Abstract
In this paper, we concentrate on a control system with a non-local condition that is governed by a Hilfer fractional neutral stochastic evolution hemivariational inequality (HFNSEHVI). By using concepts of the generalized Clarke sub-differential and a fixed point theorem for multivalued maps, we first demonstrate adequate requirements for the existence of mild solutions to the concerned control system. Then, using limited Lagrange optimal systems, we demonstrate the existence of optimal state-control pairs that are regulated by an HFNSEHVI with a non-local condition. In order to demonstrate the existence of fixed points, the symmetric structure of the spaces and operators that we create is essential. Without considering the uniqueness of the control system’s solutions, the best control results are established. Lastly, an illustration is used to demonstrate the major result.
Keywords:
optimal control; Hilfer fractional derivative; stochastic evolution equation; neutral system; hemivariational inequalities; non-local condition MSC:
26A33; 34A08; 49J20; 93E20
1. Introduction
The hemivariational inequality (HVI) was originally established by Panagiotopoulos in 1981 as a weak formulation for a number of kinds of mechanical problems using non-smooth and non-convex energy functionals [1,2]. Recently, many researchers have paid a lot of attention to the control problems of hemivariational inequalities (HVIs). Migórski and Ochal [3] explored the direct approach of the calculus of variations along with the Galerkin method in 2000 when it came to the optimal control issues of the parabolic HVIs. Using the Faedo-Galerkin technique and the direct method of the arithmetic of modification, the authors [4] showed in 2007 that there are optimum control pairs for a hyperbolic quasi-linear HVI. Researchers [5] recently employed a convergence point approach for multivalued maps to look at the approximative controllability of HVIs. The optimum control issue of second-order stochastic evolution hemivariational inequalities (SEHVIs) with Poisson jumps was recently addressed by Muthukumar et al. [6] using the fixed point technique of multivalued maps and Balder’s theorem. In order to investigate the optimal controls and solvability of impulsive HF delay evolution inclusions with Clarke sub-differential, Harrat et al. [7] used fractional calculus, semigroup theory, fixed point strategy, and multivalued analysis.
Understanding partial differential equations is greatly aided by symmetry analysis, especially when dealing with equations derived from mathematical concepts relating to accounting. Even though symmetry is absent from the majority of natural observations, it is the secret of nature. The occurrence of unexpected symmetry-breaking is a better way to conceal symmetry. The two categories are finite and infinitesimal symmetry. There are two types of discrete and continuous finite symmetries. Natural symmetries like parity and temporal inversion are discrete whereas space is a continuous change. On the other hand, fractional calculus has grown in importance in mathematics during the recent several decades. Fractional-order differential equations are better suitable for various physical issues than integer-order differential equations. Due to their applications in viscoelasticity materials, electrical circuits, neural networks, control theory, chemistry, engineering, biology, mechanics, and physics, fractional differential equations (FDEs) have become a popular research topic ([8,9,10,11,12,13,14]). We point out that during the past three decades, FDEs have undergone a substantial evolution (see, for instance, [15,16,17,18,19,20]) and are a useful instrument for the description of specific materials and processes [21,22,23,24,25,26,27,28,29,30].
Stochastic differential equations (SDEs) are useful tools in several branches of science and engineering for describing some systems and processes with stochastic disturbances. See the monograph [31] for information on the general concept of stochastic differential equations. Furthermore, it should be noted that both natural and artificial systems exhibit stochastic discomfort or noise. Stochastic differential systems have garnered a lot of interest because of their numerous uses in the biological, physical, and pharmaceutical sciences (see [32,33,34]); they are crucial for simulating real-world processes when an element of randomness is required. Stochastic evolution equations (SEEs) in infinite-dimensional spaces are inspired by the random events studied in the biological sciences, such as thermodynamics, molecular biology, and operations research. Many authors have extensively studied the existence of mild solutions for various types of SEEs and their optimum management in Hilbert spaces (see [35,36,37]).
The Reimann–Liouville (R-L) and Caputo fractional derivatives are also included in the Hilfer fractional derivative (HFD), which was first developed by Hilfer [9]. In theoretical electromagnetic simulations of glass-forming components, it is used. The presence of mild solutions to an evolution equation with HFD was looked at by Gu and Trujillo in [38]. The solvability and best controls of impulsive Hilfer fractional (HF) delay evolution systems with Clarke sub-differential were investigated by Harrat et al. in [7]. Non-local conditions for HF evolution equations provide some fascinating findings. Yang and Wang, for instance, looked at the approximability of controllability of an HF differential system with non-local conditions in [39]. The existence of mild solutions to an HF differential equation with non-local conditions was investigated by the researchers in [40,41].
Due to their vast applicability in numerous fields of pragmatic mathematics, neutral systems have attracted increased interest in recent years. With or without delay, various neutral systems, such as thermal expansion in substances, stretchability, surface waves, and several organic improvements, profit from neutral systems. Readers can consult [19,33,42,43] for more information on the neutral system and its use.
The idea of controllability is crucial to the study and design of control systems, as is well known. Furthermore, it is important to research hemivariational inequalities with fractional derivatives since they are related to applicable disciplines including evaporation in heat exchangers, thermoviscoelasticity, and selective memory thermodynamics. The authors [44] investigate the HF evolution hemivariational inequalities with non-local initial conditions and optimal controls for condensing multivalued maps. The fixed point theory for multivalued maps has been used to generate the optimum control issues for Hilfer fractional neutral stochastic evolution hemivariational inequalities, which were inspired by the aforementioned work.Both the evaluation of hemivariational inequality and the consideration of the controllability of the control systems described by a class of stochastic HVI with fractional derivatives appear to have been neglected.
We believe that the literature has not yet addressed the presence of and optimal controls for the HF neutral stochastic evolution hemivariational inequality (HFNSEHVI). This work’s main focus will be on the existence and optimal control of the subsequent control system, which is governed by HFNSEHVI.
where denotes the , , . The state variables takes values in the Hilbert space with the norm and the inner product . The infinitesimal generator of the strongly continuous cosine family in is . Let be a control function, and be the set of all admissible controls that is also a Hilbert space. is a bounded linear operator. The function and are the appropriate function. Let be a complete probability spaces, and let be the other separable Hilbert space. Assume the Wiener process is a -Wiener process with nuclear covariance operator having a finite trace. The norm of is denoted by the same notations, , where represents the space of all bounded operators from into . Simply as if . The Clarke sub-differential of a globally Lipschitz function is denoted by the notation . Let be the set of all admissible state control pairs and E denote the expectation of a random variable or the Lebesgue integral with regard to the probability measure P. The cost functional on the set is provided by
The plan of this paper is organized into five sections. We list some important preliminaries in Section 2. In Section 3 we demonstrate that the System (1) has a mild solution given a few reasonable assumptions. We answer the optimal control problem governed by (1) under acceptable criteria in Section 4. In Section 5, a specific illustration is given to demonstrate our primary findings.
2. Preliminaries
This section provides the basic material as well as the essential fractional calculus ideas, notations, and lemmas that are necessary to establish the main findings.
Suppose that is a separable Hilbert space and its norm represented by . Consider denotes the complete probability space with the usual classification . denotes the Hilbert space of all strongly -measurable square integrable -valued random variable satisfying . Suppose that is the Banach space of all continuous maps from with . represent the Hilbert space of all stochastic processes -adapted measurable determined on using values in and a norm . The space represents the Hilbert space of all stochastic processes -adapted measurable determined on assuming values in and a norm .
We suppose that ∃ a complete orthonormal system in , a bounded sequence of non-negative real integers , such that , and a sequence of independent Wiener process, such that
Let and defined by
Suppose that , then is called a Q-Hilbert Schmidt operator. Let the space of all Q-Hilbert Schmidt operators be defined as . The fulfilment of with regard to the geometry caused by , with is a Hilbert space with the above norm geometry.
The concepts from fractional calculus are introduced below. For more information [10,13].
Definition 1
(see [10,13]). For a function g, the fractional integral of order with the lower bound zero is defined as
if the right side is point-wise defined on , where Γ is the gamma function. We make the unassumed assumption that the gamma functions utilised in this work are real without loss of generality.
Definition 2
(see [10,13]). For a function , the R-L derivative of order with lower limit zero can be denoted as
Definition 3
(see [10,13]). The Caputo fractional derivative of order can be defined as
where the derivative of the function g is completely continuous up to order .
Definition 4
(see [9]). The HFD of order , for the function ϰ is defined by
We now go over numerous fundamental properties of a multivalued map; for further information, please see the works by [45,46].
In the case of a Banach space Y with , designates the dual of Y, and the pairing of Y and . For our satisfaction, we will be using the following conditions:
We will now define the generalised Clarke gradient for a globally Lipschitzian functional . represents the Clarke geometric derivative of at in the direction , i.e.,
As you may recall, the Clarke sub-differential of at , denoted by , is a subset of generated by
The upcoming fundamental characteristics of the generalised geometric derivative and the generalised gradient are crucial to our main conclusions.
Proposition 1
(see [47]). If is a globally Lipschitz function on an open set Λ of , then
- (i)
- ∀, one has
- (ii)
- ∀, the derivative is a convex, non-empty, weak-compact subset of and ∀ (where is the Lipschitz constant of g near ϰ);
- (iii)
- The graph of the generalized derivative is closed in topology, i.e., suppose that and are sequences, such that and in , in , then (where represent the Banach space related with the -topology);
- (iv)
- The multi-valued function Λ such that is upper semi-continuous.
Lemma 1
(see [48]). Let be a strongly measurable mapping such that . Then
∀ and , where is the constant employing p and .
Theorem 1
(see [49]). Let Y be a globally convex Banach space and be a compact convex valued, upper semi-continuous multivalued map such that ∃ a closed neighborhood V of 0 for which is a relatively compact set. Assume that
is bounded, then has a fixed point.
3. Existence
The following fractional evolution inclusion can be taken into account while analyzing System (1):
where denotes the generalized Clarke sub-differential of a globally Lipschitz functional . The control function is a stochastic process provided in of admissible control functions, and the set is a Hilbert space, is a bounded linear operator. is a appropriate functions and is measurable -valued random variables independent of W.
It is clear that each solution to System (3) also solves System (1). In reality, suppose is a solution of the System (1), then ∃ a function , a.e., , and satisfies the following equation:
In view of above equation, we obtain
Since and , we obtain
It is proved that by using the equivalent evolution inclusion System (3), we may refer the System (1).
Lemma 2
(see [19]). The operators , and admit the following conditions:
- (a)
- For any fixed , , and are bounded linear operators such that, ∀,
- (b)
- , and are strongly continuous.
- (c)
- If is compact, then ∀, , and are also compact operators.
Lemma 3
(see [48]). Suppose is a compact -semigroup ∀, then it is uniformly continuous ∀.
Proposition 2.
Consider and ∀, then ∃ a such that
Definition 5.
For each , an -adapted stochastic process is called a mild solution of the control System (3) suppose and ∃ a , such that , a.e., and
The following hypotheses are used throughout this paper:
- (H0)
- The operator is compact ∀.
- (H1)
- The function is continuous in ∀, and ∃ a constant , such that .
- (H2)
- fulfils the following requirements:
- (a)
- ∀, is measurable;
- (b)
- For a.e. , is globally Lipschitz continuous;
- (c)
- ∃ a , and a constant , such thatfor a.e. and ∀.
- (H3)
- is continuous in the second variable for a.e. and ∃ a function , and a constant , such that
- (H4)
- ∃, a constant such that ∀,
- (H5)
- is a continuous function and ∃ constants and , such that σ is -valued and fulfils the following requirements:
Define the admissible set as follows:
Then, by Proposition 2.1.7 and Lemma 2.3.2 of [46], we know that ; and is bounded, convex, and closed subset of with . Clearly, ∀. Next, define an operator by
We also require the following lemmas in order to reach our main results:
Lemma 4
(see [35]). Provided that holds, then ∀, the set has non-empty, convex and weakly compact values.
Lemma 5
(see [35]). Suppose that holds, Υ satisfies: if , weakly in and , therefore .
Lemma 6
(see [6]). Suppose that holds and the operator Υ fulfills: if in , weakly in and , then .
Theorem 2.
Suppose that holds, then the HF stochastic System (1) has a mild solution on given by
Proof.
∀, by corresponding Lemma 4, consider the multi-operator as follows:
We have now come to the conclusion that the goal of our concentrated effort was to identify a fixed point of . We now prove that satisfies each and every necessary premise of Theorem 1. We organised our evidence into six phases, as shown below, to make it easier to use.
Step 1: Now, we’ll demonstrate that has convex, non-empty, and weakly compact values ∀. By using Lemma 4, we may simply demonstrate that has non-empty and weakly compact values. Additionally, the values of are convex; by giving and then , we can now draw a result ∀. The function is convex.
Step 2: is bounded in C, where , ∀. Certainly, is the closed, convex and bounded set of C.
In practise, it is sufficient to demonstrate the existence of a positive constant , such that , ∀, and . Suppose that , then ∃ a function such that
From , Lemma 1 and the Hölder inequality, we obtain
Thus, is bounded in .
Step 3: is equicontinuous.
Firstly, ∀,
Next, for small enough and , we get
By the strong continuity of , we get as .
Similarly,
By assumptions and the same method used in Lemma 3.1 of [32], we get
Further, using a similar way, we can get
Hence, using Lebesgue’s dominated convergence theorem, we deduce that the right side of the above inequalities tends to zero as . Therefore, we deduce that is continuous from the right in . Which is likewise continuous from the left in , as shown by a similar argument.
In a similar manner, for and , we may demonstrate independently of as .
Therefore, it appears from the above explanations that is an equicontinuous set of functions in .
Step 4: is completely continuous.
Suppose that be fixed. We prove the set is relatively compact in . It is clear that is compact.
Therefore, only must be taken into account. Let be fixed and ∀, ∃ a , such that
∀ and any , we define
From the boundedness of , and the compactness of , we obtain the set is relatively compact in ∀. Furthermore, ∀, we get
⟹ the set is totally bounded. The Ascoli–Arzela theorem allows us to prove is completely continuous.
Step 5: has a closed graph.
Suppose that in , and in . We will prove that . Indeed, means that ∃ a , such that
From and , we may prove that is bounded. Therefore, we may suppose, proceeding on if required to a subsequent thought, that
It should be noted that in and . According to Lemma 6 and Equation (6), we get . Consequently, we have shown that , ⟹ has a closed graph. From [47], it may be concluded is upper semi-continuous.
Step 6: A priori estimate.
It is evident from Steps 1–5 that is compact convex value and ϑ, is a relatively compact set. We continue from Theorem 1 to demonstrate the collection
is bounded to get a fixed point of .
Consider and suppose ∃ a , such that
From , Lemma 1 and the Hölder inequality, we obtain
where
Hence, from , the inequality (7),
Therefore, the set is bounded. We determined that has a fixed point from Theorem 1. Hence, completed the proof. □
4. Optimal Controls
In this segment, we look at the preceding Lagrange problem (LP):
Find a pair such that
where
Here, represents the mild solution of system (1) relating to the control . We base our analysis on the following assumption to denote the Lagrange problem :
- (a)
- The functional is Borel measurable;
- (b)
- For almost all , is sequentially l.s.c. on ;
- (c)
- ∀ and almost all is convex on ;
- (d)
- ∃ constants , is positive and , such that
Theorem 3.
Assume that are fulfilled. The optimal control problem permits at least one optimal pair if is a strongly continuous operator.
Proof.
If , then we can simply determine Lagrange problem has a single optimal pair. Without loss of consensus, we might assume that . Then condition implies that . By definition of infimum, ∃ a minimizing sequence of possible pair , such that as . Since is bounded on , due to the reflexivity of , ∃ a subsequence of , represented again by , and satisfying
Since is convex and closed, it follows from Mazur’s lemma that . Consider the related sequence of solutions to the following integral equation be denoted by the symbol :
where and .
We then demonstrate that is a relatively compact subset of . Firstly, in a similar manner that the proof of Equation (7), we get
Because of the boundedness of , (9) and Gronwall’s inequality, we infer that ∃ a constant , such that , ⟹ is uniformly bounded.
Then, according to the argument of Steps 3 and 4 in Theorem 2, we may prove is equicontinuous on and is relatively compact ∀. Thus, the Ascoli–Arzelà theorem ⟹ is a relatively compact subset of and so ∃ a function , such that
The boundedness of and compactness of together with the dominated convergence theorem
Equivalent to the proof of Step 5 in Theorem 2, according to the compactness of , , , (10) and Lemma 6, one has
where and . Hence, it concludes from (11) and (12) that
This proves that is a mild solution of (1) following to the control .
We discern that satisfy all the hypotheses of Balder’s theorem (see Theorem 2.1 of [50]). Therefore, Balder’s theorem shows that the functional
is sequentially lower semi-continuous in the strong topology of and weak topology of . Since , we deduce that is weakly lower semi-continuous on . From the hypotheses , we know that . Thus, we show that reaches its infimum at and so
This completes the proof. □
5. Example
We wrap up this discussion with a straightforward illustration. We can provide references [7,51] for mathematical induction of HFD and approximate solutions of various fractional differential systems. Take into account the preceding inclusion problem:
where is the HFD of order and type , is the R-L integral of order . , m is a non-negative integer and . Take . Consider , and
Here, is a two-sided, one-dimensional Brownian motion in defined on filtered probability space , and denotes the generalized gradient of a globally Lipschitz function . A straightforward illustration of satisfying the condition is where are convex quadratic functions (see [18,47]). The function , satisfies the condition .
Let us consider the operator which is defined in with . The strongly continuous semigroup , which is compact ∀, algebraic, and identity, is then produced by . is known to have a discrete spectrum with eigenvalues of the kind , and the corresponding normalized eigenvectors are given by . Similarly, since is an orthonormal basis for , and also may be denoted by . In particular, (see [48] for more information). If we assume that , then ℏ fulfills condition (C50) (see [39]). Emphasize that the problem (14) may be denoted as (3), an abstract form. From Theorems 2–3, Equation (14) has a mild solution for , appropriately small, and its corresponding limited Lagrange problem admits at least one optimal possible pair.
6. Conclusions
For a class of HFNSEHVI with non-local circumstances, this work investigates whether mild solutions and ideal controls exist. We first established sufficient conditions for the existence of mild solutions to the relevant control system using notions from the extended Clarke sub-differential and a fixed point theorem for multivalued maps. The existence of optimum state-control pairings that are governed by an HFNSEHVI with a non-local condition was then shown using restricted Lagrange optimal systems. The optimum control outcomes are attained without taking into account how distinctive the solutions of the control system are. Finally, an example is used to demonstrate the major conclusion. In the next paper, it will be explored if there are any mild solutions and what the best controls are for HF stochastic integro-differential evolution HVIs with non-local conditions.
Author Contributions
Conceptualisation, S.S., R.U., V.S., G.A. and A.M.E.; methodology, S.S.; validation, S.S., R.U., V.S., G.A. and A.M.E.; formal analysis, S.S.; investigation, R.U.; resources, S.S.; writing original draft preparation, S.S.; writing review and editing, R.U., V.S., G.A. and A.M.E.; visualisation, R.U., V.S., G.A. and A.M.E.; supervision, R.U.; project administration, R.U. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.
Acknowledgments
The authors are appreciative of the reviewers of this work who provided thoughtful comments and guidance that enabled us to edit and enhance the papers content.
Conflicts of Interest
The authors declare no conflict of interest.
Abbreviations
The following abbreviations are used in this manuscript:
| HF | Hilfer fractional |
| HFD | Hilfer fractional derivative |
| HVI | Hemivational inequality |
| HVIs | Hemivational inequalities |
| FDEs | Fractional differential equations |
| SDEs | Stochastic differential equations |
| SEEs | Stochastic evolution equations |
| SEHVIs | Stochastic evolution hemivational inequalities |
| HFNSEHVI | Hilfer fractional neutral tochastic evolution hemivational inequality |
| R-L | Riemann–Liouville |
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