Next Article in Journal
On Ikeda-Based Memristor Map with Commensurate and Incommensurate Fractional Orders: Bifurcation, Chaos, and Entropy
Previous Article in Journal
On Some New AB-Fractional Inclusion Relations
Previous Article in Special Issue
Adaptive Control Design for Euler–Lagrange Systems Using Fixed-Time Fractional Integral Sliding Mode Scheme
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Controllability Analysis of Impulsive Multi-Term Fractional-Order Stochastic Systems Involving State-Dependent Delay

1
Department of Mathematics, PSGR Krishnammal College for Women, Coimbatore 641004, India
2
Department of Applied Mathematics, Kongju National University, Gongju-si 32588, Chungcheongnam-do, Republic of Korea
*
Authors to whom correspondence should be addressed.
Fractal Fract. 2023, 7(10), 727; https://doi.org/10.3390/fractalfract7100727
Submission received: 15 August 2023 / Revised: 24 September 2023 / Accepted: 28 September 2023 / Published: 30 September 2023

Abstract

:
This study deals with the controllability of multi-term fractional-order stochastic systems with impulsive effects and state-dependent delay that exhibit damping behavior. Based on fractional calculus theory, the Caputo fractional derivative is utilized to analyze the controllability of fractional-order systems. Mittag–Leffler functions and Laplace transform are used to derive the solution set of the problem. Sufficient conditions for the controllability of nonlinear systems are achieved using fixed-point techniques and stochastic theory. Finally, the results stated in the paper are validated using examples.

1. Introduction

Fractional calculus is a more precise technique of describing the behavior of complex systems with non-integer-order dynamics. Many real-world systems do not follow traditional integer-order differential equations exactly. Fractional calculus is used to model and control non-integer-order viscoelastic materials and systems with damping and rigidity. For example, chemical processes and reactors with non-integer-order reaction kinetics can be modeled using fractional calculus. Damping control in car suspension systems and vibration control in structures are two examples of such applications. Fractional-order differential equations (FDEs) are a class of non-integer-order differential equations, which have been addressed for various physical processes. Comparable to ordinary derivatives, fractional derivatives provide a more precise description of the rate of change of a function or process over time. Several authors have explored the application of FDEs in the last few years [1,2,3,4,5]. Fractional derivatives capture memory or hereditary effects that are essential to modeling systems with long-term dependencies, delays, or non-local interactions. Applications, like conservation laws about energy forms in fractal space, have been revealed by fractal generalized variational structures using the semi-inverse method, as discussed in [6], and a new fractional pulse narrowing transmission line model in electrical and electronic engineering is discussed in [7]. A new technique in tempered fractional calculus in both Riemann–Liouville and Caputo sense with applications in physical sciences is studied in [8]. The Caputo fractional derivative naturally incorporates initial conditions, which is suitable for solving fractional differential equations with initial values, and it is well suited for modeling real-world phenomena with memory effects. Multi-term fractional differential equations with initial values are mainly used to model problems in engineering and other areas of applications. In particular, multi-term fractional differential equations have been used to model many types of visco-elastic damping problems.
Control theory emphasizes the importance of controllability as it allows for the manipulation of a system’s behavior. Several branches of research, including control engineering and dynamical system controllability theory, have been used. Approaches to controllability analysis of fractional-ordered systems via fixed point techniques have been investigated by [9], and researchers have focused on various delays on fractional-order systems for controllability criteria with possible applications in [10,11,12,13,14,15,16,17]. The formation of new control systems that increase system performance and provide a powerful framework for describing and understanding complex dynamic systems with predictive capabilities by means of useful models has been studied in [18,19,20]. A process that has some measure of randomness or uncertainty is said to be stochastic. Stochastic processes are widely used in techniques where arbitrary circumstances, such as changes in stock prices, meteorological patterns, or the transmission of diseases, have an impact on the behavior of the system. Study results on stochastic theory for controllability are given in [21,22,23]. Impulsive effects can significantly alter the behavior of a system. They can introduce sudden changes, discontinuities, or jumps in a system’s variables, leading to deviations from the expected or predicted behavior. This alteration can affect stability, convergence, and overall system dynamics. The impulsive effect can be intentionally applied to control or manipulate a system. By strategically introducing impulses, it is possible to drive the system toward desired states, induce specific behaviors, or stabilize unstable dynamics. The monograph created by Bainov and Simeonov in [24] contains the fundamental understanding of impulsive differential equations. Controllability results for an impulsive differential system with state-dependent delay (SDD) and distributed delays in control have been analyzed in [25,26]. Fractional models with delay are very useful for analyzing population dynamics, neural networking, and physiology as they allow us to understand how a system’s behavior changes over time delays.
Damping is a phenomenon in which energy is dissipated to minimize the amplitude of vibrations in a system. As reported in [27,28], controllability criteria with damping phenomena have been explored. In recent years, this area has seen significant advances in solving both linear and nonlinear systems with certain delays in the analysis of controllability results. Step techniques were used in [29] to explore the necessary and sufficient criteria for examining controllability analysis for state delay and impulses with damping. The damping behavior of a system with certain delays has been discussed in [30,31,32]. Studies of interest regarding non-integer-order-type systems with SDD have been enormous in recent years. According to [33], the theory of existence yields a fractional system with resolvent operators and SDD. The existence theory of integro-differential and SDD in fractional order is studied in [34]. Moreover, second-order systems for controllability results with SDD have been established in [35,36]. A non-integer-order system with SDD combined with integro-differential terms is investigated in [37]. Based on the above analysis, it is valuable to study the controllability concept for multi-term fractional-order stochastic systems with impulsive effects and SDD with damping behavior.
The structure of this paper is as follows: In Section 2, a review of basic definitions and lemmas is provided. In Section 3, the controllability result is derived for a damped impulsive stochastic system with SDD by employing fixed point analysis. In Section 4, the illustrated result is demonstrated.

2. Problem Formulation

Consider an impulsive stochastic multi-term fractional system with SDD involving damping behavior
0 C D t ζ y ( t ) B 0 C D t η y ( t ) = C u ( t ) + h ˜ ( t , y ϱ ^ ( t , y t ) ) + σ ˜ ( t , y ϱ ^ ( t , y t ) ) d w ( t ) d t , t M = [ 0 , T ] ,
y ( 0 ) = y 0 , y ( 0 ) = y 1 ,
Δ y ( t ) = J p ( y ( t p ) ) , Δ y ( t ) = J ˜ p ( y ( t p ) ) , t = t p , p = 1 , 2 , , q ,
where 0 C D t ζ and 0 C D t η denote fractional derivatives of orders η 0 , 1 and ζ 1 , 2 in a Caputo sense. H denotes Hilbert space; y ( · ) R n is a state variable that takes values in H with the inner product ( · , · ) and the norm · ; and B R n × n and C R n × m are known constant matrices. u L 2 ( [ 0 , T ] , U ) is a control input for U H , and C is a bounded linear operator. In abstract space, B , y s ( Θ ) = y ( s + Θ ) denotes the function y s : ( , Θ ] H , and the function ϱ ^ : M × B ( , T ] is continuous.
PC ( M , H ) is piecewise continuous for y : M H , such that y ( t p ) = y ( t p ) and y ( t p + ) exist for p = 1 , 2 , , q . Except for some t p , the norm y PC = s u p t M | y ( t ) | is continuous every where. Δ y ( t p ) = y ( t p + ) y ( t p ) , where y ( t p + ) = lim δ 0 + y ( t p + δ ) and y ( t p ) = lim δ 0 y ( t p + δ ) represent the upper and lower bounds of y ( t ) . Similarly, Δ y ( t p ) can be defined. Let ( Ω , F , P ) be the complete probability space with filtration, { F t } t 0 , generated by an m-dimensional Wiener process with probability measure P on Ω . R m is the m-dimensional Euclidean space. The Wiener process, { W ( t ) } t > 0 , exists in complete probability space ( Ω , F , P ) . y ( t ) is a measurable and F -adapted H -valued process with the norm y 2 = sup { E y ( t ) 2 , t M } , such that y ( · ) PC ( M , L 2 ( Ω , F , P ; H ) ) ; here, E ( · ) symbolizes the expectation with respect to measure P . The appropriate functions h ˜ , σ ˜ , J p , J ˜ p are continuous, as specified later.
The filtration, { F t } t 0 , on the H -valued F measurable function is defined for the stochastic process, y ( t ) : Ω H , which is the collection of random variables in ( Ω , F , P ) . The representation F T = F t , where F t = σ ( W ( s ) : 0 s t ) , is σ -algebra generated by W. The Q-Wiener process is denoted as W ( t ) = p = 1 λ p β p e p , t 0 for tr ( Q ) < , which satisfies Q e p = λ p e p . Here, { β p } p 1 is a sequence of Brownian motions, and e p p 1 is completely orthonormal. A Q-Hilbert–Schmidt operator, ϕ , is defined for ϕ Q 2 = tr ϕ Q ϕ * = p = 1 λ p ϕ e p 2 < , where ϕ Q 2 = ϕ , ϕ .
B , · B is the abstract space, and as reported in [38], a semi-norm linear space of F 0 -measurable function satisfies the fundamental axioms:
  • If the function y : ( , T ] H is continuous for every t [ 0 , T ) , such that y 0 B , then
    (i) y t B ;
    (ii) y ( t ) N 1 y t B ;
    (iii) y t B N 2 ( t ) y 0 B + N 3 ( t ) sup { y ( s ) : 0 s T } ;
    holds, where N 1 > 0 , N 2 , N 3 : [ 0 , ) [ 0 , ) is independent of y. Here, N 3 is continuous, and N 2 is locally bounded.
Definition 1.
The CFD of order ζ ( 0 p 1 ζ < p 1 + 1 ) for the function h ˜ : R + R is known as
0 C D t ζ h ˜ ( t ) = 1 Γ ( p 1 ζ + 1 ) 0 t h ˜ ( p 1 + 1 ) ( θ ) ( t θ ) ζ p 1 d θ .
The Laplace transform (LT) of the CFD is known as
L { 0 C D t ζ h ˜ ( t ) } ( s ) = s ζ H ˜ ( s ) k = 0 m 1 h ˜ ( k ) ( t ) s ζ 1 k .
Definition 2.
For z C , the M-L function of E ζ ( z )
E ζ ( z ) = j = 0 z j Γ ( ζ j + 1 ) , ζ > 0 ,
The two-parameter M-L function, E ζ , η ( z ) ,
E ζ , η ( z ) = j = 0 z j Γ ( ζ j + η ) ,
The LT of the M-L function, E ζ , η ( z ) ,
L { t η 1 E ζ , η ( ± a t ζ ) } ( s ) = s ζ η s ζ a .
For η = 1 ,
L { E ζ ( ± a t ζ ) } ( s ) = s ζ η s ζ a .
Lemma 1
([39]). For y 0 = φ ˜ and y ( · ) | M PC , such that y : ( , T ] H is a function, then
y s B Z T + J 0 φ ˜ φ ˜ B + N T sup { y ( Θ ˜ ) ; Θ ˜ [ 0 , max { 0 , s } ] } , s V ϱ ^ M .
Lemma 2
([40]). A convex, closed and nonempty subset of Banach space X is denoted by Z . Assuming F and D as the operators and the following:
(i)
For all x , y Z , F x + D y Z ;
(ii)
F is continuous and compact;
(iii)
D is a contraction mapping.
Then, r Z , such that r = F r + D r .
Definition 3.
The stochastic process, y M × Ω H , is known as the solution to (1)–(3) if the following are met:
(i)
y ( t ) F t -adapted measurable   t M ;
(ii)
y ( t ) H satisfying
y ( t ) = E ζ η ( B t ζ η ) y 0 B t ζ η E ζ η , ζ η + 1 ( B t ζ η ) y 0 + t E ζ η , 2 ( B t ζ η ) y 1 + p = 1 q E ζ η ( B ( T t p ) ζ η ) J p ( y ( t p ) ) p = 1 q B ( T t p ) ζ η E ζ η , ζ η + 1 × ( B ( T t p ) ζ η ) J p ( y ( t p ) ) + p = 1 q ( T t p ) E ζ η , 2 ( B ( T t p ) ζ η ) J ˜ p ( y ( t p ) ) + 0 t ( t s ) ζ 1 E ζ η , ζ ( B ( t s ) ζ η ) h ˜ ( s , y ϱ ^ ( s , y s ) ) d s + 0 t ( t s ) ζ 1 × E ζ η , ζ ( B ( t s ) ζ η ) σ ˜ ( s , y ϱ ^ ( s , y s ) ) d w ( s ) d s + 0 t ( t s ) ζ 1 E ζ η , ζ ( B ( t s ) ζ η ) C u ( s ) d s .

3. Main Result

In this part, we assume the following hypothesis to demonstrate the controllability result for the system (1)–(3).
Hypothesis 1.
Functions h ˜ : M × B H and σ ˜ : M × B H are continuous and   K h ˜ > 0 and K σ ˜ > 0 , such that
E h ˜ ( t , y 1 ) h ˜ ( t , y 2 ) K h ˜ y 1 y 2 B 2 , E σ ˜ ( t , y 1 ) σ ˜ ( t , y 2 ) K σ ˜ y 1 y 2 B 2 .
Hypothesis 2.
The continuous function, ν h ˜ : ( 0 , ] ( 0 , ] , and integrable function, α: M ( 0 , ] , exist such that
E h ˜ ( t , ψ ) α ( t ) ν h ˜ ( ψ B ) , lim inf ω ν h ˜ ( ω ) ω = μ ˜ .
Hypothesis 3.
The continuous function, ν σ ˜ : ( 0 , ] ( 0 , ] , and integrable function, α 1 : M ( 0 , ] , exist such that
E σ ˜ ( t , ψ ) α 1 ( t ) ν σ ˜ ( ψ B ) , lim inf ω ν σ ˜ ( ω ) ω = μ ˜ .
Hypothesis 4.
The maps J p , J ˜ p : B H are continuous and β p , γ p : [ 0 , ) ( 0 , ) , p = 1 , 2 , , q exist
E J p ( y ) 2 β p ( E y 2 ) , lim inf r β p ( r ) r = Υ p , E J ˜ p ( y ) 2 γ p ( E y 2 ) , lim inf r γ p ( r ) r = Υ ˜ p .
Hypothesis 5.
A bounded and continuous function J φ ˜ : V ( ϱ ^ ) ( 0 , ) is well defined in t φ ˜ t from V ( ϱ ^ ) to B , such that φ ˜ B J φ ˜ ( t ) φ ˜ B t V ( ϱ ^ ) , where V ( ϱ ^ ) = ϱ ^ ( s , φ ˜ ) M × B .
Hypothesis 6.
The linear operator, W, is defined by
W u = 0 T ( T s ) ζ 1 E ζ η , ζ ( B ( T s ) ζ η ) C u ( s ) d s ,
in which a bounded invertible operator, W 1 , exists, such that W 1 l and C : U H is bounded, continuous is a constant R, such that
R = ( T s ) ζ 1 [ E ζ η , ζ ( B ( T s ) ζ η ) ] C 2
For brevity,
C 1 = s u p t M E ζ η ( B T ζ η ) 2 , C 2 = s u p t M B t ζ η E ζ η , ζ η + 1 ( B T ζ η ) 2 , C 3 = s u p t M t E ζ η , 2 ( B T ζ η ) 2 , R = ( t s ) ζ 1 [ E ζ η , ζ ( B ( T s ) ζ η ) ] C 2 , C 4 = E ζ η , ζ ( B ( t s ) ζ η ) 2 , W 1 = l .
Defining the control function,
u ( t ) = C * [ ( T s ) ζ 1 E ζ η , ζ ( B ( T t ) ζ η ) ] * W 1 y ^ ,
where
y ^ = y T E ζ η ( B T ζ η ) y 0 + B T ζ η E ζ η , ζ η + 1 ( B T ζ η ) y 0 T E ζ η , 2 ( B T ζ η ) y 1 p = 1 q E ζ η ( B ( T t p ) ζ η ) J p ( y ( t p ) ) + p = 1 q B ( T t p ) ζ η E ζ η , ζ η + 1 ( B ( T t p ) ζ η ) J p ( y ( t p ) ) p = 1 q ( T t p ) E ζ η , 2 ( B ( T t p ) ζ η ) J ˜ p ( y ( t p ) ) 0 T ( T s ) ζ 1 E ζ η , ζ ( B ( T s ) ζ η ) ( σ ˜ ( s , y ϱ ^ ( s , y s ) ) d w ( s ) ) 0 T ( T s ) ζ 1 E ζ η , ζ ( B ( T s ) ζ η ) h ˜ ( s , y ϱ ^ ( s , y s ) ) d s . E u ( t ) 2 81 R 2 l 2 T ( E y T 2 + C 1 E y 0 2 + C 2 E y 0 2 + C 3 E y 1 2 + C 1 p = 1 q β p ( r ) E y ( s ) 2 + C 2 p = 1 q β p ( r ) E y ( s ) 2 + C 3 p = 1 q γ p ( r ) E y ( s ) 2 + C 4 T 2 ζ 1 2 ζ 1 ( ν h ˜ + ν σ ˜ ) [ ( Z T + J 0 φ ˜ ) φ ˜ B + N T r ] 0 T ( α ( s ) + α 1 ( s ) ) d s ) .
Theorem 1.
If assumptions Hypothesis (1)–(6) are true, then system (1)–(3) is controllable on M if
1 9 p = 1 q [ Υ p + Υ ˜ p ] + T 2 ζ 1 2 ζ 1 μ ˜ 2 [ 0 T ( α ( s ) + α 1 ( s ) ) d s ] [ 1 + 81 R 2 l 2 T ] .
Proof. 
Define an operator, ϕ , as
( ϕ y ) ( t ) = E ζ η ( B t ζ η ) y 0 B t ζ η E ζ η , ζ η + 1 ( B t ζ η ) y 0 + t E ζ η , 2 ( B t ζ η ) y 1 + p = 1 q E ζ η ( B ( T t p ) ζ η ) J p ( y ( t p ) ) p = 1 q B ( T t p ) ζ η E ζ η , ζ η + 1 × ( B ( T t p ) ζ η ) J p ( y ( t p ) ) + p = 1 q ( T t p ) E ζ η , 2 ( B ( T t p ) ζ η ) J ˜ p ( y ( t p ) ) + 0 t ( t s ) ζ 1 E ζ η , ζ ( B ( t s ) ζ η ) h ˜ ( s , y ϱ ^ ( s , y s ) ) d s + 0 t ( t s ) ζ 1 × E ζ η , ζ ( B ( t s ) ζ η ) σ ˜ ( s , y ϱ ^ ( s , y s ) ) d w ( s ) d s + 0 t ( t s ) ζ 1 E ζ η , ζ ( B ( t s ) ζ η ) C u ( s ) d s .
Using the concept of Krasnoselkii’s fixed-point theorem, it is proven that ϕ has a fixed point and the system (1)–(3) is controllable on M . Separate the proof into several steps using Lemma 2. Let us define B r = { y B : y r } ; using Lemma 1, B r is closed, bounded, and convex set in B r .
Step 1: ϕ B r B r .
If we assume ϕ B r B r is not true, then
r E ϕ y ( t ) 2 9 E E ζ η ( B t ζ η ) y 0 2 + 9 E B t ζ η E ζ η , ζ η + 1 ( B t ζ η ) y 0 2 + 9 E t E ζ η , 2 ( B t ζ η ) y 1 2 + 9 E p = 1 q E ζ η ( B ( T t p ) ζ η ) J p ( y ( t p ) ) 2 + 9 E p = 1 q B ( T t p ) ζ η E ζ η , ζ η + 1 × ( B ( T t p ) ζ η ) J p ( y ( t p ) ) 2 + 9 E p = 1 q ( T t p ) E ζ η , 2 ( B ( T t p ) ζ η ) J ˜ p ( y ( t p ) ) 2 + 9 E 0 t ( t s ) ζ 1 E ζ η , ζ ( B ( t s ) ζ η ) h ˜ ( s , y ϱ ^ ( s , y s ) ) d s 2 + 9 E 0 t ( t s ) ζ 1 × E ζ η , ζ ( B ( t s ) ζ η ) σ ˜ ( s , y ϱ ^ ( s , y s ) ) d w ( s ) 2 + 9 E 0 t ( t s ) ζ 1 E ζ η , ζ ( B ( t s ) ζ η ) C u ( s ) d s 2 r 9 C 1 E y 0 2 + 9 C 2 E y 0 2 + 9 C 3 E y 1 2 + 9 C 1 p = 1 q β p ( r ) E y ( s ) 2 + 9 C 2 p = 1 q β p ( r ) E y ( s ) 2 + 9 C 3 p = 1 q γ p ( r ) E y ( s ) 2 + 9 C 4 T 2 ζ 1 2 ζ 1 ν h ˜ [ ( Z T + J 0 φ ˜ ) φ ˜ B + N T r ] 0 T α ( s ) d s + 9 C 4 T 2 ζ 1 2 ζ 1 ν σ ˜ [ ( Z T + J 0 φ ˜ ) φ ˜ B + N T r ] 0 T α 1 ( s ) d s + 81 R 2 l 2 T × [ E y T 2 + C 1 E y 0 2 + C 2 E y 0 2 + C 3 E y 1 2 + C 1 p = 1 q β p ( r ) E y ( s ) 2 + C 2 p = 1 q β p ( r ) E y ( s ) 2 + C 3 p = 1 q γ p ( r ) E y ( s ) 2 + C 4 T 2 ζ 1 2 ζ 1 [ ν h ˜ + ν σ ˜ ] [ ( Z T + J 0 φ ˜ ) φ ˜ B + N T r ] 0 T ( α ( s ) + α 1 ( s ) ) d s ] r 9 ( [ C 1 + C 2 ] E y 0 2 + p = 1 q β p ( r ) E y ( s ) 2 + C 3 E y 1 2 + p = 1 q γ p ( r ) E y ( s ) 2 + C 4 T 2 ζ 1 2 ζ 1 [ ν h ˜ + ν σ ˜ ] [ ( Z T + J 0 φ ˜ ) φ ˜ B + N T r ] 0 T ( α ( s ) + α 1 ( s ) ) d s ) × 1 + 81 R 2 l 2 T + 81 R 2 l 2 T ( E y T 2 )
Hence,
1 9 p = 1 q [ Υ p + Υ ˜ p ] + T 2 ζ 1 2 ζ 1 μ ˜ 2 [ 0 T ( α ( s ) + α 1 ( s ) ) d s ] [ 1 + 81 R 2 l 2 T ] ,
which is contrary to the assumption; hence, Φ B r B r .
Step 2: Consider the decomposition
ϕ ( y ) = ϕ 1 ( y ) + ϕ 2 ( y ) ,
where
ϕ 1 ( y ( t ) ) = 0 t ( t s ) ζ 1 E ζ η , ζ ( B ( t s ) ζ η h ˜ ( s , y ϱ ^ ( s , y s ) ) d s + 0 t ( t s ) ζ 1 E ζ η , ζ ( B ( t s ) ζ ) σ ˜ ( s , y ϱ ^ ( s , y s ) ) d w ( s ) . ϕ 2 ( y ( t ) ) = E ζ η ( B t ζ η ) y 0 B t ζ η E ζ η , ζ η + 1 ( B t ζ η ) y 0 + t E ζ η , 2 ( B t ζ η ) y 1 + p = 1 q E ζ η ( B ( T t p ) ζ η ) J p ( y ( t p ) ) p = 1 q B ( T t p ) ζ η E ζ η , ζ η + 1 × ( B ( T t p ) ζ η ) J p ( y ( t p ) ) + p = 1 q ( T t p ) E ζ η , 2 ( B ( T t p ) ζ η ) J ˜ p ( y ( t p ) ) + 0 t ( t s ) ζ 1 E ζ η , ζ ( B ( t s ) ζ η ) C u ( s ) d s .
Let y 1 , y 2 B r , then
E ϕ 1 ( y 1 ) ( t ) ϕ 1 ( y 2 ) ( t ) 2 2 E 0 t ( t s ) ζ 1 E ζ η , ζ ( B ( t s ) ζ η [ h ˜ ( s , y 1 ϱ ^ ( s , y s ) ) h ˜ ( s , y 2 ϱ ^ ( s , y s ) ) ] d s 2 + 2 E 0 t ( t s ) ζ 1 E ζ η , ζ ( B ( t s ) ζ η [ σ ˜ ( s , y 1 ϱ ^ ( s , y s ) ) σ ˜ ( s , y 2 ϱ ^ ( s , y s ) ) ] d s 2 2 C 4 T 2 ζ 1 2 ζ 1 K h ˜ y 1 ϱ ^ ( s , x s ) y 2 ϱ ^ ( s , x s ) 2 + 2 C 4 T 2 ζ 1 2 ζ 1 K σ ˜ y 1 ϱ ^ ( s , x s ) y 2 ϱ ^ ( s , x s ) 2 2 C 4 T 2 ζ 1 2 ζ 1 [ K h ˜ + K σ ˜ ] μ ˜ 2 sup 0 s T E y 1 ( s ) y 2 ( s ) 2 K 0 y 1 ( s ) y 2 ( s ) 2 ,
where
K 0 = 2 C 4 T 2 ζ 1 2 ζ 1 [ K h ˜ + K σ ˜ ] μ ˜ 2 .
Thus, ϕ 1 ( y ( t ) ) is contractive.
Step 3: Let y B r ,
E ϕ 2 ( y ) ( t ) 2 7 E E ζ η ( B t ζ η ) y 0 2 + 7 E B t ζ η E ζ η , ζ η + 1 ( B t ζ η ) y 0 2 + 7 E t E ζ η , 2 ( B t ζ η ) y 1 2 + 7 E p = 1 q E ζ η ( B ( T t p ) ζ η ) J p ( y ( t p ) ) 2 + 7 E p = 1 q ( B ( T t p ) ) ζ η E ζ η , ζ η + 1 ( B ( T t p ) ζ η ) J p ( y ( t p ) ) 2 + 7 E p = 1 q ( T t p ) E ζ η , 2 ( B ( T t p ) ζ η ) J ˜ p ( y ( t p ) ) 2 + 7 E 0 t ( t s ) ζ 1 E ζ η , ζ ( B ( t s ) ζ η ) C u ( s ) d s 2 7 ( C 1 + C 2 ) { E y 0 2 + p = 1 q β p ( r ) ) E y ( s ) 2 } + C 3 [ E y 1 2 + p = 1 n γ p ( r ) E y ( s ) 2 ] + R 2 T u ( s ) 2
which implies that E ϕ 2 ( y ) ( t ) 2 is bounded.
Step 4: Let 0 Γ 1 Γ 2 T ,
E ϕ 2 ( y ) ( Γ 2 ) ϕ 2 ( y ) ( Γ 1 ) 2 8 E [ E ζ η ( B ( Γ 2 ) ζ η ) E ζ η ( B ( Γ 1 ) ζ η ) ] y 0 2 + 8 E B ( Γ 2 Γ 1 ) ζ η [ E ζ η , ζ η + 1 ( B ( Γ 2 ) ζ η ) E ζ η , ζ η + 1 ( B ( Γ 1 ) ζ η ) ] y 0 2 + 8 E ( Γ 2 Γ 1 ) [ E ζ η , 2 ( B ( Γ 2 ) ζ η ) E ζ η , 2 ( B ( Γ 1 ) ζ η ) ] y 1 2 + 8 E p = 1 q [ E ζ η B ( Γ 2 t p ) ζ η E ζ η B ( Γ 1 t p ) ζ η ] J p ( y ( t p ) ) 2 + 8 E p = 1 q ( B ( Γ 2 t p ) ζ η B ( Γ 1 t p ) ζ η ) × [ E ζ η , ζ η + 1 B ( Γ 2 t p ) ζ η E ζ η , ζ η + 1 B ( Γ 1 t p ) ζ η ] J p ( y ( t p ) ) 2 + 8 E p = 1 q ( ( Γ 2 t p ) ( Γ 1 t p ) ) [ E ζ η , ζ B ( Γ 2 t p ) ζ η E ζ η , ζ B ( Γ 1 t p ) ζ η ] J ˜ p ( y ( t p ) ) 2 + 8 E 0 Γ 1 [ ( Γ 2 s ) ζ 1 E ζ η , ζ ( B ( Γ 2 s ) ζ η ) ( Γ 1 s ) ζ 1 E ζ η , ζ ( B ( Γ 1 s ) ζ η ) ] × C u ( s ) d s 2 + 8 E Γ 1 Γ 2 [ ( Γ 2 s ) ζ 1 E ζ η , ζ ( B ( Γ 2 s ) ζ η ) ] C u ( s ) d s 2 8 E [ E ζ η ( B ( Γ 2 ) ζ η ) E ζ η ( B ( Γ 1 ) ζ η ) ] y 0 2 + 8 E B ( Γ 2 Γ 1 ) ζ η [ E ζ η , ζ η + 1 ( B ( Γ 2 ) ζ η ) E ζ η , ζ η + 1 ( B ( Γ 1 ) ζ η ) ] y 0 2 + 8 E ( Γ 2 Γ 1 ) [ E ζ η , 2 ( B ( Γ 2 ) ζ η ) E ζ η , 2 ( B ( Γ 1 ) ζ η ) ] y 1 2 + 8 E [ E ζ η ( B ( Γ 2 t p ) ) ζ η E ζ η ( B ( Γ 1 t p ) ζ η ) ] 2 p = 1 q β p ( r ) E y ( s ) 2 + 8 E ( B ( Γ 2 t p ) ζ η B ( Γ 1 t p ) ζ η ) [ E ζ η , ζ η + 1 B ( Γ 2 t p ) ζ η E ζ η , ζ η + 1 B ( Γ 1 t p ) ζ η ] 2 × p = 1 q β p ( r ) + 8 E ( ( Γ 2 t p ) ( Γ 1 t p ) ) [ E ζ η , ζ B ( Γ 2 t p ) ζ η E ζ η , ζ B ( Γ 1 t p ) ζ η ] 2 p = 1 q γ p ( r ) E y ( s ) 2 + 8 R 2 0 Γ 1 ( Γ 2 s ) ζ 1 E ζ η , ζ ( B ( Γ 2 s ) ζ η ) ( Γ 1 s ) ζ 1 E ζ η , ζ ( B ( Γ 1 s ) ζ η ) d s 2 E u ( s ) 2 + 8 R 2 ( Γ 2 Γ 1 ) 2 ζ 1 2 ζ 1 u ( s ) 2 .
So, E ϕ 2 y ( Γ 2 ) ϕ 2 y ( Γ 1 ) 2 0 as T 0 . Thus, ϕ 2 is equicontinuous.
Step 5: Let 0 ϵ t ; for any y B r , define an operator, ϕ ϵ , on B r ; then,
ϕ 2 ϵ y ( t ) = E ζ η ( B t ζ η ) y 0 B t ζ η E ζ η , ζ η + 1 ( B t ζ η ) y 0 + t E ζ η , 2 ( B t ζ η ) y 1 + p = 1 q E ζ η ( B ( T t p ) ζ η ) J p ( y ( t p ) ) p = 1 q B ( T t p ) ζ η E ζ η , ζ η + 1 × ( B ( T t p ) ζ η ) J p ( y ( t p ) ) + p = 1 q ( T t p ) E ζ η , 2 ( B ( T t p ) ζ η ) J ˜ p ( y ( t p ) ) + 0 t ϵ ( t s ) ζ 1 E ζ η , ζ ( B ( t s ) ζ η ) C u ( s ) d s = E ζ η ( B t ζ η ) y 0 ( B t ζ η ) E ζ η , ζ η + 1 ( B t ζ η ) y 0 + t E ζ η , 2 ( B t ζ η ) y 1 + p = 1 q E ζ η ( B ( T t p ) ζ η ) J p ( y ( t p ) ) p = 1 q B ( T t p ) ζ η E ζ η , ζ η + 1 × ( B ( T t p ) ζ η ) J p ( y ( t p ) ) + p = 1 q ( T t p ) E ζ η , 2 ( B ( T t p ) ζ η ) J ˜ p ( y ( t p ) ) + T ( ϵ ) 0 t ϵ ( t s ϵ ) ζ 1 E ζ η , ζ ( B ( t s ) ζ η ) C u ( s ) d s .
Since T(t) is a compact operator. Q ( t ) = { ϕ 2 y ( t ) , x B r } is relatively compact set in H ϵ 0 . Furthermore, for every y B r , we have
E ( ϕ 2 ) y ( t ) ( ϕ 2 ϵ ) y ( t ) 2 t ϵ t [ C ( T s ) ζ 1 E ζ η , ζ ( B ( T s ) ζ η ) ] * W 1 × [ y T E ζ η ( B T ζ η ) y 0 + B T ζ η E ζ η , ζ η + 1 ( B T ζ η ) y 0 T E ζ η , 2 ( B T ζ η ) y 1 p = 1 q E ζ η ( B ( T t p ) ζ η ) J p ( y ( t p ) ) + p = 1 q B ( T t p ) ζ η E ζ η , ζ η + 1 ( B ( T t p ) ζ η ) J p ( y ( t p ) ) p = 1 q ( T t p ) E ζ η , 2 ( B ( T t p ) ζ η ) J ˜ p ( y ( t p ) ) 0 T ( T s ) ζ 1 E ζ η , ζ ( B ( T s ) ζ η ) × h ˜ ( s , y ϱ ^ ( s , y s ) ) d s 0 T ( T s ) ζ 1 E ζ η , ζ ( B ( T s ) ζ η ) σ ˜ ( s , y ϱ ^ ( s , y s ) ) d w ( s ) ] 2 .
Thus, E ( ϕ 2 ) ( y ) ( t ) ( ϕ 2 ϵ ( y ) ( t ) ) 2 0 as ϵ 0 .
Hence, Q ( t ) = { Φ 2 y ( t ) , y B r } is relatively compact in H . ϕ 2 is completely continuous according to the Arzela–Ascoli theorem. Thus, using Krasnoselkii fixed-point theorem, the operator, ϕ , has a fixed point. Thus, system (1)–(3) is controllable on M . □
Corollary 1.
In the absence of impulsive conditions, system (1)–(3) reduces to the following form:
0 C D t ζ y ( t ) B 0 C D t η y ( t ) = C u ( t ) + h ˜ ( t , y ϱ ^ ( t , y t ) ) + σ ˜ ( t , y ϱ ^ ( t , y t ) ) d w ( t ) d t , t M = [ 0 , T ] ,
y ( 0 ) = y 0 , y ( 0 ) = y 1 .
where C , B , h ˜ , and σ ˜ are defined similar to before. Then, the solution to system (4)–(5) can be written as
y ( t ) = E ζ η ( B t ζ η ) y 0 B t ζ η E ζ η , ζ η + 1 ( B t ζ η ) y 0 + t E ζ η , 2 ( B t ζ η ) y 1 + 0 t ( t s ) ζ 1 E ζ η , ζ ( B ( t s ) ζ η ) h ˜ ( s , y ϱ ^ ( s , y s ) ) d s + 0 t ( t s ) ζ 1 × E ζ η , ζ ( B ( t s ) ζ η ) σ ˜ ( s , y ϱ ^ ( s , y s ) ) d w ( s ) d s + 0 t ( t s ) ζ 1 E ζ η , ζ ( B ( t s ) ζ η ) C u ( s ) d s .
where y ( t ) H satisfies Hypothesis 6; then, for any t J , the control can be chosen as
u ( t ) = C * [ ( T s ) ζ 1 E ζ η , ζ ( B ( T t ) ζ η ) ] * W 1 [ y T E ζ η ( B T ζ η ) y 0 B T ζ η E ζ η , ζ η + 1 ( B T ζ η ) y 0 T E ζ η , 2 ( B T ζ η ) y 1 0 T ( T s ) ζ 1 E ζ η , ζ ( B ( T s ) ζ η ) ( σ ˜ ( s , y ϱ ^ ( s , y s ) ) d w ( s ) ) 0 T ( T s ) ζ 1 E ζ η , ζ ( B ( T s ) ζ η ) h ˜ ( s , y ϱ ^ ( s , y s ) ) d s ] .
Then, the solution to system (4)–(5) satisfies y ( t ) = y 1 . Hence, the system is controllable on M .
Remark 1.
The study of approximate controllability of fractional-order non-instantaneous impulsive evolution systems involving SDD is studied in [25]. Controllability results for different types of linear and nonlinear systems with damping behavior are analyzed in [29,30,32]. Moreover, the approximate controllability of fractional neutral integro-differential equations with state-dependent delay in Hilbert space is discussed in [37]. To the best of the authors’ knowledge, there are no studies concerning the controllability of multi-term fractional-order impulsive stochastic systems with SDD involving damping behaviors, which is the main motivation of this study.

4. Example

Example 1.
Impulsive damped fractional-order stochastic system involving SDD of the form
C D t ζ Z ( t , x ) + λ C D t η Z ( t , x ) = C u ( t , x ) + k 2 2 z 2 Z ( t , x ) + t Π ( s t ) Z ( s ϱ ^ 1 ( t ) ϱ ^ 2 ( Z ( t ) ) , x ) d s + t Π ˜ ( s t ) y ( s ϱ ^ 1 ( t ) ϱ ^ 2 ( Z ( t ) ) , x ) d s d β ( t ) d t , t M = [ 0 , T ] , Z ( 0 , x ) = Z 0 ( x ) , Z ( 0 , x ) = Z 1 ( x ) , Z ( t , 0 ) = Z ( t , π ) = 0 , Δ Z ( t p , x ) = t p g ( t p s ) Z ( s , x ) d x , p = 1 , 2 , , q , Δ Z ( t p , x ) = t p g ˜ ( t p s ) Z ( s , x ) d x , p = 1 , 2 , , q .
Here, the Caputo derivatives, 0 C D t η and 0 C D t ζ , are of the order 0 < η 1 , 1 < ζ 2 and β ( t ) is the Wiener process in H = L 2 [ 0 , π ] on ( Ω , F , P ) . For ϱ ^ : M × B H , then ϱ ^ i : [ 0 , ) [ 0 , ) , i = 1 , 2 .
ϱ ^ ( t , ψ ) ( z ) = t ϱ ^ 1 ( t ) ϱ ^ 2 ( ψ ( 0 , z ) ) .
Furthermore, M × B H , Π , Π ˜ : R R is continuous
h ˜ ( t , ψ ) ( x ) = 0 Π ( s ) ψ ( s , x ) d x , σ ˜ ( t , ψ ) ( x ) = 0 Π ˜ ( s ) ψ ( s , x ) d x .
For z [ 0 , π ] , C u ( t , z ) : U M H is a bounded linear operator and C u ( t , z ) : [ 0 , T ] × [ 0 , π ] H is continuous. Define the operator, W, as
( W u ) ( ξ ) = n = 1 0 π 1 n sin n s ( C ( s , ξ ) , z n ) z n d s , ξ [ 0 , π ] .
Furthermore, J p , J ˜ p : B H and g , g ˜ > 0 for p = 1 , 2 , , q ,
J p ( ψ ) ( z ) = t p g ( t p s ) y ( s , z ) d z , J ˜ p ( ψ ) ( z ) = t p g ˜ ( t p s ) y ( s , z ) d z .
Furthermore, h ˜ K h ˜ , σ ˜ K σ ˜   J p K J p , J ˜ p K J ˜ p are bounded linear operators. Thus, the impulsive damped fractional-order stochastic system with SDD (1)–(3) is represented in abstract form (6). Therefore, system (1)–(3) is controllable on M , as (6) satisfies the conditions of Theorem 1.

5. Conclusions

The controllability results of damped impulsive multi-term non-integer-order stochastic systems involving SDD were addressed in this paper. By utilizing Krasnoselskii’s fixed-point technique, sufficient conditions were proven under certain assumptions. To illustrate the effectiveness of the result, an example was provided. The proposed approach can be applied to various kinds of multi-order fractional dynamical systems involving several delay effects, which will be the focus of future analysis.

Author Contributions

Conceptualization, G.A., M.V. and Y.-K.M.; methodology, G.A. and M.V.; software, G.A. and M.V.; validation, G.A., M.V. and Y.-K.M.; formal analysis, G.A.; investigation, G.A. and Y.-K.M.; resources, G.A. and Y.-K.M.; data curation, G.A., M.V. and Y.-K.M.; writing–original draft preparation, G.A. and M.V.; writing–review and editing, G.A., M.V. and Y.-K.M.; visualization, G.A.; supervision, G.A. and Y.-K.M.; project administration, G.A.; funding acquisition, G.A. and Y.-K.M. All authors have read and agreed to the published version of the manuscript.

Funding

The work of G.Arthi was supported by the Science and Engineering Research Board (SERB) POWER Grant (No.: SPG/2022/001970) funded by the Government of India. The work of Yong-Ki Ma was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIT) (No.: 2021R1F1A1048937).

Data Availability Statement

Not applicable.

Acknowledgments

The authors would like to thank the referees for their useful suggestions which have significantly improved the paper.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Abbas, S.; Benchohra, M.; Nakata, G.M. Advanced Fractional Differential and Integral Equations; Nova Science Publishers: New York, NY, USA, 2015. [Google Scholar]
  2. Hilfer, R. Applications of Fractional Calculus in Physics; World Scientific Publisher: Singapore, 2000. [Google Scholar]
  3. Kilbas, A.; Srivastava, H.M.; Trujillo, J.J. Theory and Applications of Fractional Differential Equations; Elsevier: Amsterdam, The Netherlands, 2006. [Google Scholar]
  4. Miller, K.S.; Ross, B. An Introduction to the Fractional Calculus and Fractional Differential Equations; Wiley: Hoboken, NJ, USA, 1993. [Google Scholar]
  5. He, J.H. Fractal calculus and its geometrical explanation. Results Phys. 2018, 10, 272–276. [Google Scholar] [CrossRef]
  6. Wang, K.J.; Xu, P. Generalized variational structure of the fractal modified KdV–Zakharov–Kuznetsov equation. Fractals 2023, 31, 2350084. [Google Scholar] [CrossRef]
  7. Wang, K.J.; Wang, G.D.; Shi, F. The pulse narrowing nonlinear transmission lines model within the local fractional calculus on the Cantor sets. COMPEL-Int. J. Comput. Math. Electr. Electron. Eng. 2023. [Google Scholar] [CrossRef]
  8. Saifullah, S.; Ali, A.; Khan, A.A.; Shah, K.; Abdeljawad, T. A Novel Tempered Fractional Transform: Theory, Properties and Applications to Differential Equations. Fractals 2023. [Google Scholar] [CrossRef]
  9. Balachandran, K.; Dauer, J.P. Controllability of nonlinear systems via fixed point theorems. J. Optim. Theory Appl. 1983, 53, 345–352. [Google Scholar] [CrossRef]
  10. Ankit, K.; Ramesh, V.K.; Kanika, D.; Avadhesh, K. Approximate controllability of delay nonautonomous integro-differential system with impulses. Math. Methods Appl. Sci. 2022, 45, 7322–7335. [Google Scholar]
  11. Arora, U.; Vijayakumar, V.; Shukla, A.; Sajid, M.; Nisar, K.S. A discussion on controllability of nonlocal fractional semilinear equations of order 1 < r < 2 with monotonic nonlinearity. J. King Saud Univ. Sci. 2022, 34, 102295. [Google Scholar]
  12. Camacho, O.; Leiva, H.; Riera-Segura, L. Controllability of semilinear neutral differential equations with impulses and nonlocal conditions. Math. Methods Appl. Sci. 2022, 45, 9826–9839. [Google Scholar] [CrossRef]
  13. Hakkar, N.; Dhayal, R. A Debbouche and DFM Torres, Approximate controllability of delayed fractional stochastic differential systems with mixed noise and impulsive effects. Fractal Fract. 2023, 7, 104. [Google Scholar] [CrossRef]
  14. Huang, J.; Luo, D. Relatively exact controllability of fractional stochastic delay system driven by Levy noise. Math. Methods Appl. Sci. 2023, 46, 11188–11211. [Google Scholar] [CrossRef]
  15. Nawaz, M.; Wei, J.; Jiale, S. The controllability of fractional differential system with state and control delay. Adv. Differ. Equ. 2020, 30. [Google Scholar] [CrossRef]
  16. Wei, J. The controllability of fractional control systems with control delay. Comput. Math. Appl. 2012, 64, 3153–3159. [Google Scholar] [CrossRef]
  17. Yan, L.; Fu, Y. Approximate controllability of fully nonlocal stochastic delay control problems driven by hybrid noises. Fractal Fract. 2021, 5, 30. [Google Scholar] [CrossRef]
  18. Fatima, B.; Rahman, M.U.; Althobaiti, S.; Althobaiti, A.; Arfan, M. Analysis of age wise fractional order problems for the COVID-19 under non-singular kernel of Mittag–Leffler law. Comput. Methods Biomech. Biomed. Eng. 2023, 1–19. [Google Scholar] [CrossRef] [PubMed]
  19. Ahmad, S.; Pak, S.; Rahman, M.U.; Al-Bossly, A. On the analysis of a fractional tuberculosis model with the effect of an imperfect vaccine and exogenous factors under the Mittag–Leffler kernel. Fractal Fract. 2023, 7, 526. [Google Scholar] [CrossRef]
  20. Shah, K.; Ali, A.; Zeb, S.; Khan, A.; Alqudah, M.A.; Abdeljawad, T. Study of fractional order dynamics of nonlinear mathematical model. Alex. Eng. J. 2022, 61, 11211–11224. [Google Scholar] [CrossRef]
  21. Ma, Y.K.; Johnson, M.; Vijayakumar, V.; Radhika, T.; Shukla, A.; Nisar, K.S. A note on approximate controllability of second-order impulsive stochastic Volterra-Fredholm integrodifferential system with infinite delay. J. King Saud Univ. Sci. 2023, 35, 102637. [Google Scholar] [CrossRef]
  22. Mahmudov, N.I.; Zorlu, S. Controllability of non-linear stochastic systems. Int. J. Control 2003, 76, 95–104. [Google Scholar] [CrossRef]
  23. Ain, Q.T.; Nadeem, M.; Akgul, A.; la Sen, M.D. Controllability of impulsive neutral fractional stochastic systems. Symmetry 2022, 14, 2612. [Google Scholar] [CrossRef]
  24. Bainov, D.; Simeonov, P. Impulsive Differential Equations: Periodic Solutions and Applications; Routledge: Oxfordshire, UK, 2017. [Google Scholar]
  25. Arora, S.; Mohan, M.T.; Dabas, J. Approximate controllability of fractional order non-instantaneous impulsive functional evolution equations with state-dependent delay in Banach spaces. IMA J. Math. Control Inf. 2022, 39, 1103–1142. [Google Scholar] [CrossRef]
  26. Debbouche, A.; Vadivoo, B.S.; Fedorov, V.E.; Antonov, V. Controllability criteria for nonlinear impulsive fractional differential systems with distributed delays in controls. Math. Comput. Appl. 2023, 28, 13. [Google Scholar] [CrossRef]
  27. Balachandran, K.; Govindaraj, V.; Rivero, M.; Trujillo, J.J. Controllability of fractional damped dynamical systems. Appl. Math. Comput. 2015, 257, 66–73. [Google Scholar] [CrossRef]
  28. Liu, Z.; Li, X. Existence of solutions and controllability for impulsive fractional order damped systems. J. Integral Equ. Appl. 2016, 28, 551–579. [Google Scholar] [CrossRef]
  29. Nawaz, M.; Wei, J.; Sheng, J.; Khan, A.U. The controllability of damped fractional differential system with impulses and state delay. Adv. Differ. Equ. 2020, 337. [Google Scholar] [CrossRef]
  30. Arthi, G.; Park, J.H.; Suganya, K. Controllability of fractional order damped dynamical systems with distributed delays. Math. Comput. Simul. 2019, 165, 74–91. [Google Scholar] [CrossRef]
  31. Arthi, G.; Suganya, K. Controllability of non-linear fractional-order systems with damping behaviour and multiple delays. IMA J. Math. Control Inf. 2021, 38, 794–821. [Google Scholar] [CrossRef]
  32. He, B.B.; Zhou, H.C.; Kou, C.H. The controllability of fractional damped dynamical systems with control delay. Commun. Nonlinear Sci. Numer. Simul. 2016, 32, 190–198. [Google Scholar] [CrossRef]
  33. Dos Santos, J.P.; Cuevas, C.; de Andrade, B. Existence results for a fractional equation with state-dependent delay. Adv. Differ. Equ. 2011, 2011, 642013. [Google Scholar] [CrossRef]
  34. Agarwal, R.P.; de Andrade, B.; Siracusa, G. On fractional integro-differential equations with state-dependent delay. Comput. Math. Appl. 2011, 62, 1143–1149. [Google Scholar] [CrossRef]
  35. Arthi, G.; Balachandran, K. Controllability of second-order impulsive functional differenial equations with state dependent delay. Bull. Korean Math. Soc. 2011, 48, 1271–1290. [Google Scholar] [CrossRef]
  36. Hernandez, E.; Azevedo, K.A.; O’Regan, D. On second order differential equations with state-dependent delay. Appl. Anal. 2018, 97, 2610–2617. [Google Scholar] [CrossRef]
  37. Yan, Z. Approximate controllability of fractional neutral integro-differential inclusions with state-dependent delay in Hilbert spaces. IMA J. Math. Control Inf. 2013, 30, 443–462. [Google Scholar] [CrossRef]
  38. Hino, Y.; Murakami, S.; Naito, T. Functional Differential Equations with Infinite Delay; Springer: Berlin/Heidelberg, Germany, 2006. [Google Scholar]
  39. Hernandez, E.; Prokopczyk, A.; Ladeira, L. A note on partial functional differential equations with state-dependent delay. Nonlinear Anal. Real World Appl. 2006, 7, 510–5199. [Google Scholar] [CrossRef]
  40. Burton, T. A fixed-point theorem of Krasnoselskii. Appl. Math. Lett. 1998, 11, 85–88. [Google Scholar] [CrossRef]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Arthi, G.; Vaanmathi, M.; Ma, Y.-K. Controllability Analysis of Impulsive Multi-Term Fractional-Order Stochastic Systems Involving State-Dependent Delay. Fractal Fract. 2023, 7, 727. https://doi.org/10.3390/fractalfract7100727

AMA Style

Arthi G, Vaanmathi M, Ma Y-K. Controllability Analysis of Impulsive Multi-Term Fractional-Order Stochastic Systems Involving State-Dependent Delay. Fractal and Fractional. 2023; 7(10):727. https://doi.org/10.3390/fractalfract7100727

Chicago/Turabian Style

Arthi, G., M. Vaanmathi, and Yong-Ki Ma. 2023. "Controllability Analysis of Impulsive Multi-Term Fractional-Order Stochastic Systems Involving State-Dependent Delay" Fractal and Fractional 7, no. 10: 727. https://doi.org/10.3390/fractalfract7100727

APA Style

Arthi, G., Vaanmathi, M., & Ma, Y. -K. (2023). Controllability Analysis of Impulsive Multi-Term Fractional-Order Stochastic Systems Involving State-Dependent Delay. Fractal and Fractional, 7(10), 727. https://doi.org/10.3390/fractalfract7100727

Article Metrics

Back to TopTop