1. Introduction
Fractional calculus refers to the integration and differentiation of a non-integer order and is as old as the classical (integer order) calculus [
1]. It is a subject that has gained much popularity and importance in the last few decades and has been applied in several fields of knowledge, such as mechanics [
2,
3], bioengineering [
4], signal and image processing [
5], physics [
6,
7], viscoelasticity [
8], electrical engineering [
9], economics [
10], epidemiology [
11,
12], control theory [
13,
14], energy supply-demand systems [
15], and fuzzy problems [
16].
One of the specificities of fractional calculus is that there are many definitions of fractional derivatives that allow the researcher to choose the one that best corresponds to a given problem. Some of the most commonly used fractional derivatives are the Riemann–Liouville, the Erdélyi–Kober, the Caputo, the Hadamard, and the Grünwald–Letnikov derivatives. For a detailed study on this subject, see [
1,
17]. In this present work, we consider fractional operators with respect to an arbitrary kernel (see [
17] for the Riemann–Liouville sense and [
18] for the Caputo sense).
Fractional calculus of variations is a recent field that consists of minimizing or maximizing functionals that depend on fractional operators. The first works in this scientific area are due to Riewe [
3,
19]. Since then, many papers were published on different topics of the fractional calculus of variations for different types of fractional operators (see [
2,
20,
21,
22,
23,
24,
25,
26,
27,
28,
29] and the references therein). For more details, we recommend the works [
30,
31,
32].
By considering a more general form of the fractional derivative, like the Caputo fractional derivative with respect to an arbitrary kernel (see [
18]), we can generalize different fractional variational problems. In [
23,
33], necessary and sufficient optimality conditions were proven for different variational problems depending on the Caputo fractional derivative with respect to an arbitrary kernel.
In [
33], the following problem was studied: determine
and
that extremize:
where
and
and
denote, respectively, the left and right
g-Caputo fractional derivatives of
x of order
and
, with
(see Definition 3). The main results of [
33] are optimality conditions for variational problems with or without isoperimetric and holonomic constraints. The aim of this paper is to generalize these previous results. It is important to mention that this type of generalized fractional variational problem cannot be solved using the classical theory. Moreover, since the
g-Caputo fractional derivatives are generalizations of several fractional derivatives and the variational problem (
1) is a generalization of several kinds of the calculus of variation problems, the results obtained in [
33] not only generalize some known results, but also give new contributions to the theory of the fractional calculus of variations.
In this paper, we prove optimality conditions for different fractional variational problems that are generalizations of the one introduced in [
33]. Namely, we prove the generalized fractional variational principle for problems with optimal orders, with time delay and with arbitrary real order fractional derivatives. In addition, we prove sufficient optimality conditions for all of the problems considered in the paper.
This main structure of this paper is as follows. In
Section 2, we present some preliminaries on fractional calculus. In
Section 3, we exhibit the main results. We finish the paper with two examples and some conclusions.
2. Preliminaries
We begin with a brief review of some important concepts and results that will be used in this paper. In what follows, represents the well-known Gamma function, and the integer part of is denoted by .
Definition 1. [
17]
Let γ be a positive real, a function with positive derivative, and . The left Riemann–Liouville fractional integral of x of order γ, with respect to the kernel g, is defined as:and the right derivative is given by: Next, we present the definitions of the g-Riemann–Liouville fractional derivatives of a function x of order .
Definition 2. [
17]
Let γ be a positive real, a function with positive derivative, and . The left Riemann–Liouville fractional derivative of x of order γ, with respect to the kernel g, is given by:and the right derivative by:where . Remark 1. It is easily seen that:
- 1.
for certain choices of the kernel g, we recover well-known fractional derivatives, such as Riemann–Liouville (), Hadamard (, ), and Erdélyi–Kober fractional derivatives (, );
- 2.
Next, the concept of g-Caputo fractional derivatives of x of order is presented, which is fundamental for the formulation of our problem.
Definition 3. [
18]
Let γ be a positive real and:Let be two real functions defined on , where g satisfies . The left Caputo fractional derivative of x of order γ, with respect to the kernel g, is defined as:and the right derivative as: Remark 2. It is clear that if g is the identity, then and are the usual Caputo fractional derivatives of x. Notice that if , then: Since the integration by parts formula is of great importance in the calculus of variations, we state here this basic result.
Theorem 1. [
18]
Let x be a continuous function and two functions, with domain . Then,and: Remark 3. In particular, if , Theorem 1 reduces to:and: Next, we present the following result, which is useful in applications. For a more detailed study of the
g-Caputo fractional derivatives, we refer to [
18].
Lemma 1. [
18]
If , then:and: Throughout the text, the partial derivative of L with respect to its i-th argument is denoted by .
3. Main Results
Now, we are ready to present the main contributions of this work, by proving some generalizations of the fractional variational problem studied in [
33]. The results of the paper are trivially generalized for the case of vector functions
x.
3.1. Generalized Fractional Variational Principle with Optimal Orders
One of the advantages of fractional derivatives is that, in many real problems, they better describe the dynamics of the problems compared to the classical derivative. With this in mind, a natural issue is to include the order of the fractional derivatives in the optimization process, that is, the variational problem under study consists of finding a curve x, a parameter , and the order of the fractional derivatives and that extremize the variational functional.
Consider the following problem:
Problem 1. Determine the functions of class , the parameters , and fractional orders that minimize or maximize:where and and can be fixed or free. For simplification, we use the notation:
The next result is the optimal fractional order variational principle for Problem 1.
Theorem 2. If is an extremizer of functional defined by (2) and if the maps:are continuous, then, for all t, Furthermore, the following conditions hold:where, for each , and are the functions defined as follows: If is not fixed, the following is verified: Furthermore, if is not fixed, Proof. Suppose that
is an extremizer for functional
. Hence, for any (fixed)
,
such that
and
, with
in a neighborhood of zero, we conclude that:
Therefore, the following condition holds:
Integration by parts gives (see Remark 3):
We first consider functions
such that
. In this case, Equation (
9) becomes:
By the arbitrariness of
,
, and
, if we consider that all of them are null, using Lemma 2.2.2 in [
34], we get:
for all
t, proving the Euler–Lagrange Equation (
3). Since
satisfies Equality (
3) for all
, the first integral in (
10) vanishes, and then, it takes the form:
By the arbitrariness of
,
, and
, we deduce from (
11) the necessary conditions (
4)–(
6). We now seek the natural boundary conditions.
- 1.
If
is not fixed in the formulation of the problem, then
need not to be null at
. Restricting
to be null at
and substituting the necessary conditions (
3)–(
6) into (
9), it follows that:
Since
is an arbitrary real, we prove (
7).
- 2.
Suppose now that
is not fixed. Restricting
to be null at
and using similar arguments as previously, we get Equation (
8).
□
Remark 4. We note that if L does not depend on , , , and ζ, then Theorem 2 reduces to Theorem 2.9 from [
23]
if the final time is fixed. 3.2. Generalized Variational Problems with Time Delay
It is known that a delay is inherent in many problems, such as in control theory, bioengineering, electrochemistry, and social sciences. Differential equations with time delays have been used to model complex systems and have led to an intense topic of research for many years. Although fractional derivatives are not local in nature and are capable of modeling memory effects, delays are also very important because they take into account the system’s history from a previous state. For these reasons, many real-world problems can be modeled more precisely, including fractional derivatives and time delays. In recent years, delayed fractional differential equations have started to attract the attention of many researchers [
35,
36,
37]. Few works are yet devoted to fractional variational problems with time delay so far [
38,
39,
40].
Encouraged by the importance of considering a delay in many real-world problems, we study here the following fractional problem with a time delay , where satisfies .
Problem 2. Determine a function , subject to , for all , where X is a given initial function of class and that minimize or maximize:where . Theorem 3. Suppose that is an extremizer of defined by (
12)
and that the functions exist and are continuous:and: Then, for all ,and for all , Moreover,and if is not fixed, then: Proof. Let
be a
function vanishing on
, and let
be a real. Consider:
defined on an open interval containing zero. Since
is an extremizer of
, then
, and therefore:
Considering
, we obtain:
Observe that, for
,
and for
,
By Theorem 1 and (
19), we obtain:
Again, by Theorem 1 and (
20), we obtain:
Introducing (
18), (
21), and (
22) into Equation (
17), we can conclude that:
Since Equation (
23) is valid for any variations
and all
, assuming that
vanishes on the interval
and taking
, from Lemma 2.2.2 in [
34], we prove that Condition (
13) holds on
. Restricting the variations
to those functions that satisfy
and introducing Condition (
13) into (
23), we obtain:
Since the last equality holds for all
, then, in particular, it holds for
; hence, from Lemma 2.2.2 in [
34], Condition (
14) holds on the interval
. Introducing (
14) into (
24), we conclude, from the arbitrariness of
, that
proving the necessary condition (
15). If
is free,
need not to be null; in this case, we get from (
23) that:
From the arbitrariness of
, we prove Condition (
16), as desired. □
Remark 5. We remark that:
- 1.
if the delay is removed (), then Problem 2 coincides with the problem given by (
1)
if we consider fixed, and therefore, the fractional variational principle given by Theorem 3 in [
33]
can be obtained from Theorem 3; - 2.
when the final time is fixed, Theorem 2.7 in [
23]
can be obtained from Theorem 3.
3.3. Generalized Higher Order Fractional Variational Principle
In this subsection, we consider an extension of the generalized variational problem given by (
1), by including in the Lagrangian function arbitrary real fractional orders
. With this, we obtain what is known as a fractional variational problem with arbitrary higher order fractional derivatives. The problem formulation is the following.
Problem 3. Find functions of class and that minimize or maximize the functional:where , and , for . Furthermore, the boundary conditions:are assumed to hold, where are fixed, for all k. Theorem 4. If is an extremizer of functional defined by (
25)
and the functions exist and are continuous:for all , then:and: If is not fixed, then:and if is not fixed, then: Proof. Consider the pair given by
, where
satisfies
and
, for all
, and
are two arbitrary real numbers. Observe that:
Defining:
the condition
implies that:
Applying Theorem 1, we get, for each
,
and:
Since
and
are arbitrary, we prove Equations (
27)–(
30). □
Remark 6. We remark that:
- 1.
we considered the constraints (
26)
for the simplicity of presentation; of course, we could consider the case when and , , are free, and at the end deduce the respective natural boundary conditions; - 2.
Theorem 2.8 in [
23]
with the final time fixed is a corollary of Theorem 4.
3.4. Sufficient Optimality Conditions
In this subsection, we give sufficient conditions of optimization for all the problems considered previously, first for Problem 1.
Theorem 5. Suppose that L satisfies the inequality:for all , , and such that and , where and as defined in Theorem 2. If satisfies the necessary conditions (
3)–(
8)
, then is a minimizer (resp. maximizer) of functional . Proof. We present the proof only when the inequality (
31) holds for ≥; the other case is similar. Let
,
, and
such that
and
. In what follows, we denote:
Using Conditions (
3)–(
8), we conclude that:
proving the desired result. □
Definition 4. Let and such that . Function is said to be jointly convex in if, for all , are continuous and satisfy:for all . We say that L is jointly concave in if the previous inequality holds, replacing ≥ by ≤. Next, we present a sufficient optimality condition for the problem considered in
Section 3.2.
Theorem 6. Let L be jointly convex (respectively jointly concave) in . If satisfies the necessary conditions (
13)–(
16)
, then is a minimizer (respectively maximizer) of functional . Proof. Consider a function
, of class
, vanishing at
, and let
be an arbitrary real number. Using the same ideas used to prove Theorem 3, one gets:
where
denotes the left-hand side of Equation (
23). Introducing (
13)–(
16) into the last expression, we get that
, as desired. □
The following result can be proven using the same methods as before.
Theorem 7. Suppose that L is jointly convex (respectively jointly concave) in . If satisfies the necessary conditions (
27)–(
30)
, then is a minimizer (respectively maximizer) of functional . 5. Concluding Remarks
Optimization problems are an important issue in several fields of research. In particular, variational problems are useful in Newton’s laws of motion, geometric optics, mathematical economics, hydrodynamics, minimal surfaces, Noether’s theorems, etc. For centuries, the considered problems involved integer order derivatives only, but in the last few years, generalizations of such a rich theory were considered, by including fractional derivatives in the formulation of the variational problems. However, due to the large number of choices for such fractional derivatives, we considered here a general form of the fractional derivative. We continued our study initiated in [
33], by considering three new questions: first, how to find the best order of the fractional derivatives that extremizes the functional, secondly to determine the necessary conditions of optimization with time delay, and finally, when the Lagrangian function contains higher order derivatives. To end, sufficient conditions were proven and some examples were given.
For the future, one important problem is to develop numerical methods to deal directly with the variational problems of these types, without the use of necessary conditions, for example: using discretizations of the fractional derivatives and of the integral, reduce each problem to a finite dimensional one or, using appropriate approximations of the derivatives, depending only on the first order derivative, convert the fractional variational system as an ordinary optimal control problem. Other possibilities can be studied to enrich this theory.