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Article

Regional Enlarged Observability of Fractional Differential Equations with Riemann—Liouville Time Derivatives

1
TSI Team, MACS Laboratory, Department of Mathematics and Computer Science, Moulay Ismail University, Faculty of Sciences, Meknes 11201, Morocco
2
Center for Research & Development in Mathematics and Applications (CIDMA), Department of Mathematics, University of Aveiro, 3810-193 Aveiro, Portugal
*
Author to whom correspondence should be addressed.
Axioms 2018, 7(4), 92; https://doi.org/10.3390/axioms7040092
Submission received: 4 November 2018 / Revised: 25 November 2018 / Accepted: 28 November 2018 / Published: 1 December 2018
(This article belongs to the Special Issue Fractional Differential Equations)

Abstract

:
We introduce the concept of regional enlarged observability for fractional evolution differential equations involving Riemann–Liouville derivatives. The Hilbert Uniqueness Method (HUM) is used to reconstruct the initial state between two prescribed functions, in an interested subregion of the whole domain, without the knowledge of the state.

1. Introduction

The exact birthday of fractional calculus, and the idea of non-integer differentiation, goes back to the 17th century, precisely to 30 September 1695, when L’Hôpital wrote a question to Leibniz about the meaning of n t n in the case n = 1 2 ([1], pp. 301–302). Since then, many great mathematicians have investigated around this question. We can mention the studies of Euler (1730), Lagrange (1772), Laplace (1812), Fourier (1822), Abel (1823), Liouville (1832), Riemann (1847), Greer (1859), Grünwald (1867), Laurent (1884), Heaviside (1892), Pincherle (1902), Marchaud (1927), Love (1938), Widder (1941), Riesz (1949), Feller (1952), among others [2]. These mathematicians began to consider how to define a fractional derivative. In 1860s, Riemann and Liouville obtained now celebrated definitions of fractional operators, by extending the Cauchy integral formula. Such fractional operators have a major role in practical problems [3]. In particular, Heymans and Podlubny have shown that it is possible to attribute a physical meaning to initial conditions expressed in terms of Riemann–Liouville fractional derivatives on the field of viscoelasticity, which is more appropriate than standard initial conditions [4]. From a mathematical point of view, fractional calculus is a generalization of the traditional differential calculus to integrals and derivatives of non-integer order.
The fact that real systems are better described with non-integer order differential equations has attracted engineers’ interest, making fractional calculus a tool used nowadays in almost every area of sciences. Indeed, in the last decades, fractional calculus has been recognized as one of the best tools to describe long-memory processes and materials, anomalous diffusion, long-range interactions, long-term behaviours, power laws, and allometric scaling [5]. Such models are those described by differential equations containing fractional order derivatives. On the other hand, fractional calculus is especially efficient for modelling systems related to diffusion processes [6]. In [7,8], the heat transfer process was successfully modelled using a fractional model based on normal and anomalous diffusion equations. In [9,10,11], very accurate models for ultra-capacitors and electrical energy storage elements based on diffusion and the Helmholtz effect are presented. Simultaneously, fractional calculus has played a very important role in various fields such as physics, chemistry, mechanics, electricity, economics, signal and image processing, biophysics, and bioengineering [12,13,14]. Likewise, for control theory, fractional calculus has an enormous role [15,16,17]. The reader interested in applications of fractional calculus in control and mathematical modelling of systems and processes in physics, aerodynamics, electrodynamics of complex medium, viscoelasticity, heat conduction, and electricity mechanics, is referred to [2,18,19,20,21,22,23,24] and references therein. For numerical methods to fractional partial differential equations, see [25,26,27].
Despite its development, the theory of fractional differential equations, compared with the classical theory of differential equations, is a field of research only on its initial stage of development, calling great interest to many mathematicians [28,29,30]. For distributed parameter systems, several works deal with the problem of regional observability, which we study here in the fractional context, investigating the possibility to reconstruct the initial state or gradient only on a subregion ω of the evolution domain Ω [31,32,33,34,35]. For results on controllability, see [36,37,38,39]. The interest to study the concept of observability for fractional differential equations is not newee [17,40,41,42]. Here we investigate, for the first time in the literature, the concept of regional enlarged observability, that is, observability with constraints on the state for fractional diffusion equations. For that, we make use of the Hilbert Uniqueness Method (HUM) of Lions [43,44].
The paper is organized as follows. In Section 2, we present the problem of regional enlarged observability of fractional diffusion systems with the traditional first-order time derivative replaced by the Riemann–Liouville time fractional derivative. In Section 3, we give some preliminary results, which will be used throughout the paper. In Section 4, we characterize the enlarged observability of the system. Section 5 is focused on the regional reconstruction of the initial state in an internal subregion of the evolution domain. We present in Section 6 an example to demonstrate our main results. We end with Section 7 of conclusions and some possible directions of future research.

2. Problem Statement

In this section we formulate the concept of regional enlarged observability for a Riemann–Liouville time fractional diffusion system of order α ( 0 , 1 ] . Let Ω be an open bounded subset of R n ( n = 1 , 2 , 3 ), with a regular boundary Ω . For T > 0 , let us denote Q T = Ω × [ 0 , T ] and Σ T = Ω × [ 0 , T ] . We consider the following time fractional order diffusion system:
0 D t α y ( x , t ) = A y ( x , t ) in Q T , y ( ξ , t ) = 0 on Σ T , lim t 0 + 0 I t 1 α y ( x , t ) = y 0 ( x ) in Ω ,
where 0 D t α and 0 I t α denote, respectively, the (left) Riemann–Liouville fractional derivative and integral with respect to time t, with α R such that 0 < α 1 . For details on these operators, see, e.g., [20,24,25,45]. Here we just recall their definition:
0 D t α y ( x , t ) = t 0 I t 1 α y ( x , t )
and
0 I t α y ( x , t ) = 1 Γ ( α ) 0 t ( t s ) α 1 y ( x , s ) d s ,
where Γ ( α ) denotes Euler’s Gamma function. The second order operator A in (1) is linear with dense domain, such that the coefficients do not depend on time t and generates a strongly continuous semi-group ( S ( t ) ) t 0 on the Hilbert space L 2 ( Ω ) . We assume that the initial state y 0 L 2 ( Ω ) is unknown. The observation space is O = L 2 ( 0 , T ; R q ) .
Without loss of generality, we denote y ( · , t ) : = y ( t ) . The measurements are obtained by the output function given by
z ( t ) = C y ( t ) , t [ 0 , T ] ,
where C is called the observation operator, which is a linear operator (possibly unbounded), depending on the structure and the number q N of the considered sensors, with dense domain D ( C ) L 2 ( Ω ) and range in O .

3. Preliminaries

In this section, we recall some results for Riemann–Liouville time fractional differential systems and some notions and results to be used thereafter.
Lemma 1
(See [36,46,47]). For any u 0 L 2 ( Ω ) , 0 < α 1 , we say that the function u L 2 ( 0 , T ; L 2 ( Ω ) ) is a mild solution of the system
0 D t α u ( t ) = A u ( t ) , t [ 0 , T ] , lim t 0 + 0 I t 1 α u ( t ) = u 0 ,
if u satisfies the equation
u ( t ) = H α ( t ) u 0 ,
where
H α ( t ) = α t α 1 0 θ ξ α ( θ ) S ( t α θ ) d θ ,
ξ α ( θ ) = 1 α θ 1 1 α ϖ α ( θ 1 α ) ,
ϖ α ( θ ) = 1 π n = 1 ( 1 ) n 1 θ n α 1 Γ ( n α + 1 ) n ! sin ( n π α ) , θ ( 0 , ) ,
with ξ α the probability density function defined on ( 0 , ) , satisfying
ξ α ( θ ) 0 , θ ( 0 , ) a n d 0 ξ α ( θ ) d θ = 1 .
Moreover,
0 θ ν ξ α ( θ ) d θ = Γ ( 1 + ν ) Γ ( 1 + α ν ) , ν 0 .
Note that a mild solution of system (1) can be written as
y ( x , t ) = H α ( t ) y 0 , t [ 0 , T ] .
In order to prove our results, the following lemma is used.
Lemma 2
(See [48]). Let the reflection operator Q on the interval [ 0 , T ] be defined by
Q f ( t ) : = f ( T t ) ,
for some function f that is differentiable and integrable in the Riemann–Liouville sense. Then the following relations hold:
Q 0 D t α f ( t ) = t D T α Q f ( t ) , Q 0 I t α f ( t ) = t I T α Q f ( t )
and
0 D t α Q f ( t ) = Q t D T α f ( t ) , 0 I t α Q f ( t ) = Q t I T α f ( t ) .
Follows some notions of admissibility of the output operator C. The output function of the autonomous system (1) is expressed by
z ( t ) = C H α ( t ) y 0 = K α ( t ) y 0 , t [ 0 , T ] ,
where K α : L 2 ( Ω ) O is a linear operator. To obtain the adjoint operator of K α , we have two cases.
Case 1.
C is bounded (e.g., zone sensors). Let C : L 2 ( Ω ) O and C * be its adjoint. We get that the adjoint operator of K α can be given by
K α * : O L 2 ( Ω ) z * 0 T H α * ( s ) C * z * ( s ) d s .
Case 2.
C is unbounded (e.g., pointwise sensors). In this case, we have
C : D ( C ) L 2 ( Ω ) O
with C * denoting its adjoint. In order to give a sense to (2), we make the assumption that C is an admissible observation operator in the sense of Definition 1.
Definition 1.
The operator C of system (1) and (2) is an admissible observation operator if there exists a constant M > 0 such that
0 T C H α ( s ) y 0 2 d s M y 0 2
for any y 0 D ( C ) .
Note that the admissibility of C guarantees that we can extend the mapping
y 0 C H α ( t ) y 0 = K α ( t ) y 0
to a bounded linear operator from L 2 ( Ω ) to O . For more details, see, e.g., [49,50,51]. Then the adjoint of the operator K α can be defined as
K α * : D ( K α * ) O L 2 ( Ω ) z * 0 T H α * ( s ) C * z * ( s ) d s .

4. Enlarged Observability and Characterization

Let ω be a subregion of Ω with a positive Lebesgue measure. We define the restriction operator χ ω and its adjoint χ ω * by
χ ω : L 2 ( Ω ) L 2 ( ω ) y χ ω y = y | ω
and
( χ ω * y ) ( x ) = y ( x ) if x ω , 0 if x Ω \ ω .
Similarly to the discussions in [32,49,52], it follows that a necessary and sufficient condition for the regional exact observability of the system described by (1) and (2) in ω at time t is that I m ( χ ω K α * ) = L 2 ( ω ) .
Let β ( · ) and γ ( · ) be two functions defined in L 2 ( ω ) such that β ( · ) γ ( · ) almost everywhere (a.e.) in ω . Throughout the paper, we set
[ β ( · ) , γ ( · ) ] = y L 2 ( ω ) | β ( · ) y ( · ) γ ( · ) a . e . in ω .
We consider
y 0 = y 0 1 in [ β ( · ) , γ ( · ) ] , y 0 2 in L 2 ( Ω ) \ [ β ( · ) , γ ( · ) ] .
The study of regional enlarged observability for the Riemann–Liouville time fractional order diffusion system amounts to solving the following problem.
Problem 1.
Given the system (1) together with the output (2) in ω at time t [ 0 , T ] , is it possible to reconstruct y 0 1 between two prescribed functions β ( · ) and γ ( · ) in ω?
Before proving our first result, we need two important definitions.
Definition 2.
The system (1) together with the output (2) is said to be exactly [ β ( · ) , γ ( · ) ] –observable in ω if
I m ( χ ω K α * ) [ β ( · ) , γ ( · ) ] .
Definition 3.
The sensor ( D , f ) is said to be exactly [ β ( · ) , γ ( · ) ] –strategic in ω if the observed system is exactly [ β ( · ) , γ ( · ) ] –observable in ω.
Remark 1.
If α = 1 , then system (1) is reduced to the normal diffusion process recently considered in [53]. The results of [53] are a particular case of our results.
Remark 2.
If the system (1) together with the output (2) is exactly [ β ( · ) , γ ( · ) ] –observable in ω 1 , then it is exactly [ β ( · ) , γ ( · ) ] –observable in any subregion ω 2 ω 1 .
Theorem 1.
The following two statements are equivalent:
1. 
The system (1) together with the output (2) is exactly [ β ( · ) , γ ( · ) ] –observable in ω.
2. 
K e r ( K α χ ω * ) [ β ( · ) , γ ( · ) ] = { 0 } .
Proof. 
We begin by proving that statement 1 implies 2. For that we show that
I m ( χ ω K α * ) [ β ( · ) , γ ( · ) ] K e r ( K α χ ω * ) [ β ( · ) , γ ( · ) ] = { 0 } .
Suppose that
K e r ( K α χ ω * ) [ β ( · ) , γ ( · ) ] { 0 } .
Let us consider y K e r ( K α χ ω * ) [ β ( · ) , γ ( · ) ] such that y 0 . Then, y K e r ( K α χ ω * ) and y [ β ( · ) , γ ( · ) ] . We have K e r ( K α χ ω * ) = I m ( χ ω K α * ) , so that y I m ( χ ω K α * ) , y 0 . Therefore, y I m ( χ ω K α * ) , and
K e r ( K α χ ω * ) [ β ( · ) , γ ( · ) ] L 2 ( ω ) \ I m ( χ ω K α * ) , I m ( χ ω K α * ) L 2 ( ω ) \ K e r ( K α χ ω * ) L 2 ( ω ) \ [ β ( · ) , γ ( · ) ] .
We have
I m ( χ ω K α * ) L 2 ( ω ) \ K e r ( K α χ ω * ) .
Accordingly,
I m ( χ ω K α * ) K e r ( K α χ ω * ) =
and
I m ( χ ω K α * ) I m ( χ ω K α * ) = ,
which is absurd. Since
I m ( χ ω K α * ) L 2 ( ω ) \ [ β ( · ) , γ ( · ) ] ,
it follows that
I m ( χ ω K α * ) [ β ( · ) , γ ( · ) ] = ,
which is also absurd. Consequently,
K e r ( K α χ ω * ) [ β ( · ) , γ ( · ) ] = { 0 } .
We now prove the reverse implicationtatement 2 implies 1. For that we show that
K e r ( K α χ ω * ) [ β ( · ) , γ ( · ) ] = { 0 } I m ( χ ω K α * ) [ β ( · ) , γ ( · ) ] .
Suppose that
K e r ( K α χ ω * ) [ β ( · ) , γ ( · ) ] = { 0 } .
Let us consider
y K e r ( K α χ ω * ) [ β ( · ) , γ ( · ) ] .
Then, y K e r ( K α χ ω * ) and y [ β ( · ) , γ ( · ) ] such that y = 0 . We have
K e r ( K α χ ω * ) = I m ( χ ω K α * ) ,
so y I m ( χ ω K α * ) such that y = 0 . Hence,
y I m ( χ ω K α * ) and y [ β ( · ) , γ ( · ) ]
and
I m ( χ ω K α * ) [ β ( · ) , γ ( · ) ] ,
which shows that (1) and (2) is exactly [ β ( · ) , γ ( · ) ] –observable in ω . □

5. The HUM Approach

The purpose of this section is to present an approach that allows us to reconstruct the initial state of the system (1) between two prescribed functions β ( · ) and γ ( · ) in ω . Our approach constitutes an extension of the Hilbert Uniqueness Method (HUM) developed by Lions [43,44]. In what follows, G is defined by
G = g L 2 ( Ω ) | g = 0 in L 2 ( Ω ) \ [ β ( · ) , γ ( · ) ] .

5.1. Pointwise Sensors

Let us consider system (1) observed by a pointwise sensor ( b , δ b ) , where b Ω ¯ is the sensor location and δ is the Dirac mass concentrated in b. For details on pointwise sensors we refer the reader to [33]. Here the output function is given by
z ( t ) = φ ( b , T t ) , t [ 0 , T ] .
For φ 0 G , we consider the following system:
0 D t α φ ( x , t ) = A φ ( x , t ) in Q T , φ ( ξ , t ) = 0 on Σ T , lim t 0 + 0 I t 1 α φ ( x , t ) = φ 0 ( x ) in Ω .
Without loss of generality, we denote φ ( x , t ) : = φ ( t ) . System (5) admits a unique solution φ L 2 ( 0 , T ; H 0 1 ( Ω ) ) C ( Ω × [ 0 , T ] ) given by φ ( t ) = H α ( t ) φ 0 . We consider a semi-norm on G defined by
φ 0 φ 0 G 2 = 0 T C φ ( T t ) 2 d t .
The following result holds.
Lemma 3.
If the system (1) together with the output (4) is exactly [ β ( · ) , γ ( · ) ] –observable in ω, then (6) defines a norm on G .
Proof. 
Consider φ 0 G . Then,
φ 0 G C φ ( T t ) = 0 for all t [ 0 , T ] .
We have
φ 0 L 2 ( Ω ) χ ω φ 0 L 2 ( ω )
or
K α ( t ) χ ω * χ ω φ 0 = C H α ( t ) χ ω * χ ω φ 0 = 0 .
Hence,
χ ω φ 0 K e r ( K α χ ω * ) .
For χ ω φ 0 [ β ( · ) , γ ( · ) ] , one has χ ω φ 0 K e r ( K α χ ω * ) [ β ( · ) , γ ( · ) ] and, because the system is exactly [ β ( · ) , γ ( · ) ] –observable in ω , χ ω φ 0 = 0 . Consequently, φ 0 = 0 and (6) is a norm. □
Consider the system
Q t D T α Ψ ( x , t ) = A * Q Ψ ( x , t ) + C * C Q φ ( x , t ) in Q T , Ψ ( ξ , t ) = 0 on Σ T , lim t T Q t I T 1 α Ψ ( x , t ) = 0 in Ω ,
controlled by the solution of (5). For φ 0 G , we define the operator Λ : G G * by
Λ φ 0 = P ( Ψ ( 0 ) ) ,
where P = χ ω * χ ω and Ψ ( 0 ) = Ψ ( x , 0 ) . Let us now consider the system
Q t D T α Θ ( x , t ) = A * Q Θ ( x , t ) + C * Q z ( t ) in Q T , Θ ( ξ , t ) = 0 on Σ T , lim t T Q t I T 1 α Θ ( x , t ) = 0 in Ω .
If φ 0 is chosen such that Θ ( 0 ) = Ψ ( 0 ) in ω , then system (8) can be seen as the adjoint of system (1) and our problem of enlarged observability is reduced to solve the equation
Λ φ 0 = P ( Θ ( 0 ) ) .
Theorem 2.
If system (1) together with the output (4) is exactly [ β ( · ) , γ ( · ) ] –observable in ω, then Equation (9) admits a unique solution φ 0 G , which coincides with the initial state y 0 1 observed between β ( · ) and γ ( · ) in ω. Moreover, y 0 1 = χ ω φ 0 .
Proof. 
By Lemma 1, we see that · G is a norm of the space G provided that the system (1) together with the output (4) is exactly [ β ( · ) , γ ( · ) ] –observable in ω . Now, we show that (9) admits a unique solution in G . For any φ 0 G , Equation (9) admits a unique solution if Λ is an isomorphism. Then,
Λ φ 0 , φ 0 L 2 ( Ω ) = P Ψ ( 0 ) , φ 0 L 2 ( Ω ) = χ ω * χ ω Ψ ( 0 ) , φ 0 L 2 ( Ω ) = Ψ ( 0 ) , φ 0 L 2 ( ω )
or Ψ ( t ) is the solution of system (7), that is,
Ψ ( t ) = H α * ( T t ) Ψ ( T ) + t T H α * ( T τ ) C * C φ ( T τ ) d τ
and
Ψ ( 0 ) = 0 T H α * ( T τ ) C * C φ ( T τ ) d τ ,
where
H α * ( t ) = α t α 1 0 θ ξ α ( θ ) S * ( t α θ ) d θ
with ( S * ( t ) ) t 0 the strongly continuous semi-group generated by A * . We obtain that
Λ φ 0 , φ 0 L 2 ( Ω ) = Ψ ( 0 ) , φ 0 = 0 T H α * ( T τ ) C * C φ ( T τ ) d τ , φ 0 = 0 T C φ ( T τ ) , C H α ( T τ ) φ 0 d τ = 0 T C φ ( T τ ) 2 d τ = φ 0 G 2 ,
concluding that Λ is an isomorphism. Consequently, Equation (9) has a unique solution that is also the initial state to be estimated between β ( · ) and γ ( · ) in the subregion ω given by
y 0 1 = χ ω φ 0 .
The proof is complete. □

5.2. Zone Sensors

Let us come back to system (1) and suppose that the measurements are given by an internal zone sensor defined by ( D , f ) with D Ω and f L 2 ( D ) . The system is augmented with the output function
z ( t ) = D y ( x , T t ) f ( x ) d x .
In this case, we consider (5), G given by (3), and we define a semi-norm on G by
φ 0 G 2 = 0 T φ ( T t ) , f L 2 ( D ) 2 d t
with
Q t D T α Ψ ( x , t ) = A * Q Ψ ( x , t ) + Q φ ( t ) , f L 2 ( D ) χ D f ( x ) in Q T , Ψ ( ξ , t ) = 0 on Σ T , lim t T Q t I T 1 α Ψ ( x , t ) = 0 in Ω .
We introduce the operator
Λ : G G * φ 0 Λ φ 0 = P ( Ψ ( 0 ) ) ,
where P = χ ω * χ ω and Ψ ( 0 ) = Ψ ( x , 0 ) . Let us consider the system
Q t D T α Θ ( x , t ) = A * Q Θ ( x , t ) + Q z ( t ) , f L 2 ( D ) χ D f ( x ) in Q T , Θ ( ξ , t ) = 0 on Σ T , lim t T Q t I T 1 α Θ ( x , t ) = 0 in Ω .
If φ 0 is chosen such that Θ ( 0 ) = Ψ ( 0 ) in ω , then (13) can be seen as the adjoint of system (1) and our problem of enlarged observability consists to solve the equation
Λ φ 0 = P ( Θ ( 0 ) ) .
Theorem 3.
If system (1) together with the output (10) is exactly [ β ( · ) , γ ( · ) ] –observable in ω, then Equation (14) has a unique solution φ 0 G , which coincides with the initial state y 0 1 observed between β ( · ) and γ ( · ) in ω.
Proof. 
The proof is similar to the proof of Theorem 2. □

6. Example

Let us consider the following one-dimensional time fractional differential system of order α ( 0 , 1 ] in Ω 1 = [ 0 , 1 ] , excited by a pointwise sensor:
0 D t α y ( x , t ) = 2 y ( x , t ) x 2 in [ 0 , 1 ] × [ 0 , T ] , y ( 0 , t ) = y ( 1 , t ) = 0 in [ 0 , T ] , lim t 0 + 0 I t 1 α y ( x , t ) = y 0 ( x ) in [ 0 , 1 ] ,
augmented with the output function
z ( t ) = C y ( x , t ) = y ( b , t ) ,
where b = 1 2 Ω 1 . The operator A = 2 x 2 has a complete set of eigenfunctions ( φ k ) in L 2 ( Ω 1 ) associated with the eigenvalues ( λ k ) , given by
φ k ( x ) = 2 sin ( k π x ) and λ k = k 2 π 2
with
S ( t ) y ( x ) = k = 1 e λ k t y , φ k L 2 ( Ω 1 ) φ k ( x ) .
Then,
H α ( t ) y ( x ) = k = 1 t α 1 E α , α ( λ k t α ) y , φ k L 2 ( Ω 1 ) φ k ( x ) ,
where E α , α ( z ) : = k = 0 z k Γ ( α k + α ) , Re α > 0 , α , z C , is the generalized Mittag–Leffler function in two parameters (see, e.g., [54]). Let y 0 ( x ) = sin ( 2 π x ) be the initial state to be observed. Then, for ω 1 = 1 6 , 1 3 , the following result holds.
Proposition 1.
There is a state for which the system (15) and (16) is not weakly observable in Ω 1 but it is exactly [ β 1 ( · ) , γ 1 ( · ) ] –observable in ω 1 .
Proof. 
To show that system (15) and (16) is not weakly observable in Ω 1 , it is sufficient to verify that y 0 K e r ( K α ) . We have
K α y 0 ( x ) = i = 1 t α 1 E α , α ( λ i t α ) y 0 , φ i L 2 ( Ω 1 ) φ i ( b ) = 2 i = 1 t α 1 E α , α ( λ i t α ) sin i π 2 0 1 sin ( 2 π x ) sin ( i π x ) d x = 0 .
Hence, K α y 0 ( x ) = 0 . Consequently, the state y 0 is not weakly observable in Ω 1 . On the other hand, one has
K α χ ω 1 * χ ω 1 y 0 ( x ) = i = 1 t α 1 E α , α ( λ i t α ) χ ω 1 * χ ω 1 y 0 , φ i L 2 ( Ω 1 ) φ i ( b ) = i = 1 t α 1 E α , α ( λ i t α ) y 0 , φ i L 2 ( ω 1 ) φ i ( b ) = 2 t α 1 E α , α ( π 2 t α ) 1 6 1 3 sin ( 2 π x ) sin ( π x ) d x = ( 3 3 1 ) t α 1 E α , α ( π 2 t α ) 6 π 0 ,
which means that the state y 0 is weakly observable in ω 1 . Moreover, for
β 1 ( x ) = y | ω 1 0 ( x ) 1 < y | ω 1 0 ( x )
and
γ 1 ( x ) = y | ω 1 0 ( x ) + 1 > y | ω 1 0 ( x ) , x ω 1 ,
we have χ ω 1 y 0 ( x ) [ β 1 ( · ) , γ 1 ( · ) ] and system (15) and (16) is exactly [ β 1 ( · ) , γ 1 ( · ) ] -observable in ω 1 . The proof is complete. □
Let G 1 be the set defined by
G 1 = g L 2 ( Ω 1 ) | g = 0 in L 2 ( Ω 1 ) \ [ β 1 ( · ) , γ 1 ( · ) ] .
From Lemma 3, we see that
φ 0 φ 0 G 1 2 = 0 T C H α ( T t ) φ 0 2 d t
defines a norm on G 1 . Consider the system
Q t D T α Θ ( x , t ) = A * Q Θ ( x , t ) + δ ( x b ) z ( T t ) in Ω 1 × [ 0 , T ] , Θ ( ξ , t ) = 0 on Ω 1 × [ 0 , T ] , lim t T Q t I T 1 α Θ ( x , t ) = 0 in Ω 1 .
It follows from Theorem 2 that the equation Λ : φ 0 P ( Θ ( 0 ) ) has a unique solution in G 1 , which is also the initial state y 0 observed between β 1 ( · ) and γ 1 ( · ) in the subregion ω 1 .

7. Conclusions

In this paper we have investigated the notion of regional enlarged observability for a time fractional diffusion system with Riemann–Liouville fraction derivative of order α ( 0 , 1 ] . We developed an approach that leads to the reconstruction of the initial state between two prescribed functions only in an internal subregion ω of the whole domain Ω . We claim that the results here obtained can be useful to real problems of engineering.
As future work, we plan to study problems of regional boundary enlarged observability and regional gradient enlarged observability of fractional order distributed parameter systems, and provide illustrative numerical examples.

Author Contributions

H.Z., A.B. and D.F.M.T. contributed equally to this work.

Funding

This research was funded by Académie Hassan II des Sciences et Techniques, Morocco, grant number 630/2016 and by Fundação para a Ciência e a Tecnologia, Portugal, grant number UID/MAT/04106/2013 (CIDMA).

Acknowledgments

This research was initiated during a one-month visit of Zouiten to the Department of Mathematics of the University of Aveiro (DMat-UA), November and December 2017, followed by a ten days visit to DMat-UA in February 2018. The hospitality of the host institution and the support of Moulay Ismail University, Morocco, and R&D unit CIDMA, Portugal, are here gratefully acknowledged. The authors would like also to thank two anonymous reviewers for their critical remarks and precious suggestions, which helped them to improve the quality and clarity of the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Leibniz, G.W. Mathematische Schriften. Bd. V: Die mathematischen Abhandlungen; Herausgegeben von C. I. Gerhardt, Georg Olms Verlagsbuchhandlung: Hildesheim, Germany, 1962. [Google Scholar]
  2. Samko, S.G.; Kilbas, A.A.; Marichev, O.I. Fractional Integrals and Derivatives; Gordon and Breach Science Publishers: Yverdon, Switzerland, 1993. [Google Scholar]
  3. Machado, J.A.T.; Kiryakova, V. The chronicles of fractional calculus. Fract. Calc. Appl. Anal. 2017, 20, 307–336. [Google Scholar] [CrossRef]
  4. Heymans, N.; Podlubny, I. Physical interpretation of initial conditions for fractional differential equations with Riemann-Liouville fractional derivatives. Rheol. Acta 2006, 45, 765–771. [Google Scholar] [CrossRef]
  5. Povstenko, Y. Linear Fractional Diffusion-wave Equation for Scientists and Engineers; Springer: Cham, Switzerland, 2015. [Google Scholar] [CrossRef]
  6. Tenreiro Machado, J.A.; Mainardi, F.; Kiryakova, V.; Atanacković, T. Fractional calculus: D’où venons-nous? Que sommes-nous? Où allons-nous? Fract. Calc. Appl. Anal. 2016, 19, 1074–1104. [Google Scholar] [CrossRef]
  7. Dzieliński, A.; Sierociuk, D. Fractional order model of beam heating process and its experimental verification. In New Trends in Nanotechnology and Fractional Calculus Applications; Springer: New York, NY, USA, 2010; pp. 287–294. [Google Scholar] [CrossRef]
  8. Sierociuk, D.; Dzieliński, A.; Sarwas, G.; Petras, I.; Podlubny, I.; Skovranek, T. Modelling heat transfer in heterogeneous media using fractional calculus. Philos. Trans. R. Soc. Lond. Ser. A Math. Phys. Eng. Sci. 2013, 371, 20120146. [Google Scholar] [CrossRef] [PubMed]
  9. Dzieliński, A.; Sarwas, G.; Sierociuk, D. Time domain validation of ultracapacitor fractional order model. In Proceedings of the 49th IEEE Conference on Decision and Control (CDC), Atlanta, GA, USA, 15–17 December 2010; pp. 3730–3735. [Google Scholar] [CrossRef]
  10. Dzieliński, A.; Sierociuk, D. Ultracapacitor modelling and control using discrete fractional order state-space model. Acta Montan. Slovaca 2008, 13, 136–145. [Google Scholar]
  11. Dzieliński, A.; Sierociuk, D.; Sarwas, G. Some applications of fractional order calculus. Bull. Pol. Acad. Sci. Tech. Sci. 2010, 58, 583–592. [Google Scholar] [CrossRef]
  12. Hilfer, R. (Ed.) Applications of Fractional Calculus in Physics; World Scientific Publishing Co., Inc.: River Edge, NJ, USA, 2000. [Google Scholar] [CrossRef]
  13. Magin, R.L. Fractional Calculus in Bioengineering; Begell House Inc.: Danbury, CT, USA, 2006. [Google Scholar]
  14. Tavares, D.; Almeida, R.; Torres, D.F.M. Fractional Herglotz variational problems of variable order. Discrete Contin. Dyn. Syst. Ser. S 2018, 11, 143–154. [Google Scholar] [CrossRef]
  15. Debbouche, A.; Torres, D.F.M. Sobolev type fractional dynamic equations and optimal multi-integral controls with fractional nonlocal conditions. Fract. Calc. Appl. Anal. 2015, 18, 95–121. [Google Scholar] [CrossRef]
  16. Mozyrska, D.; Torres, D.F.M. Minimal modified energy control for fractional linear control systems with the Caputo derivative. Carpathian J. Math. 2010, 26, 210–221. [Google Scholar]
  17. Xue, R. Observability for fractional diffusion equations by interior control. Fract. Calc. Appl. Anal. 2017, 20, 537–552. [Google Scholar] [CrossRef]
  18. Axtell, M.; Bise, M.E. Fractional calculus application in control systems. In Proceedings of the IEEE Conference on Aerospace and Electronics, Dayton, OH, USA, 21–25 May 1990; Volume 2, pp. 563–566. [Google Scholar] [CrossRef]
  19. Baleanu, D.; Golmankhaneh, A.K.; Golmankhaneh, A.K. On electromagnetic field in fractional space. Nonlinear Anal. Real World Appl. 2010, 11, 288–292. [Google Scholar] [CrossRef]
  20. Kilbas, A.A.; Srivastava, H.M.; Trujillo, J.J. Theory and Applications of Fractional Differential Equations; North-Holland Mathematics Studies; Elsevier Science B.V.: Amsterdam, The Netherlands, 2006; Volume 204. [Google Scholar]
  21. Liu, Z.; Han, J. Integral boundary value problems for fractional order integro-differential equations. Dyn. Syst. Appl. 2012, 21, 535–547. [Google Scholar]
  22. Liu, Z.; Sun, J. Nonlinear boundary value problems of fractional functional integro-differential equations. Comput. Math. Appl. 2012, 64, 3228–3234. [Google Scholar] [CrossRef]
  23. Oustaloup, A. From fractality to non integer derivation: A fundamental idea for a new process control strategy. In Analysis and Optimization of Systems; Bensoussan, A., Lions, J.L., Eds.; Springer: Berlin/Heidelberg, Germany, 1988; pp. 53–64. [Google Scholar] [CrossRef]
  24. Podlubny, I. Fractional differential equations. In Mathematics in Science and Engineering; Academic Press, Inc.: San Diego, CA, USA, 1999. [Google Scholar]
  25. Li, C.; Zeng, F. Numerical Methods for Fractional Calculus; Chapman & Hall/CRC Numerical Analysis and Scientific Computing, CRC Press: Boca Raton, FL, USA, 2015. [Google Scholar]
  26. Li, C.; Yi, Q.; Kurths, J. Fractional Convection. J. Comput. Nonlinear Dyn. 2017, 13, 011004. [Google Scholar] [CrossRef]
  27. Li, C.; Chen, A. Numerical methods for fractional partial differential equations. Int. J. Comput. Math. 2018, 95, 1048–1099. [Google Scholar] [CrossRef]
  28. Abbas, S.; Benchohra, M.; N’Guérékata, G.M. Topics in fractional differential equations. In Developments in Mathematics; Springer: New York, NY, USA, 2012; Volume 27. [Google Scholar] [CrossRef]
  29. Agarwal, R.P.; Baleanu, D.; Nieto, J.J.; Torres, D.F.M.; Zhou, Y. A survey on fuzzy fractional differential and optimal control nonlocal evolution equations. J. Comput. Appl. Math. 2018, 339, 3–29. [Google Scholar] [CrossRef] [Green Version]
  30. Zhang, X.; Zhong, Q. Triple positive solutions for nonlocal fractional differential equations with singularities both on time and space variables. Appl. Math. Lett. 2018, 80, 12–19. [Google Scholar] [CrossRef]
  31. Amouroux, M.; El Jaï, A.; Zerrik, E. Regional observability of distributed systems. Int. J. Syst. Sci. 1994, 25, 301–313. [Google Scholar] [CrossRef]
  32. Dolecki, S.; Russell, D.L. A general theory of observation and control. SIAM J. Control Optim. 1977, 15, 185–220. [Google Scholar] [CrossRef]
  33. El Jaï, A.; Pritchard, A.J. Capteurs et actionneurs dans l’analyse des systèmes distribués. In Recherches en Mathématiques Appliquées [Research in Applied Mathematics]; Masson: Paris, France, 1986; Volume 3, p. 204. [Google Scholar]
  34. El Jaï, A.; Simon, M.C.; Zerrik, E. Regional observability and sensor structures. Sens. Actuators A Phys. 1993, 39, 95–102. [Google Scholar] [CrossRef]
  35. Zerrik, E.H.; Bourray, H. Gradient observability for diffusion systems. Int. J. Appl. Math. Comput. Sci. 2003, 13, 139–150. [Google Scholar]
  36. Ge, F.; Chen, Y.; Kou, C. Regional gradient controllability of sub-diffusion processes. J. Math. Anal. Appl. 2016, 440, 865–884. [Google Scholar] [CrossRef]
  37. Debbouche, A.; Torres, D.F.M. Approximate controllability of fractional nonlocal delay semilinear systems in Hilbert spaces. Int. J. Control 2013, 86, 1577–1585. [Google Scholar] [CrossRef] [Green Version]
  38. Debbouche, A.; Torres, D.F.M. Approximate controllability of fractional delay dynamic inclusions with nonlocal control conditions. Appl. Math. Comput. 2014, 243, 161–175. [Google Scholar] [CrossRef] [Green Version]
  39. Karite, T.; Boutoulout, A.; Torres, D.F.M. Enlarged controllability of Riemann-Liouville fractional differential equations. J. Comput. Nonlinear Dyn. 2018, 13, 090907. [Google Scholar] [CrossRef]
  40. Ge, F.; Chen, Y.; Kou, C. On the regional gradient observability of time fractional diffusion processes. Autom. J. IFAC 2016, 74, 1–9. [Google Scholar] [CrossRef] [Green Version]
  41. Ge, F.; Chen, Y.; Kou, C. Regional Analysis of Time-Fractional Diffusion Processes; Springer: Cham, Switzerland, 2018. [Google Scholar] [CrossRef]
  42. Zheng, G.; Ali, M.M. Observability estimate for the fractional order parabolic equations on measurable sets. Abstr. Appl. Anal. 2014, 361904. [Google Scholar] [CrossRef]
  43. Lions, J.L. Contrôlabilité exacte, perturbations et stabilisation de systèmes distribués. Tome 1. In Recherches en Mathématiques Appliquées [Research in Applied Mathematics]; Masson: Paris, France, 1988. [Google Scholar]
  44. Lions, J.L. Sur la contrôlabilité exacte élargie. In Partial Differential Equations and the Calculus of Variations; Birkhäuser Boston: Boston, MA, USA, 1989; Volume 2, pp. 703–727. [Google Scholar]
  45. Almeida, R.; Pooseh, S.; Torres, D.F.M. Computational Methods in the Fractional Calculus of Variations; Imperial College Press: London, UK, 2015. [Google Scholar] [CrossRef]
  46. Liu, Z.; Li, X. Approximate controllability of fractional evolution systems with Riemann-Liouville fractional derivatives. SIAM J. Control Optim. 2015, 53, 1920–1933. [Google Scholar] [CrossRef]
  47. Mainardi, F.; Paradisi, P.; Gorenflo, R. Probability distributions generated by fractional diffusion equations. arXiv, 2007; arXiv:0704.0320. [Google Scholar]
  48. Klimek, M. On Solutions of Linear Fractional Differential Equations of a Variational Type; Czestochowa University of Technology: Czestochowa, Poland, 2009. [Google Scholar]
  49. Pritchard, A.J.; Wirth, A. Unbounded control and observation systems and their duality. SIAM J. Control Optim. 1978, 16, 535–545. [Google Scholar] [CrossRef]
  50. Salamon, D. Infinite-dimensional linear systems with unbounded control and observation: A functional analytic approach. Trans. Am. Math. Soc. 1987, 300, 383–431. [Google Scholar] [CrossRef]
  51. Weiss, G. Admissible observation operators for linear semigroups. Israel J. Math. 1989, 65, 17–43. [Google Scholar] [CrossRef]
  52. Curtain, R.F.; Zwart, H. An Introduction to Infinite-Dimensional Linear Systems Theory; Texts in Applied Mathematics; Springer: New York, NY, USA, 1995; Volume 21. [Google Scholar] [CrossRef]
  53. Zouiten, H.; Boutoulout, A.; El Alaoui, F.Z. On the regional enlarged observability for linear parabolic systems. J. Math. Syst. Sci. 2017, 7, 79–87. [Google Scholar] [CrossRef]
  54. Gorenflo, R.; Kilbas, A.A.; Mainardi, F.; Rogosin, S.V. Mittag-Leffler Functions, Related Topics and Applications; Springer Monographs in Mathematics; Springer: Heidelberg, Germany, 2014. [Google Scholar] [CrossRef]

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Zouiten, H.; Boutoulout, A.; Torres, D.F.M. Regional Enlarged Observability of Fractional Differential Equations with Riemann—Liouville Time Derivatives. Axioms 2018, 7, 92. https://doi.org/10.3390/axioms7040092

AMA Style

Zouiten H, Boutoulout A, Torres DFM. Regional Enlarged Observability of Fractional Differential Equations with Riemann—Liouville Time Derivatives. Axioms. 2018; 7(4):92. https://doi.org/10.3390/axioms7040092

Chicago/Turabian Style

Zouiten, Hayat, Ali Boutoulout, and Delfim F. M. Torres. 2018. "Regional Enlarged Observability of Fractional Differential Equations with Riemann—Liouville Time Derivatives" Axioms 7, no. 4: 92. https://doi.org/10.3390/axioms7040092

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