex. Assume is u.s.c. with closed, convex values, and that and is compact. In this case, has a fixed point.
Lemma 4
([40]). For any there exists such that
3. Main Results
To explore the existence results for the system (1), we present the following hypotheses:
- (H1):
- The continuous function , and there exists a positive constant such that the function satisfies that
- (H2):
- The multi-valued map is an -Caratheodory function that satisfies the below conditions:
- For each the function is u.s.c; and for each the function is measurable. And, for each fixed the setis non-empty.
- There exists a positive function such thatfor a.e. and the function such that
Theorem 1.
Assume hypotheses – are satisfied. Then, the system (1) has a mild solution on , provided that
where
Proof.
To demonstrate that system (1) has mild solutions, convert it into a fixed point problem. For any , take the operator defined by
Our goal is to demonstrate that has a fixed point. We separate the proof into many steps for the ease of application.
Step 1. is convex for each . If , then there exists , such that
Let us assume that . Then for each , we have
Since is convex, . Hence,
Step 2. We show that ∃ such that . Here, is a bounded, closed, convex set in .
If it is not true, then there exists such that for every positive number and , there exists a function , but , that is, . For such , we can show that
where
By applying Hlder’s inequality and –, we obtain
If we divide the above inequality by on both sides and assuming , we get that
which is a contradiction to our assumption. Thus, for , for some positive number and some , .
Step 3. is equicontinuous on Take for , ; we can easily get as . For , there exists , and we get
Note that if we apply -inequality, then we get
By applying Hlder’s inequality and –, we obtain
According to the above analysis, to tends to , i.e., for ; therefore, recalling the relationship of and , one can easily deduce that is equicontinuous on .
Step 4. Next, we show that the set is relatively compact in .
For , it is evident that is relatively compact in for . Assume that is fixed. Then, we define the operator for any , arbitrary and by
If is compact, then is also compact. Then, for any and any , we can deduce that is relatively compact in . In addition, for any , we obtain
For , we have
To prove , we obtain
Before examining the , utilizing and the for all we deduce that
Now, we need to prove , then
For , we get
Let us take (4), and we get
For , we have
By using the Equation (4), we get
By using the Lebesgue dominated convergence theorem, we derive that to . Thus, . Therefore, the set is relatively compact in .
Step 5. has a closed graph.
Let , as , for each , and as . We shall show that . Since , then there exists such that
We have to demonstrate that exists such that
To illustrate that
Consider the linear continuous operator , so we have
Moreover, we have
Since , it follows from Lemma 8 that
therefore, has a closed graph.
Using the Arzela-Ascoli theorem, we deduce that is a compact multivalued map, u.s.c., with convex closed values, as a result of Steps 1 through 5. From Lemma 3, we may infer that has a mild solution of system (1) with a fixed point on . □
Theorem 2.
Suppose that the assumptions of Theorem 1 hold. Then, the system (1) is approximately controllable on .
Proof.
Let be an operator fixed point. Any fixed point of is a mild solution of (1) according to Theorem 1. According to the stochastic Fubini theorem, this indicates that there exists , and such that
Furthermore, according to the Dunford–Pettis Theorem and the boundedness of f and , we have that the sequences , and are weakly compact in and . As a result, some subsequences are still denoted by , and that weakly converge to say, f and , respectively, in and .
Conversely, based on the assumption of , the operator strongly as for every , and additionally, . Thus, the compactness of and the Lebesgue-dominated convergence theorem provide
It follows that holds, demonstrating that the proof is complete and the system (1) is approximately controllable. □
4. Example
Consider the upcoming Hilfer fractional stochastic control system of the form:
where is the HF partial derivative , , and is an R-L integral with order ().
Take and defined satisfies , Then, for any , the uniformly bounded strongly continuous cosine family has as its infinitesimal generator. Take implying that are eigenvalues of , and that is an orthonormal basis of . Now,
where is the inner product . From [39], we have
According to [39], we have
where is the Mittag-Leffler function. Let , and . The functions and are satisfies and .
Since (5) can be formulated as problem (1) in , it follows that the Theorem 1 satisfies all assumptions, and then that system (5) has a mild solution and is approximately controllable on .
5. Conclusions
This study examined the approximate controllability of Hilfer fractional stochastic differential inclusions with order . Cosine families, Hilfer fractional derivatives, fixed point theory, fractional calculus, and stochastic analysis all form the foundation of our findings. First, we obtain the mild solution for the given system (1) and show that it is approximately controllable. Lastly, an application that demonstrates the theory is presented. Further work will investigate the existence and approximate controllability of Hilfer fractional stochastic integrodifferential systems with order using the noncompactness measure.
Author Contributions
Conceptualization, A.S., S.K.P., V.V. and K.K.; Methodology, A.S., V.V. and K.T.; Validation, S.K.P., V.V., K.K. and K.T.; Formal analysis, A.S., S.K.P. and V.V.; Investigation, A.S., S.K.P., V.V. and K.T.; Data curation, K.K.; Writing – original draft, A.S., S.K.P., V.V., K.K. and K.T.; Writing – review & editing, V.V.; Visualization, K.K.; Supervision, K.T. All authors have read and agreed to the published version of the manuscript.
Funding
The corresponding author expresses sincere gratitude to the Science and Engineering Research Board (SERB) for their generous financial support through the MATRICS grant program (Grant No. (MTR/2023/000064)). This funding has been instrumental in the successful execution of research project and has significantly contributed to the advancement of knowledge in field.
Data Availability Statement
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.
Conflicts of Interest
The authors declare no conflicts of interest.
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