1. Introduction
Fractional differential equations (FDEs) have emerged as indispensable mathematical tools for modeling complex phenomena in which the current state of a system depends on its entire history. Unlike classical integer-order models, which assume an instantaneous and local response, fractional-order operators encode memory and hereditary properties through a nonlocal convolution structure [
1,
2,
3]. This makes FDEs particularly well-suited for applications in viscoelasticity, anomalous diffusion, signal processing, control theory, and biological systems [
4,
5].
Among the many fractional derivative formulations that have been proposed, the Hilfer derivative occupies a distinguished position because it continuously interpolates between the Riemann–Liouville and Caputo derivatives through a single type parameter
. Building on this, the
-Hilfer fractional derivative [
6,
7] was introduced, which further generalizes the operator by incorporating a strictly increasing auxiliary function
, thereby allowing the fractional calculus to adapt to different temporal scales and geometric settings. This flexibility has attracted growing analytical interest, and well-posedness and stability results for
-Hilfer FDEs have been obtained in several recent contributions [
8,
9,
10,
11,
12].
A particularly challenging subclass consists of implicit FDEs, in which the highest-order fractional derivative appears nonlinearly on both sides of the governing equation. Specifically, we consider the
-Hilfer fractional Cauchy problem
subject to the generalized initial conditions
where
is the
-Hilfer derivative of complex order
with
and type
,
, and the solution
x is sought in the weighted space
. The operator notation
, with
, ensures that the initial data are prescribed consistently with the regularity of solutions in the weighted space. Here,
is a given function.
The present work extends our previous analysis of implicit Hilfer fractional differential equations [
12] in two important directions. First, we replace the standard Hilfer derivative with the
-Hilfer derivative, where the function
can be chosen freely to adapt the model to different applications (
gives a Hadamard-type derivative;
gives a Hilfer-type derivative). Second, we allow the fractional order
to be complex, with
. Complex-order derivatives arise naturally in models of viscoelasticity (capturing damping via complex moduli) and in control systems with oscillatory memory, whereas our previous work [
12] was limited to real orders. Physically, a complex order
endows the fractional operator with a built-in phase shift: writing the power-law kernel as
, the real part
controls the usual power-law memory decay, while the imaginary part
modulates this decay by a logarithmically oscillating factor. This is the fractional-calculus analogue of a complex modulus/complex compliance in viscoelasticity, where the storage and loss moduli are simultaneously encoded in a single complex-valued material parameter, and it mirrors the use of complex susceptibilities in dielectric relaxation and complex admittances in fractional-order circuit elements (constant phase elements). In control engineering, complex-order operators arise naturally when designing CRONE-type and fractional-order PID controllers with a prescribed constant phase margin over a frequency band, a property that cannot be reproduced by a real-order operator alone. The principal advantage of a complex-order model over two independent real-order models is therefore parsimony: a single complex parameter
captures both the power-law decay rate and the oscillatory/dispersive character of the memory kernel, which would otherwise require a superposition of several real-order terms. Note that the imaginary part
enters only through the modulus
and the complex exponents
; all stability constants depend on
solely through
and
.
The study of Hilfer and
-Hilfer FDEs has developed rapidly since the foundational works of Hilfer [
5] and Sousa–Oliveira [
6]. Existence and uniqueness for explicit Hilfer equations of order
were established in [
13,
14], and extended to nonlocal boundary conditions in [
15]. Ulam stability for Caputo-type equations was investigated in [
16,
17], and for the
-Hilfer setting in [
9,
18]. Implicit Hilfer equations of order
were studied in [
10,
19], where existence and UH stability were obtained under Lipschitz conditions in a fixed weighted space. The present work differs from all these contributions in three essential respects: (i) we treat the fully general case
for any
; (ii) we work with complex order
, which unifies the real-order theory; and (iii) we provide quantitative Ulam constants expressed through the function
, sharpening the qualitative estimates available in the existing literature.
The literature on
-Hilfer and related fractional operators has continued to grow rapidly during the preparation of this work. Multi-term
-Caputo equations with infinite delay have been shown to be existent and Hyers–Ulam stable via the Leray–Schauder alternative and new delay-adapted Gronwall-type inequalities [
20]; Ulam–Hyers–Rassias and Ulam–Hyers stability for
-Hilfer Volterra integro-differential equations with multiple variable delays have been obtained by fixed-point methods [
21]; existence and Ulam–Hyers–Rassias/semi-Ulam–Hyers–Rassias stability for
-Hilfer equations taken in the Caputo sense have been established using Mönch’s fixed-point theorem together with measures of noncompactness [
22]; Ulam-type stability for
-Hilfer Volterra integro-delay differential and integral equations incorporating multiple variable time delays has been proved via the Banach contraction principle [
23]; and, closest in spirit to our application in
Section 6, existence, uniqueness and Ulam-type stability for implicit
-Hilfer fractional differential equations have been established and applied to an RC electric circuit model [
24]. None of these contributions treats complex order
, and none handles the implicit nonlinearity via the auxiliary functional-equation reformulation used in Lemma 12; our doubly singular Gronwall inequality (Theorem 4) and the explicit Ulam constants of Remark 7 are also new relative to [
20,
21,
22,
23,
24]. On the modeling side, the foundational treatment of complex-order fractional derivatives in viscoelasticity by Atanacković, Konjik, Pilipović and Zorica [
25], who derived real-valued thermodynamic compatibility constraints for complex-order constitutive laws, provides independent physical motivation for the complex-order operator studied here; unlike [
25], we do not impose a reality constraint on the solution and instead study the full complex-valued well-posedness theory, with the real-order (physically constrained) case recovered as the special instance
.
To make the contribution of the paper explicit, we summarize below the aspects in which the present work goes beyond the existing
-Hilfer and Hilfer literature, including our own previous contribution [
26]:
- (C1)
Complex order. We treat the
-Hilfer derivative of complex order
with
,
, whereas [
6,
7,
8,
9,
10,
11] and our earlier paper [
26] are confined to real order. All well-posedness and stability constants are expressed through
and the modulus
only, so the classical real-order theory is recovered as the special case
.
- (C2)
Arbitrary integer part
n. The analysis covers the whole range
for arbitrary
, rather than being restricted to
as in [
13,
14,
19].
- (C3)
Implicit nonlinearity via an auxiliary functional equation. The reformulation of the implicit equation through the auxiliary function (Lemma 12) is new in the -Hilfer, complex-order setting; it reduces to the explicit theory when f does not depend on its third argument, thereby unifying both cases in a single framework.
- (C4)
A doubly singular generalized Gronwall inequality. Theorem 4 extends the Gronwall-type inequality of Ye et al. [
27] and of [
12] to
-weighted kernels with a singularity both at
and at
, which is required because of the weighted initial conditions (
2).
- (C5)
Explicit, computable stability constants. Unlike the qualitative Ulam stability statements in [
16,
17,
18,
19], Theorems 9–11 give closed-form constants
,
expressed through the entire function
of Definition 11, which we evaluate numerically in
Section 6.
We emphasize that the fixed-point theorems (Schaefer, Banach) and compactness criterion (Arzelà–Ascoli) used below are classical and are not themselves new. The contribution of the present paper is not the invention of new abstract fixed-point machinery, but rather (i) the non-trivial reformulation (C3) that makes this machinery applicable to the implicit, complex-order,
-weighted problem in the first place, and (ii) the new doubly singular Gronwall inequality (C4), which is required because the standard tools of Ye et al. [
27] and of our earlier work [
12] do not accommodate the additional singularity at
introduced by the weighted initial conditions (
2). We therefore regard the present work as a genuine but incremental extension of the existing
-Hilfer and Hilfer literature, rather than as a paper introducing fundamentally new analytical tools.
Three key difficulties distinguish problem (
1)–(
2) from the explicit
-Hilfer theory.
- 1.
Implicit nonlinearity. Because
appears as both the unknown and an argument of
f, one cannot apply the fractional integral
directly to obtain a Volterra equation. Instead, one must first solve an auxiliary functional equation
pointwise in
t, extract
as an element of
, and only then recover
x through the integral representation (
22).
- 2.
Singular kernels. The -Riemann–Liouville integral carries the kernel , which is singular both at (when ) and at (through the weighted space norm). Standard Gronwall inequalities are inapplicable; a new -adapted Gronwall inequality with a double singularity is required.
- 3.
General complex order. Extending the theory to complex with requires careful tracking of real and imaginary parts throughout, particularly in the contraction constant and in the Beta-function estimates used in the compactness argument.
We address these difficulties systematically. The implicit equation is first converted to an equivalent Volterra formulation (Lemma 12), whose derivation relies on the composition identities in Theorems 5 and 6 and the auxiliary space characterization of Lemma 10. Existence is then proved via Schaefer’s fixed-point theorem (Theorem 7) by establishing complete continuity of the fixed-point operator
and boundedness of the set
. Uniqueness (Theorem 8) follows from a Banach contraction argument under the condition
, where
is expressed explicitly in terms of
,
, and the Lipschitz constants of
f. Ulam-type stabilities (Theorems 9–10) and continuous dependence on initial data (Theorem 11) are derived using the new generalized Gronwall inequality (Theorem 4), which extends the classical result of Ye et al. [
27] to
-weighted kernels. The theory is then applied to a fractional nonlinear oscillator with saturating feedback (
Section 6), where all hypotheses are verified analytically and confirmed numerically.
The remainder of the paper is organized as follows.
Section 2 collects the necessary preliminaries on weighted function spaces, fractional operators, fixed-point theorems, and the new Gronwall inequality.
Section 3 presents auxiliary lemmas, the equivalent Volterra formulation, and the existence and uniqueness results.
Section 4 establishes the four Ulam stabilities.
Section 5 addresses continuous dependence on initial conditions.
Section 6 applies the theory to the fractional nonlinear oscillator with saturating feedback.
Section 7 and
Section 8 provide discussion and conclusions, respectively. The numerical algorithm is detailed in
Appendix A.
2. Preliminaries
Throughout the paper, we fix the following conventions, which are used without further comment: (i) always denote fractional orders, with , their real and imaginary parts, the (complex-valued) Euler Gamma function evaluated at , and its modulus; the modulus, rather than itself, is what appears in every norm estimate and stability constant, since these constants must be real and non-negative. (ii) is reserved exclusively for the effective order associated with type , and is never reused with another meaning. (iii) Weighted spaces are denoted , and exactly as in Definitions 1–3, with the weight exponent always written as a subscript preceding the comma and .
This section collects the basic definitions, function spaces, and preliminary results needed for the analysis of the considered fractional differential problem.
2.1. Weighted Function Spaces
Throughout the paper, let and let satisfy . These assumptions are fixed unless stated otherwise. All complex function spaces are considered over the interval .
Standing Assumption (). Unless explicitly stated otherwise, is assumed throughout the paper to satisfy for the relevant , to be strictly increasing on , and to satisfy for all . This hypothesis is not repeated in the subsequent definitions, lemmas and theorems.
For define . In particular, . This notation is used throughout the paper in place of and .
Let
denote the complex continuous function space. We generalize the real weight function spaces
and
(see [
6]) to complex function spaces as follows.
Definition 1 (
-Weighted Continuous Space
)
. Let be strictly increasing with on . The space consists of all functions such that is endowed with the norm The space
allows functions to exhibit a controlled singularity at
, no stronger than
. In particular,
where
and
are the continuous and weighted spaces, respectively, defined in [
1].
Definition 2 (
-Weighted Space
)
. Let and let with on . We define as the Banach space of functions such thatwhere . The norm is given by In particular, if , we have .
Definition 3 Let , , and set . Define the weighted spacewith norm . Extending the definitions from [
6,
12,
28] to higher order, we introduce
where
.
2.2. Fractional Integrals and -Hilfer Fractional Derivative
This subsection introduces the fractional operators required for the analysis. Throughout, let be a non-integer with , and let denote its integer part. All operators are defined on the interval , where .
Definition 4 (
-Riemann–Liouville fractional integral, [
7])
. Let with . Under the standing assumption on , the ψ-Riemann–Liouville fractional integral of order α of a function f is defined by Since ranges over in (3), we always have ; by Remark 1, the integrand is therefore well-defined and single-valued for every , with no branch ambiguity.
Lemma 1 ([
6])
. Let with and . Then, we have the following semigroup property given by Definition 5 (
-Riemann–Liouville fractional derivative, [
7])
. Let with . Under the standing assumption on , the ψ-Riemann–Liouville derivative of order α of a function f on is defined by Property 1 ([
1])
. Let with and .- (i)
If , then - (ii)
If , then - (iii)
If , then
Definition 6 (
-Hilfer fractional derivative, [
7])
. Let with and . Under the standing assumption on , the ψ-Hilfer fractional derivative of order α and type β is defined by Equivalently, the
-Hilfer fractional derivative admits the representation
where
denotes the
-Riemann–Liouville fractional derivative.
Remark 1 (Well-posedness of the complex power: absence of branch ambiguity)
. For with , the Standing Assumption on ψ (strict monotonicity, ) guarantees is a strictly positive real number. Hence, for any , the complex poweris defined using the ordinary (single-valued, real) natural logarithm of a positive real number. No branch cut of the complex logarithm is ever crossed, so this power is well-defined, single-valued, and jointly continuous (indeed jointly real-analytic in and entire in α) throughout the paper; in particular, it does not depend on any choice of branch of log. This is what justifies the exact modulus identityused in Lemmas 3–5, Theorem 4 and throughout Section 3, Section 4 and Section 5: it is an equality, not an estimate obtained by analogy with the real-order theory. Similarly, since with excludes every pole of the Euler Gamma function, the maps and are holomorphic on the whole admissible vertical strip ; consequently, the exact identities of Property 1 and Lemma 1, which involve without a modulus, are valid on this strip by analytic continuation from the classical real-order identities, and are not merely formal extensions. Remark 2. For , the parameters and satisfy the following properties:
- (i)
γ is a convex combination: ;
- (ii)
strictly when ;
- (iii)
;
- (iv)
.
2.3. Functional Analysis Tools
The well-posedness analysis carried out in
Section 3 rests on reformulating the implicit fractional problem as a fixed-point equation on a suitable Banach space. Once the original equation is expressed in integral form, existence, uniqueness, and stability reduce to questions about a single operator
, which are then addressed through classical fixed-point theory.
A subset of a Banach space is relatively compact if every sequence in admits a convergent subsequence in . An operator is completely continuous if it is continuous and sends every bounded subset of to a relatively compact set. Throughout, we work in the weighted Banach space , and relative compactness in this space is verified using the criterion of the Arzelà–Ascoli theorem, which characterizes compact sets through uniform boundedness and equicontinuity. Existence of at least one solution is then obtained from the following fixed-point theorem for completely continuous operators.
Theorem 1 (Arzelà–Ascoli)
. A subset of the Banach space is relatively compact if and only if it is uniformly bounded and equicontinuous on .
Theorem 2 (Schaefer’s Fixed-Point Theorem, [
29])
. Let be a completely continuous operator in the Banach space , and suppose that the setis bounded. Then has at least one fixed point in . In addition to compactness arguments, uniqueness of solutions is obtained through Banach’s fixed-point theorem.
Theorem 3 (Banach’s Fixed-Point Theorem on a Closed Subset)
. Let be a non-empty closed subset of the Banach space . If a mapping is a contraction, i.e., there exists a constant such thatthen has a unique fixed point in . 2.4. Ulam Stability
The previous section settled existence and uniqueness, so the fractional problem is well-posed in the right functional space. Solvability alone, however, does not tell us how solutions react to small disturbances. Those matter in practice, where models always carry some uncertainty from measurements, approximations, or external effects.
Motivated by this consideration, we investigate the stability of solutions to problem (
1)–(
2) in the sense of Ulam. The objective is to ensure that approximate solutions, which satisfy the equation up to a prescribed perturbation, remain close to exact solutions while preserving the structural conditions of the problem.
Let
,
be a continuous function, and
. Let
be a given function with
. For problem (
1), we analyze the perturbation inequalities listed below:
with the integral initial value conditions
where
is the
-Hilfer fractional derivative with
and
.
Definition 7 (Ulam–Hyers stable, [
26])
. Problem (1)–(2) is Ulam–Hyers stable (UH-Stable) if there exists a real number such that, for each and for each solution of the inequality (7) with (10), there exists a solution of problem (1)–(2) satisfying Definition 8 (Generalized Ulam–Hyers stable, [
26])
. Problem (1)–(2) is generalized Ulam–Hyers stable (GUH-Stable) if there exists a continuous function with such that, for each and for each solution of the inequality (7) with (10), there exists a solution of problem (1)–(2) satisfying Definition 9 (Ulam–Hyers–Rassias stable, [
26])
. Problem (1)–(2) is Ulam–Hyers–Rassias stable (UHR-Stable) with respect to σ if there exists a constant such that, for each and for each solution of the inequality (8) with (10), there exists a solution of problem (1)–(2) satisfying Definition 10 (Generalized Ulam–Hyers–Rassias stable, [
26])
. Problem (1)–(2) is generalized Ulam–Hyers–Rassias stable (GUHR-Stable) with respect to σ if there exists a constant such that, for each solution of the inequality (9) with (10), there exists a solution of problem (1)–(2) satisfying Remark 3. It is clear that (i) Definition 7 implies Definition 8; (ii) Definition 9 implies Definition 10; (iii) Definition 9 implies Definition 7.
Remark 4. A function is a solution of inequality (7) if and only if there exists a function such that for andAnalogous observations hold for inequalities (8) and (9). 2.5. Generalization of Gronwall’s Inequality
In this subsection, we develop an integral inequality involving singular
-weighted kernels, which plays a central role in establishing the stability and uniqueness results of the subsequent sections. The classical Gronwall-type inequality with singular behavior, due to Ye et al. [
27], was extended in [
12] to handle the singular kernels arising in Hilfer-type fractional equations. Here, we carry this line of analysis further by generalizing the inequality to the
-weighted setting, accommodating the broader class of
-Hilfer fractional operators considered in the present work. We begin by recalling a definition and an auxiliary lemma needed for the proof.
Definition 11 ([
30])
. Let and . Thus, we get the following definition:where , (empty product) and for . Lemma 2 ([
30])
. Let and . Then Remark 5. The asymptotic estimate provided by Lemma 2 confirms that the function introduced in Definition 11 is well-defined. Indeed, applying that estimate to successive coefficients gives . Since , the exponent is negative, so as . Equivalently, the reciprocal ratio satisfies , so the ratio test yields an infinite radius of convergence for the power series . Consequently, this series converges absolutely and uniformly on every compact subset of , and the resulting function is infinitely differentiable—in particular, continuous—on the whole real line.
To this end, we present a generalized version of Gronwall’s inequality involving a singular kernel, which serves as a fundamental tool for establishing the main results of this section. The proof follows by reducing the inequality to the original generalized Gronwall Inequality (Theorem 3 of [
12]) via the change of variable
.
Theorem 4 (Generalized Gronwall Inequality with respect to ). Assume that , , . Let be strictly increasing, i.e., for all , where . Let and be non-negative, non-decreasing continuous functions on with for some positive constant M. Suppose is non-negative and is locally integrable on .
If u satisfies the inequalitythen the following estimate holds:for all . Proof. Define the transformed variable . Since is strictly increasing with , the map is a diffeomorphism from onto , where and (which may be ). The inverse function is also on .
Now define the transformed functions:
Consider the integral term in inequality (
11). Make the substitution
. Then,
, and when
,
; when
,
. Moreover,
and
. Thus,
The inequality (
11) becomes
for all
. If
, the resulting inequality (12) is understood to hold on
; this causes no difficulty since all hypotheses (monotonicity, local integrability) are stated locally.
We now check that all conditions of Theorem 3 of [
12] are satisfied for the transformed inequality:
and
,
remain unchanged. Since
and
are non-negative and non-decreasing on
, and
is strictly increasing, the transformed functions
and
are also non-negative and non-decreasing on
. The bound
implies
.
is non-negative because
. The condition that
is locally integrable in
t implies, via the change of variable, that
is locally integrable in
x (since
is continuous and positive, the Jacobian factor is bounded away from zero on compact intervals).
Thus all hypotheses of Theorem 3 of [
12] are satisfied. So applying the theorem to inequality (
12) yields
for all
.
Now substitute back
,
,
,
,
. Then,
for all
. Recalling the definition of
, we obtain the final estimate:
where the empty product for
or
is understood as 1.
Since
and
, we have
. The ratio test gives
because
as
. Hence, the series converges absolutely for all
. This completes the proof. □
Corollary 1. Assume that , , . Let be strictly increasing, i.e., for all , where , . Let and be non-negative, non-decreasing continuous functions on with for some positive constant M. Further suppose that is non-negative and is locally integrable on .
If u satisfies the inequalitythen the following estimate holds:for all . The main results of the paper follow. Working in the functional framework just described, we prove existence, uniqueness, and stability for the fractional differential problem under consideration.
6. Application: A Fractional Nonlinear Oscillator with Saturating Feedback
We consider a mechanical oscillator of mass m (kg) attached to a linear spring with stiffness constant k (N/m). The oscillator is subjected to an active feedback force that depends on its instantaneous acceleration. Such configurations arise in active vibration control systems, where an actuator applies a corrective force proportional to the measured acceleration of the mass. However, physical actuators have limited power output; as the required acceleration becomes large, the actuator saturates, and the effective feedback force decays toward zero rather than increasing without bound.
A standard engineering model for this saturation phenomenon is the Lorentzian (or algebraic) saturation function
where
is the maximum feedback force (when
),
a is the saturation acceleration (the scale at which saturation becomes significant), and
is the instantaneous acceleration. This function is bounded
, smooth, and reduces to
for small accelerations
, while decaying as
for large accelerations
.
Newton’s second law for the oscillator (mass
m) is
The parameters and variables of the mechanical oscillator model are defined below, with dimensions given in the MLT system (Mass
M, Length
L, Time
T). The mass-spring oscillator with saturating acceleration feedback is illustrated schematically in
Figure 1.
Time: t; .
Displacement: ; .
Velocity: ; .
Acceleration: ; .
Mass: m; .
Spring constant: k; (force per unit displacement).
Maximum feedback force: ; .
Saturation acceleration: a; .
To nondimensionalize the system, we introduce the characteristic scales:
where
is the natural time scale of the undamped oscillator, and
is the static displacement under constant force
. By defining dimensionless variables
the physical acceleration becomes
Substituting into Newton’s law and dividing by
, we obtain
Define the dimensionless saturation parameter:
For simplicity, we set
(i.e.,
), which corresponds to choosing the saturation acceleration as the natural acceleration scale of the system. Dropping the “dimless” subscript and relabeling
as
t, we arrive at the dimensionless classical equation:
with initial conditions
Classical integer-order models assume instantaneous response. However, real viscoelastic materials and memory-dependent control systems exhibit hereditary effects. To capture memory, we replace the integer-order acceleration
in the normalized classical Equation (
59) with the
-Hilfer fractional derivative
of order
. The parameter
controls memory strength: as
, we recover the classical derivative;
near 1 indicates strong memory.
In the normalized classical equation, all quantities are dimensionless. However, the fractional derivative
has dimension
in physical units, while the classical acceleration
is dimensionless in the normalized equation. To maintain dimensional consistency when passing from the classical to the fractional formulation, we introduce a characteristic time scale
(with
) and define the dimensionless replacement rule:
This scaling ensures that the fractional term remains dimensionless (since
has dimension
, which cancels the dimension
of the fractional derivative). The classical model is recovered when
(since
).
Applying this substitution directly to the normalized classical Equation (
59) yields the fractional generalization:
Following the methodology of Gómez-Aguilar et al. [
31], we establish the scaling relation:
, where
is the fractional saturation parameter with dimensions
, and
a is the classical saturation acceleration. This ensures that the argument of the saturation function remains dimensionless. In the classical limit
, we have
and
, recovering the classical saturation parameter.
For the numerical simulations, we choose specific values of
(or equivalently
) that satisfy the physical constraints. After normalizing the remaining parameters (
), the fractional model becomes
The fractional generalization is subject to the
-Hilfer initial conditions:
where
is the effective order. The parameter
interpolates between Riemann–Liouville-type (
) and Caputo-type (
) initial conditions.
Before proceeding to the numerical solution of the fractional model, it is instructive to first examine the behavior of the classical integer-order system, which serves as a reference and limiting case.
For the fractional model, we set
,
,
, and
in the numerical simulations. With these choices, we have
, so the
-Hilfer derivative reduces to the Caputo derivative
, and the initial conditions (
62) simplify to the classical form (
60). From Lemma 12, the equivalent integral equation for (
61) becomes
where
satisfies
, and
denotes the standard Riemann–Liouville fractional integral of order
.
The numerical solutions in
Figure 2 are obtained using two schemes. For the classical model (
59), an explicit Runge–Kutta method is employed, where the implicit algebraic relation
is resolved at each stage by Newton’s method. For the fractional model, the equivalent Volterra Equation (
63) is discretized on a uniform grid with
points using a product quadrature rule, and the resulting nonlinear equation for
is again solved by Newton’s method. Both schemes use a Newton tolerance of
. The complete implementation is given in
Algorithm A1 (
Appendix A).
To assess the numerical accuracy of the product-quadrature scheme described above, we fix
,
,
,
and compute a reference solution
on
using
grid points. For
, we compute the corresponding numerical solution
and the discrete maximum error
interpolating
onto the coarser grid where necessary. The estimated order of convergence is
, which measures the rate at which the error decreases as the grid is refined.
Table 1 reports
and
; the results are consistent with the theoretical rate
expected for a piecewise-constant product quadrature of a Caputo-type equation with
, where the discretization error is dominated by the
approximation of
on each subinterval rather than by the (integrable) kernel singularity.
Regarding computational complexity, the convolution weights
defined in (
A2) must be computed for all
, requiring
arithmetic operations and
storage in the direct implementation used here (
Algorithm A1); the Newton iteration at each of the
N time steps adds only an
overall contribution since each scalar Newton solve converges in a fixed, small number of iterations independent of
N (typically 3–5 iterations to reach the tolerance
). For the problem sizes used in this paper (
), this
cost is negligible in absolute terms; for substantially larger
N, fast convolution techniques for fractional operators (e.g., sum-of-exponentials or FFT-based approaches) would reduce the cost to
, but such acceleration is outside the scope of the present well-posedness study and is left for future numerical work.
The fractional-order dynamics exhibit memory effects that alter the system response relative to the classical model. As increases, memory diminishes and solutions converge to the classical trajectory, confirming consistency. On the short interval , all solutions remain close. On the extended interval , memory effects become more pronounced, with lower values (stronger memory) exhibiting slower response and reduced amplitude.
Equation (
61) is a special case of the abstract problem (
1)–(
2) with nonlinearity
Since is a composition of continuous functions and for all , the map belongs to for any , since is closed under addition, bounded perturbations, and composition with smooth bounded functions. Hence, (H0) is satisfied.
For any
, we have
where we used
. Setting
, we obtain
with
, and
(as constant functions). Hence, (H1) is satisfied.
For any
and
, we have
For the second term, writing
and applying the mean value theorem with
, we get
Therefore,
with constants
. Hence, hypothesis (H2) is satisfied globally on
.
By Theorem 7, the Cauchy problem admits at least one solution in
, since (H0)–(H2) are satisfied. Moreover, with the computed constants
and
, the contraction constant
in Theorem 8 is given by
For the specific case
, we have
,
, and
Thus, on the interval
, the uniqueness condition
is satisfied for
, and Theorem 8 guarantees a unique solution in
.
Remark 10. As the fractional order α approaches the classical limit , the admissible interval length b satisfying attains its maximum, and the type parameter β tends to 1 (Caputo case). Specifically, for , the maximum b is approximately , at which point only is admissible. For smaller α, the maximum admissible b decreases significantly. Similarly, for fixed , the maximum admissible b decreases sharply as β moves away from 1, dropping from at to at . Consequently, to maximize the interval length while preserving the uniqueness condition , one should take α as close to 2 as possible and (Caputo formulation). Any deviation from these optimal values necessitates a corresponding reduction in b.
Furthermore, under the same condition, Theorems 9 and 10 establish that the solution is UH-stable and GUH-stable. To verify this numerically, we introduce a perturbation as described in Remark 4, choosing so that for the fractional oscillator with , . This oscillatory perturbation represents a common type of disturbance in physical systems (e.g., sensor noise or environmental vibrations), and its non-constant, sign-changing nature provides a stringent test of the stability estimate. Using Remark 7 with the parameters above, we obtain . The theoretical bound from Theorem 9 then gives .
Figure 3 shows the unperturbed and perturbed solutions: panel (a) on
(where stability is guaranteed) and panel (b) on
(extended view). The trajectories remain close despite the perturbation.
Figure 4 shows that the computed deviation
remains well below this bound for all
, confirming Ulam–Hyers stability.
To demonstrate Ulam–Hyers–Rassias stability, we choose . We define the perturbation , which satisfies with .
For
, we need to verify hypothesis (H3): there exists
such that for each
The theoretical bound from Theorem 10 and Remark 7 for
on the interval
gives
Figure 5 shows the unperturbed solution
and the perturbed solution
. The exponentially modulated perturbation
(satisfying
with
and
as in Remark 4) produces a larger deviation from the unperturbed trajectory while preserving the qualitative behavior of the system.
Figure 6 plots the deviation
together with the theoretical bound
. The deviation remains below this exponentially growing bound for all
, confirming the UHR stability of the fractional model.
To verify the continuous dependence result of Theorem 11, we perturb the initial conditions by a small amount
. With
, we set
and choose
as a representative small perturbation. Applying Theorem 11 with
and
, the theoretical bound becomes
where
is given by (
44):
Figure 7 shows the numerical solutions corresponding to the initial data
and
. Although the initial conditions differ, the resulting trajectories remain close throughout the interval, indicating that small perturbations in the prescribed data produce only moderate changes in the solution.
Figure 8 displays the difference
together with the theoretical bounds
. The numerical difference remains well within these bounds over the entire interval, demonstrating excellent agreement with the estimate obtained in Theorem 11 and providing numerical verification of the continuous dependence of solutions on the initial conditions.
The classical Hilfer and Caputo models are recovered from (
61)–(
62) as the special cases
, with
giving the Caputo derivative used for the baseline curves in
Figure 2, and
giving the (real-order) Riemann–Liouville Hilfer derivative. To isolate the effect of a genuinely complex order, we solve (
61) with
(same real part as the
curve of
Figure 2) and compare the real part of the resulting complex-valued trajectory,
, against the real-order solution with
.
Figure 9 shows that the two trajectories share the same overall power-law growth rate (governed by
in both cases), while the complex-order trajectory exhibits a small phase-shifted oscillation superimposed on the monotone trend, consistent with the interpretation of
as a logarithmically oscillating modulation of the memory kernel discussed in the Introduction. Physically, this is the fractional-order analogue of adding a fixed phase lag to the system’s memory response, a feature that is unavailable in any real-order (classical Hilfer or Caputo) model and that motivates the use of complex-order operators in CRONE-type controllers and in viscoelastic models with a prescribed constant phase margin, as discussed in
Section 1.
The qualitative statement above can be made fully explicit. By Remark 1, for
the quantity
is a strictly positive real number, so Euler’s formula applied to the exact identity of Remark 1 gives, without approximation,
Thus the real part of the memory kernel used in
is
exactly a power-law kernel of exponent
modulated by a single cosine that oscillates linearly in
, with frequency
and zero phase. Reproducing the same real part with purely real-order operators requires, in general, a superposition
and matching a single log-periodic modulation of prescribed frequency and phase forces
with 4 independent real parameters
(or, equivalently, an amplitude/phase pair together with two decay rates), against the 2 real parameters
needed in the complex-order formulation. This is the precise sense in which the complex-order operator is parsimonious relative to real-order superposition models: identity (
55) is exact algebra, not a numerical fit, so this advantage is not an artifact of the particular oscillator example of
Section 6 but a structural property of the complex-order kernel itself.
Two concrete practical points distinguish the present framework from existing real-order
-Hilfer treatments and from a naive discretization of the implicit problem (
1)–(
2).
(i) Parameter economy. As shown by the exact decomposition (
65), matching both the power-law memory decay and a prescribed phase lag in the oscillator response (
Figure 9) requires only the two real numbers
in the complex-order model, versus at least four real parameters in any real-order superposition achieving the same log-periodic modulation. For system-identification tasks such as fitting a CRONE controller or a viscoelastic constant-phase element to measured frequency-response data, this halves the number of free parameters to be estimated, at the cost of solving the complex-order well-posedness problem addressed by Theorems 7–8 (rather than a system of coupled real-order equations).
(ii) Computational cost relative to a direct implicit discretization. The reformulation of Lemma 12 replaces the original implicit equation, in which the fractional derivative appears nonlinearly on both sides, by the explicit Volterra representation (
22) together with the scalar pointwise functional Equation (
23) for the auxiliary unknown
. Consequently, in the product-quadrature scheme of
Appendix A, the nonlinear system to be solved by Newton’s method at each time step
is one-dimensional (Equation (
A4)), and the overall cost is
(dominated by the convolution weights
) plus
scalar Newton solves, each converging in 3–5 iterations (
Section 6, “Convergence analysis”). A direct discretization of the original implicit Equation (
1) that does not use the reformulation of Lemma 12 would instead need to solve, at each step, a nonlinear system coupling the approximation of
with the approximation of its own fractional derivative, which does not reduce to a scalar equation, in general, and is correspondingly more expensive and more delicate to initialize reliably. The independent fractional Adams–Bashforth–Moulton cross-check already reported in
Table 1 (columns
and “Agreement”) confirms that the two approaches agree to within
at
, while the reformulated scheme used throughout this paper benefits from the reduced, scalar nonlinear structure guaranteed by Lemma 12. This is a direct practical payoff of the theoretical reformulation developed in
Section 3, rather than merely a numerical illustration of it.
7. Discussion
The results established in this paper provide a rigorous and comprehensive well-posedness theory for a broad class of nonlinear implicit -Hilfer fractional differential equations. Several aspects of the analysis merit further comment.
The choice of the weighted Banach space is not merely a technical convenience but reflects the genuine behavior of solutions near the left endpoint . Solutions to -Hilfer equations exhibit a singularity at , and the weighted norm is precisely calibrated to handle this behavior.
In the existence proof via Schaefer’s fixed-point theorem, we first establish a fixed point x in the larger space . Then, by applying the fractional derivative to the integral equation and using the regularity of , we show that . This implies , i.e., the solution automatically possesses higher regularity. Thus, the weighted space provides not only a natural setting for existence but also a pathway to regularity.
The core analytical challenge of problem (
1) is that the
-Hilfer derivative
appears simultaneously as the left-hand side and as an argument of
f on the right. The resolution, developed in Lemma 12, is to introduce the auxiliary function
. For each fixed
t, the equation
can be rewritten as
where
. Under hypothesis (H2) with
, we have
, so the Implicit Function Theorem guarantees that
is uniquely determined as a function of
near any solution. Moreover, the Lipschitz estimate
follows directly from the contraction property of
f in its second argument. This reformulation turns the original implicit problem into a standard Volterra equation, and all our main results are built upon this key idea.
The stability and continuous-dependence proofs require bounding an integral inequality of the form
whose kernel is singular at both
and
. The new Theorem 4 extends the result of [
12] to this doubly singular
-weighted setting via a change of variable
, reducing to the classical Gronwall inequality in the transformed coordinates. The resulting bound, expressed through the entire function
, is sharp and yields the explicit stability constants in Remark 7.
The uniqueness criterion
in Theorem 8 provides a fully explicit condition on the problem data. For the application in
Section 6, with
,
,
(Caputo type), and
,
, the condition reduces to
which is satisfied for
and all
. In particular, for
one obtains
, close to the boundary of the admissible region. The restriction
is not a weakness of our method. As explained in Remark 10, for
and
, the maximum admissible
b is approximately
, so
lies safely within this bound. On the longer interval
, uniqueness is no longer guaranteed by the theorem, but our numerical simulations show that different solutions do not actually appear. This suggests that the sufficient condition we used may be stricter than necessary.
The four Ulam stabilities established in Theorems 9–10 have a clear physical meaning in the context of the fractional oscillator application. UH stability guarantees that a trajectory satisfying the governing equation only approximately (e.g., due to sensor noise or numerical integration error) remains uniformly close to the exact trajectory, with the deviation bounded by
regardless of time. UHR stability is more refined: it allows the error tolerance to grow with a prescribed weight function
, accommodating situations in which disturbances intensify over time, as in the exponentially modulated perturbation
used in
Section 6. The computed stability constant
and the resulting bound
are confirmed by
Figure 6, which shows the numerical deviation remaining well within the theoretical envelope throughout
.
Theorem 11 establishes that a small perturbation
in each initial datum
produces a deviation bounded by
. The factor
vanishes at
for
, reflecting the fact that the solution is insensitive to initial data perturbations
at the endpoint (where the singularity of the weighted space absorbs the perturbation) but that sensitivity grows as
t moves away from
a. Remark 8 captures this observation explicitly: substantial sensitivity may appear on
for any
, but the deviation is controlled globally on
by the bound in (
48).
Figure 8 illustrates this behavior numerically for
.
We emphasize, however, that these three extensions—namely (i) the full range for arbitrary , (ii) the treatment of complex-order with , and (iii) the handling of the implicit structure via the auxiliary functional equation—are obtained by carefully adapting standard fixed-point and Gronwall-type techniques rather than by developing fundamentally new analytical machinery. The principal value of the paper lies in demonstrating that such an adaptation is feasible and in providing fully explicit, computable well-posedness and stability estimates that can be applied directly to problems in viscoelasticity, control theory, and anomalous diffusion.
Compared with existing results for
-Hilfer equations, which are mostly restricted to order
and often treat explicit problems only, the present work offers three principal extensions. First, the theory applies to the full range
for any
, and to complex
. Second, the implicit structure is handled by the auxiliary functional equation strategy, which includes the explicit case (
f independent of its third argument) as a special instance. Third, the stability constants
and
are given in closed form through
, providing quantitative estimates that can be evaluated numerically for problem (
1)–(
2) with arbitrary
satisfying
for any
.