Asymptotic Expansion Research Articles

Efficient path integral approach via analytical asymptotic expansion for nonlinear systems under Gaussian white noise

AbstractIn this paper an efficient formulation of the Path integral (PI) approach is developed for determining the response probability density functions (PDFs) and first-passage statistics of nonlinear oscillators subject to stationary and time-modulated external Gaussian white noise excitations. Specifically, the evolution of the response PDF is obtained in short time steps, by using a discrete version of the Chapman-Kolmogorov equation and assuming a Gaussian form for the conditional response PDF. Next, the technique involves proceeding to treating the problem via an analytical asymptotic expansion procedure, namely the Laplace’s method of integration. In this manner, the repetitive double integrals involved in the standard implementation of the PI approach are evaluated in a closed form, while the response and first-passage PDFs are obtained by mundane step-by-step application of the derived approximate analytical expression. It is shown that the herein proposed formulation can drastically decrease the associated computational cost by several orders of magnitude, as compared to both the standard PI technique and Monte Carlo solution (MCS) approach. A number of nonlinear oscillators are considered in the numerical examples. Notably, for these systems both response PDFs and first-passage probabilities are presented, whereas comparisons with pertinent MCS data demonstrate the efficiency and accuracy of the technique.

Near-Field to Far-Field Approximation Formulas Based on the Asymptotic Expansion Method for Electromagnetic Wave Propagation Across Air–Sea Boundary Due to a Vertical Magnetic Dipole

Near-Field to Far-Field Approximation Formulas Based on the Asymptotic Expansion Method for Electromagnetic Wave Propagation Across Air–Sea Boundary Due to a Vertical Magnetic Dipole

Adaptive Robust Control of Uncertain Euler-Lagrange Systems Using Gaussian Processes.

This article proposes a novel adaptive robust control approach based on Gaussian processes (GPs) for the high-precision tracking problem of uncertain Euler-Lagrange (EL) systems with time-varying external disturbances. Given a prior dynamic model, the GP regression (GPR) technique is employed to obtain a nonparametric data-based uncertainty model, including its probabilistic confidence intervals. Based on the adaptive sliding mode control (ASMC) framework, the posterior means of GPs are utilized for dynamic compensation, whereas the posterior variances are applied to adjust the feedback gains. This proposed control strategy is robust against significant system uncertainty with low feedback gains. A novel adaptive law for updating hyperparameters based on tracking error feedback is presented, thereby improving the performance of both tracking control and GP modeling simultaneously. Compared to existing likelihood-based optimization methods, this hyperparameter adaptive law enables data-efficient and fast uncertainty learning for control applications. The proposed control strategy guarantees the semiglobal asymptotic convergence to zero tracking error with a specified probability. Simulations using an underwater robot model demonstrate that the utilization of GPs and hyperparameter adaptive law significantly improves the performance of tracking control and uncertainty learning.

Mean-variance portfolio with wealth and volatility dependent risk aversion

Risk aversion rate plays a significant role in mean-variance portfolio selection. Most existing literature assumes it to be constant or wealth dependent, which is unrealistic. In this study, I contend that it is both wealth and volatility dependent because it varies across economic status: either steady or fluctuated. In addition, I decompose the risk aversion rate into a wealth prudence rate and a volatility prudence rate and investigate their mutual effect on portfolio selection under a continuous-time mean-variance framework. Using Game theoretic approach and asymptotic expansion, I derive the optimal trading rule analytically.

Abelian covers of hyperbolic surfaces: equidistribution of spectra and infinite volume mixing asymptotics for horocycle flows

We consider Abelian covers of compact hyperbolic surfaces. We establish an asymptotic expansion of the correlations for the horocycle flow on Zd -covers, thus proving a strong form of Krickeberg mixing. We also prove that the spectral measures around 0 of the Casimir operators on any increasing sequence of finite Abelian covers converge weakly to an absolutely continuous measure.

Asymptotic Expansions of Finite Hankel Transforms and the Surjectivity of Convolution Operators

A compactly supported distribution is called invertible in the sense of Ehrenpreis-Hörmander if the convolution with it induces a surjection from $\mathcal{C}^{\infty}(\mathbb{R}^{n})$ to itself. We give sufficient conditions for radial functions to be invertible. Our analysis is based on the asymptotic expansions of finite Hankel transforms. The dominant term may be the contribution from the origin or from the boundary of the support of the function. For the proof, we propose a new method to calculate the asymptotic expansions of finite Hankel transforms of functions with singularities at a point other than the origin.

Instability of a weakly viscoelastic film flow on an oscillating inclined plane

The long- and finite-wavelength instabilities of weakly viscoelastic film on an oscillating inclined plane are investigated. By using the Chebyshev series solution with the Floquet theory, the combined effects of viscoelasticity and forcing amplitude on instability are described when the inclined plane oscillates in streamwise and wall-normal directions. For long-wavelength instability, the solution to the eigenvalue problem is obtained analytically by the asymptotic expansion method. Results show that the Floquet exponent is independent of the wall-normal oscillation amplitude. The effects of inclined angle, gravity and surface tension on the stability of viscoelastic liquid film are also discussed. For finite-wavelength instability, numerical results corresponding to the wall-normal oscillation disclose that with the increase of viscoelasticity, the unstable gravitational boundary moves to a higher wavenumber, and the critical amplitudes of subharmonic and harmonic instabilities are reduced. The neutral curve of gravity instability for streamwise oscillatory flow is divided into two parts, and a stable bandwidth is formed for a large value of the viscoelastic parameter. Besides, a new oscillatory mode is identified at small angles of inclination, which will be enhanced if the effect of viscoelasticity is incorporated.

Optimal control strategies for PMSM with a decoupling super twisting SMC and inductance estimation in the presence of saturation

This paper deals with optimal control strategies robustified by a decoupling super-twisting sliding mode control (ST-SMC) for permanent magnet synchronous machines (PMSM). In general, ST-SMC is a robust method for controlling systems and guarantees asymptotic convergence in cases where the upper bound of model uncertainties such as disturbances and parametric uncertainties is not known a priori, if the upper bound of the derivative is known. The proposed optimal control strategy is designed for a constant torque reference whose control is implemented in two control regions: maximum torque per ampere (MTPA) and flux-weakening control. In the proposed analysis, the operating point of the motor can also be in the saturation region, which makes the estimation of Ld and Lq in the proposed optimization procedure of particular interest and also important for the decoupling control implemented with ST-SMC. To compensate for typical parameter variations, adaptive parameter estimation is introduced into the control system using an extended Kalman filter (EKF) in combination with a bivariate polynomial. In order to be able to make an assessment of the control performance of the ST-SMC, a comparison with a conventional PI controller is shown at the end of the paper. Measured results using hardware in the loop (HIL) to validate the results and their detailed discussion and analysis are included.

Model Predictive Control With Guaranteed Feasibility of Inequality Path Constraints.

This article concerns nonlinear model predictive control (MPC) with guaranteed feasibility of inequality path constraints (PCs). For MPC with PCs, the existing methods, such as direct multiple shooting, cannot guarantee feasibility of PCs because the PCs are enforced at finitely many time points only. Therefore, this article presents a novel MPC framework that is capable of not only achieving stability control but also guaranteeing feasibility of PCs during the rolling optimization stages of MPC. Under the above MPC framework, an algorithm is first proposed by applying the semi-infinite programming technique to the rolling optimization of MPC. However, it takes heavy computational time to achieve guaranteed feasibility of PCs. Therefore, to guarantee feasibility of PCs meanwhile effectively reducing the computation burden of the closed-loop system, an event-triggered sampling mechanism is constructed in the above path-constrained MPC algorithm. Moreover, sufficient conditions are given for asymptotic convergence of the closed-loop systems. Finally, the effectiveness of the proposed results is illustrated via a cart-damper-spring system.

Comparing different operating regimes of a Dicke quantum battery

In this paper, we consider a quantum battery, namely a miniaturized device able to store energy and release it on-demand. In the so-called Dicke configuration, it is made of [Formula: see text] two-level systems embedded into a cavity which plays the role of charger. In this context, we compare two different situations, namely: the resonant regime, where the energy of the two-level systems is the same as the one of the photons trapped in the cavity, and the off-resonant regime, where the photons are way more energetic. We observe that, while the energy stored per two-level system is comparable in the two cases, the average charging power, despite showing the same asymptotic superextensive scaling, is strongly suppressed in the latter. This analysis will help to orient future experimental implementations of these devices.

Nonlinear Wave Interaction of Nanorods Embedded in a Viscoelastic Medium

Abstract Purpose Nonlinear interactions between two acoustic waves in nanorods traveling at various wave numbers, group velocities, and frequencies are examined in this study. Methods The nonlinear equation of the nanorod in a viscoelastic medium is obtained using the theory of nonlocal elasticity. Furthermore, the multiple-scale expansion method is applied to study strongly dispersive, weakly nonlinear waves in a nonlocal viscoelastic medium. Using this expansion technique, we can derive the coupled nonlinear Schrödinger equations as the governing equations, which we solve as differential equations of some parameters by expanding the field quantities into an asymptotic series of the smallness parameter. Results We give the nonlinear plane wave solutions to these equations in several special cases. The plane wave solutions show how the wave amplitude affects the frequencies of nonlinear plane waves. Additionally, we show numerically how the real and imaginary parts of the group velocities and natural frequency of the system for a carbon nanotube in a viscoelastic medium are affected by the nonlocal, damping, and stiffness parameters.

Flexural-gravity wave interaction with undulating bottom topography in the presence of uniform current: An asymptotic approach

Using an asymptotic method, this article deals with flexural-gravity wave scattering with undulating bottom topography, including the effect of uniform currents. The interest in this problem lies in developing second-order solutions using the Fourier transform, which minimises the error gap between first and second-order solutions. The present method allows the physical processes involved in the sea-bed topography, uniform current, plate-covered surface, and wave interaction to be studied. Specifically, we observe Bragg resonance between the flexural-gravity waves and the bottom ripples, which are associated with the reflection of incident wave energy. We examine the effects of wave current and emphasise how crucial the asymptotic expansion method is to the emergence of the current response. We demonstrate that bottom topography dominates the effects of Bragg resonance for depth Froude numbers valued at 0.8 or less. Further, most reflected wave components have their frequencies shifted by the current, and wave action conservation causes reflected wave energy to be enhanced for following currents. Using the Joint North Sea Wave Observation Project spectrum and the discrete Fourier transform, the theory derived in the frequency domain is shown in the time domain to analyse wave propagation through the whole system.

Expansion in supercritical random subgraphs of expanders and its consequences

In 2004, Frieze, Krivelevich and Martin established the emergence of a giant component in random subgraphs of pseudo‐random graphs. We study several typical properties of the giant component, most notably its expansion characteristics. We establish an asymptotic vertex expansion of connected sets in the giant by a factor of . From these expansion properties, we derive that the diameter of the giant is whp , and that the mixing time of a lazy random walk on the giant is asymptotically . We also show similar asymptotic expansion properties of (not necessarily connected) linear‐sized subsets in the giant, and the typical existence of a large expander as a subgraph.

Asymptotic Series of the Associated Legendre Function with Respect to Seismic Surface Waves

The asymptotic series of the associated Legendre function is investigated for the angular degrees n=2-13 in the context of the amplitude and wavelength of seismic surface waves. The approximate formula for the associated Legendre function is used to determine the wavelength and phase velocity of free oscillations of the Earth and long-period surface waves. The approximate formula is derived from the first term of the asymptotic series and its error increases with decreasing n. In the present study, the effect of higher terms on the approximate formula is studied with respect to the amplitudes of the 1st term (a1), the 2nd term (a2), and the 3rd term (a3). For the colatitude angle θ of π/3≦θ≦2π/3, the amplitude ratios a2/a1 are approximately 3.5％-1.0％ for n=2-11 and are less than 1.0％ for n ≧ 12, while the amplitude ratios a3/a1 are approximately 0.4％ - 0.03％ for n=2-13. The effect of amplitudes of higher terms on the 1st term increases as the colatitude angle approaches the pole or the antipode. The wavelengths of the 1st term (λ1) , the 2nd term (λ2) , and the 3rd term (λ3) are in the sequence λ1 >λ2 >λ3. The wavelength ratiosλ2 /λ1 and λ3 >λ1 for n=2-13 are 0.71%-0.93% and 0.55%-0.87%, respectively. The relationship of the wavelengths between the different angular degrees may be expressed as λ n k =λn-1k+1, where n and k are respectively the angular degree and the ordinal number of the asymptotic series.

Reconstruction of elastic inclusions in layered medium

We consider the recovery of small inclusions in a two-layered and an arbitrary multi-layered medium for the elastic equation, respectively. We use layer potential techniques and asymptotic analysis to obtain asymptotic expansions of the perturbed elastic field in a two-layered and an arbitrary multi-layered medium, respectively. Furthermore, we show the uniqueness of the recovery of the locations and Lamé constants of small inclusions through a single measurement.

Boundary Layer Thickness and Friction Velocity by Symmetry Arguments.

We derive analytically for the first time the downstream evolution of the boundary layer thickness and the friction velocity of the zero-pressure-gradient turbulent boundary layer (ZPGTBL). Lie groups were used to derive the downstream evolution and to obtain the full set of the similarity variables and the leading-order similarity equations. An approximate leading-order solution was obtained using matched asymptotic expansions. The similarities and differences between ZPGTBL and turbulent channel flows in terms of the similarity equations are discussed to support the notion of leading-order universality of the near-wall layer.

On a finite sum of cosecants appearing in various problems

Finite trigonometric sums is a challenging and often quite difficult object of study. In this paper we investigate the finite sum of cosecants ∑csc⁡(φ+aπl/n), where the summation index l runs through 1 to n−1 and φ and a are arbitrary parameters, as well as several closely related sums, such as similar sums of a series of secants, of tangents and of cotangents. These trigonometric sums appear in various problems in mathematics, physics, and a variety of related disciplines. Their particular cases were fragmentarily considered in previous works, and it was noted that even a simple particular case ∑csc⁡(πl/n) does not have a closed–form, i.e. a compact summation formula (similar sums of sines, of cosines, of tangents, of cotangents and even of secants, if they exist, possess such expressions). In this paper, we derive several alternative representations for the above–mentioned sums, study their properties, relate them to many other finite and infinite sums, obtain their complete asymptotic expansions for large n and provide accurate upper and lower bounds (e.g. the typical relative error for the upper bound is lesser than 2×10−9 for n⩾10 and lesser than 7×10−14 for n⩾50, which is much better than the bounds we could find in previous works). Our researches reveal that these sums are deeply related to several special numbers and functions, especially to the digamma function (furthermore, as a by-product, we obtain several interesting summation formulæ for the digamma function). Asymptotical studies show that these sums may have qualitatively different behaviour depending on the choice of φ and a. In particular, as n increases some of them may become sporadically large and we identify the terms of the asymptotics responsible for such a behaviour. Finally, we also provide several historical remarks related to various sums considered in the paper. We show that some results in the field either were rediscovered several times or can easily be deduced from various known formulæ, including some formulæ dating back to the XVIIIth century.

Adaptive control for nonlinear systems with unknown actuator dynamics based on a novel extended Nussbaum function

AbstractIn this article, we introduced a novel definition for a class of extended Nussbaum functions that have looser requirements than the traditional Nussbaum function. The extended Nussbaum function achieves comparable performance to the traditional Nussbaum function while addressing the challenges of unknown control directions and time‐varying actuator faults. We then provide a specific example function based on the extended Nussbaum function definition. Unlike the traditional Nussbaum function, which oscillates with fixed frequency and constantly increasing amplitude, this example function constrains the amplitude and oscillates with decreasing frequency. Consequently, its amplitude and derivative remain bounded. The oscillation of the system caused by the too large amplitude of the traditional Nussbaum function can be avoided. Furthermore, this example function is constructed using portions of elliptic curve. As a result, it is continuous and differentiable, in contrast to the normal decreasing‐frequency Nussbaum function using trigonometric function, which is only continuous. The overall closed‐loop system's global stability and asymptotic convergence of system states are successfully achieved. Simulation results demonstrate the effectiveness of the proposed function.

Accelerated Gradient Approach For Deep Neural Network-Based Adaptive Control of Unknown Nonlinear Systems.

Recent connections in the adaptive control literature to continuous-time analogs of Nesterov's accelerated gradient method have led to the development of new real-time adaptation laws based on accelerated gradient methods. However, previous results assume that the system's uncertainties are linear-in-the-parameters (LIP). To compensate for non-LIP uncertainties, our preliminary results developed a neural network (NN)-based accelerated gradient adaptive controller to achieve trajectory tracking for nonlinear systems; however, the development and analysis only considered single-hidden-layer NNs. In this article, a generalized deep NN (DNN) architecture with an arbitrary number of hidden layers is considered, and a new DNN-based accelerated gradient adaptation scheme is developed to generate estimates of all the DNN weights in real-time. A nonsmooth Lyapunov-based analysis is used to guarantee the developed accelerated gradient-based DNN adaptation design achieves global asymptotic tracking error convergence for general nonlinear control affine systems subject to unknown (non-LIP) drift dynamics and exogenous disturbances. A comprehensive set of simulation studies are conducted on a two-state nonlinear system, a robotic manipulator, and a complex 20-D nonlinear system to demonstrate the improved performance of the developed method. Our simulation studies demonstrate enhanced tracking and function approximation performance from both DNN architectures and accelerated gradient adaptation.

Resource-efficient quantum principal component analysis

Principal component analysis (PCA) is an important dimensionality reduction method in machine learning and data analysis. Recently, the quantum version of PCA has been established to diagonalize quantum states. Although these quantum algorithms promise quantum advantages, they require substantial resources beyond the reach of state-of-the-art quantum technologies. This work aims to reduce resource requirements and improve the efficiency of quantum PCA. Assuming that the quantum state is accessed through a purified quantum query model and a sampling model, we propose quantum algorithms that use minimal resource requirements for ancillary qubits to reveal properties of eigenvectors and eigenvalues of a state. In particular, we show that estimating eigenvalue λ with error ε and success probability larger than λ(1−η) requests a query complexity O~(ϵ−1) and a sample complexity O~(ϵ−2η−1) , respectively. To our knowledge, our result is the first quantum speedup that achieves asymptotic linear scaling in 1/ϵ for quantum PCA. As applications, we discuss estimating the minimum relative entropy of entanglement of bipartite pure-states and performing quantum state discrimination tasks. We show that quantum speedups are maintained when the pure state has a low Schmidt number and states of discrimination have a low rank. This study opens up a new quantum PCA method for high-dimensional quantum data analysis and discusses its application in quantum information processing tasks.