Nonlinear Drift Term Research Articles

General hazard-type scaling of abandonment time distribution for a G/Ph/n+GI queue in the Halfin–Whitt heavy-traffic regime

We consider the diffusion approximation of a G/Ph/n queue with customer abandonment in the Halfin---Whitt heavy-traffic regime and extend the conventional locally Lipschitz hazard-type scaling of abandonment time distribution to a more general scaling in the study. Under that new scaling of abandonment, not only the non-locally Lipschitz hazard-type case such as the non-locally bounded hazard-rate scaling which has never been solved to date, but also a wider range of abandonment time distributions including the non-absolutely continuous ones becomes subject to the analysis of diffusion approximation. Due to the general character of our scaling scheme of abandonment time, the stochastic equation for the limit of C-tight, scaled, and centered customer-count processes contains a nonlinear drift term as the limit of abandonment-count process, which does not satisfy the local Lipschitz condition so that the continuous mapping method in the literature of this area is invalid in our case. Instead, applying the Girsanov transformation to the localized multidimensional equation satisfied by the limit of customer-count process in multiphase of service time, we show the uniqueness in law of the solution to establish the desired diffusion approximation.

Distributed practical output tracking of high-order stochastic multi-agent systems with inherent nonlinear drift and diffusion terms

This paper investigates the distributed tracking problem for a class of high-order stochastic nonlinear multi-agent systems where the subsystem of each agent is driven by nonlinear drift and diffusion terms. For the case where the graph topology is directed and the leader is the neighbor of only a small portion of followers, a new distributed integrator backstepping design method is proposed, and distributed tracking control laws are designed, which can effectively deal with the interactions among agents and coupling terms. By using the algebra graph theory and stochastic analysis, it is shown that the closed-loop system has an almost surely unique solution on [0,∞), all the states of the closed-loop system are bounded in probability, and the tracking errors can be tuned to arbitrarily small with a tunable exponential converge rate. The efficiency of the tracking controller is demonstrated by a simulation example.

Empirical Fokker-Planck-based test of stationarity for time series.

We propose a test of stationarity based on the drift coefficients of the Langevin type and the associated Fokker-Planck equations. The test relies on the estimation of the drift coefficients of the underlying probability densities and posits that a time series is nonstationary if the estimated drift term is a nonlinear function of the random variable of the observed time series and the Markov property holds. We provide ample empirical evidence that demonstrates that well- known stationary systems give rise to linear estimates of the drift coefficients, whereas nonstationary time series exhibit nonlinear estimates of the drift term. This does not, indeed, imply that a nonlinear drift term in the Fokker-Planck equation of a dynamic stochastic process causes nonstationarity.

Local null controllability for a chemotaxis system of parabolic–elliptic type

We consider the controllability of a chemotaxis system of parabolic–elliptic type. By linearizing the nonlinear system into two separate linear equations, we can bypass the obstacle caused by the nonlinear drift term and establish local null controllability of the original nonlinear system. This approach is different from the usual method for dealing with coupled parabolic systems.

Distributed Tracking of Multi-Agent Systems with High-Order Stochastic Nonlinear Dynamics

The distributed tracking problem is investigated for a class of multi-agent systems with high-order stochastic nonlinear dynamics where the subsystem of each agent is driven by inherent nonlinear drift and diffusion terms. For the case where the graph topology is directed and the leader is the neighbor of only a small portion of followers, a new distributed integrator backstepping design method is proposed, and distributed tracking control laws are designed to ensure that the closed-loop system has an almost surely unique solution on [0, ∞), all the states of the closed-loop system are bounded in probability, and the tracking errors can be tuned to arbitrarily small with a tunable exponential converge rate. The efficiency of the tracking controller is demonstrated by a simulation example.

Driven translocation of a polymer: Fluctuations at work

The impact of thermal fluctuations on the translocation dynamics of a polymer chain driven through a narrow pore has been investigated theoretically and by means of extensive Molecular-Dynamics (MD) simulation. The theoretical consideration is based on the so-called velocity Langevin (V-Langevin) equation which determines the progress of the translocation in terms of the number of polymer segments, $s(t)$, that have passed through the pore at time $t$ due to a driving force $f$. The formalism is based only on the assumption that, due to thermal fluctuations, the translocation velocity $v=\dot{s}(t)$ is a Gaussian random process as suggested by our MD data. With this in mind we have derived the corresponding Fokker-Planck equation (FPE) which has a nonlinear drift term and diffusion term with a {\em time-dependent} diffusion coefficient $D(t)$. Our MD simulation reveals that the driven translocation process follows a {\em super}diffusive law with a running diffusion coefficient $D(t) \propto t^{\gamma}$ where $\gamma < 1$. This finding is then used in the numerical solution of the FPE which yields an important result: for comparatively small driving forces fluctuations facilitate the translocation dynamics. As a consequence, the exponent $\alpha$ which describes the scaling of the mean translocation time $<\tau>$ with the length $N$ of the polymer, $<\tau> \propto N^{\alpha}$ is found to diminish. Thus, taking thermal fluctuations into account, one can explain the systematic discrepancy between theoretically predicted duration of a driven translocation process, considered usually as a deterministic event, and measurements in computer simulations. In the non-driven case, $f=0$, the translocation is slightly subdiffusive and can be treated within the framework of fractional Brownian motion (fBm).

Existence and Regularity of the Density for Solutions to Semilinear Dissipative Parabolic SPDEs

We prove existence and smoothness of the density of the solution to a nonlinear stochastic heat equation on \(L^2(\mathcal{O})\) (evaluated at fixed points in time and space), where \(\mathcal{O}\) is an open bounded domain in ℝd with smooth boundary. The equation is driven by an additive Wiener noise and the nonlinear drift term is the superposition operator associated to a real function which is assumed to be (maximal) monotone, continuously differentiable, and growing not faster than a polynomial. The proof uses tools of the Malliavin calculus combined with methods coming from the theory of maximal monotone operators.

A Nearly Exact Reformulation of the Girsanov Linearization for Stochastically Driven Nonlinear Oscillators

The Girsanov linearization method (GLM), proposed earlier in Saha, N., and Roy, D., 2007, “The Girsanov Linearisation Method for Stochastically Driven Nonlinear Oscillators,” J. Appl. Mech.,74, pp. 885–897, is reformulated to arrive at a nearly exact, semianalytical, weak and explicit scheme for nonlinear mechanical oscillators under additive stochastic excitations. At the heart of the reformulated linearization is a temporally localized rejection sampling strategy that, combined with a resampling scheme, enables selecting from and appropriately modifying an ensemble of locally linearized trajectories while weakly applying the Girsanov correction (the Radon–Nikodym derivative) for the linearization errors. The semianalyticity is due to an explicit linearization of the nonlinear drift terms and it plays a crucial role in keeping the Radon–Nikodym derivative “nearly bounded” above by the inverse of the linearization time step (which means that only a subset of linearized trajectories with low, yet finite, probability exceeds this bound). Drift linearization is conveniently accomplished via the first few (lower order) terms in the associated stochastic (Ito) Taylor expansion to exclude (multiple) stochastic integrals from the numerical treatment. Similarly, the Radon–Nikodym derivative, which is a strictly positive, exponential (super-) martingale, is converted to a canonical form and evaluated over each time step without directly computing the stochastic integrals appearing in its argument. Through their numeric implementations for a few low-dimensional nonlinear oscillators, the proposed variants of the scheme, presently referred to as the Girsanov corrected linearization method (GCLM), are shown to exhibit remarkably higher numerical accuracy over a much larger range of the time step size than is possible with the local drift-linearization schemes on their own.

Optimal stopping under ambiguity in continuous time

We develop a theory of optimal stopping problems under ambiguity in continuous time. Using results from (backward) stochastic calculus, we characterize the value function as the smallest (nonlinear) supermartingale dominating the payoff process. For Markovian models, we derive an adjusted Hamilton–Jacobi–Bellman equation involving a nonlinear drift term that stems from the agent’s ambiguity aversion. We show how to use these general results for search problems and American options.

Fokker–Planck model for computational studies of monatomic rarefied gas flows

In this study, we propose a non-linear continuous stochastic velocity process for simulations of monatomic gas flows. The model equation is derived from a Fokker–Planck approximation of the Boltzmann equation. By introducing a cubic non-linear drift term, the model leads to the correct Prandtl number of 2/3 for monatomic gas, which is crucial to study heat transport phenomena. Moreover, a highly accurate scheme to evolve the computational particles in velocity- and physical space is devised. An important property of this integration scheme is that it ensures energy conservation and honours the tortuosity of particle trajectories. Especially in situations with small to moderate Knudsen numbers, this allows to proceed with much larger time steps than with direct simulation Monte Carlo (DSMC), i.e. the mean collision time not necessarily has to be resolved, and thus leads to more efficient simulations. Another computational advantage is that no direct collisions have to be calculated in the proposed algorithm. For validation, different micro-channel flow test cases in the near continuum and transitional regimes were considered. Detailed comparisons with DSMC for Knudsen numbers between 0.07 and 2 reveal that the new solution algorithm based on the Fokker–Planck approximation for the collision operator can accurately predict molecular stresses and heat flux and thus also gas velocity and temperature profiles. Moreover, for the Knudsen Paradox, it is shown that good agreement with DSMC is achieved up to a Knudsen number of about 5.

Two-Stage Extended Kalman Filters with Derivative-Free Local Linearizations

This paper proposes a derivative-free two-stage extended Kalman filter (2-EKF) especially suited for state and parameter identification of mechanical oscillators under Gaussian white noise. Two sources of modeling uncertainties are considered: (1) errors in linearization, and (2) an inadequate system model. The state vector is presently composed of the original dynamical/parameter states plus the so-called bias states accounting for the unmodeled dynamics. An extended Kalman estimation concept is applied within a framework predicated on explicit and derivative-free local linearizations (DLL) of nonlinear drift terms in the governing stochastic differential equations (SDEs). The original and bias states are estimated by two separate filters; the bias filter improves the estimates of the original states. Measurements are artificially generated by corrupting the numerical solutions of the SDEs with noise through an implicit form of a higher-order linearization. Numerical illustrations are provided for a few ...

On Factorization of the Nonlinear Drift Term for SDRE Approach

AbstractThis study presents necessary and sufficient conditions for the existence of a constant matrix A that satisfies Ax = f, (A, B) is stabilizable and (A, C) is observable (resp., detectable), where x and f are two constant vectors with x ≠ 0, while B and C are two constant matrices with approximate dimension. The conditions presented are very easy to check and a set of such feasible matrices A are also provided explicitly when the existence conditions are satisfied. In practical applications, this study may cooperate with the state-dependent Riccati equation (SDRE) implementation at those states where the SDRE scheme fails to operate, yet the presented existence conditions hold there.

Adaptive output feedback control for uncertain nonholonomic chained systems

An adaptive output feedback control was proposed to deal with a class of nonholonomic systems in chained form with strong nonlinear disturbances and drift terms. The objective was to design adaptive nonlinear output feedback laws such that the closed-loop systems were globally asymptotically stable, while the estimated parameters remained bounded. The proposed systematic strategy combined input-state-scaling with backstepping technique. The adaptive output feedback controller was designed for a general case of uncertain chained system. Furthermore, one special case was considered. Simulation results demonstrate the effectiveness of the proposed controllers.

Critical fluctuations and anomalous transport in soft Yukawa-Langevin systems

Simulation of a Langevin-dynamics model demonstrates emergence of critical fluctuations and anomalous grain transport which have been observed in experiments on "soft" quasi-two-dimensional dusty plasma clusters. Our model does not contain external drive or plasma interactions that serve to drive the system away from thermodynamic equilibrium. The grains are confined by an external potential, interact via static Yukawa forces, and are subject to stochastic heating and dissipation from neutrals. One remarkable feature is emergence of leptokurtic probability distributions of grain displacements xi(tau) on time scales tau < tau(Delta), where tau(Delta) is the time at which the standard deviation sigma(tau) identical with (xi(2)(tau))(1/2) approaches the mean intergrain distance Delta. Others are development of humps in the distributions on multiples of Delta , anomalous Hurst exponents, and transitions from leptokurtic toward Gaussian displacement distributions on time scales tau > tau(Delta). The latter is a signature of intermittency, here interpreted as a transition from bursty transport associated with hopping on intermediate time scales to vortical flows on longer time scales. These intermittency features are quantitatively modeled by a single-particle Itô-Langevin stochastic equation with a nonlinear drift term.

Extended Kalman filters using explicit and derivative-free local linearizations

We propose three variants of the extended Kalman filter (EKF) especially suited for parameter estimations in mechanical oscillators under Gaussian white noises. These filters are based on three versions of explicit and derivative-free local linearizations (DLL) of the non-linear drift terms in the governing stochastic differential equations (SDE-s). Besides a basic linearization of the non-linear drift functions via one-term replacements, linearizations using replacements through explicit Euler and Newmark expansions are also attempted in order to ensure higher closeness of true solutions with the linearized ones. Thus, unlike the conventional EKF, the proposed filters do not need computing derivatives (tangent matrices) at any stage. The measurements are synthetically generated by corrupting with noise the numerical solutions of the SDE-s through implicit versions of these linearizations. In order to demonstrate the effectiveness and accuracy of the proposed methods vis-à-vis the conventional EKF, numerical illustrations are provided for a few single degree-of-freedom (DOF) oscillators and a three-DOF shear frame with constant parameters.

Symmetry Operators for the Fokker-Plank-Kolmogorov Equation with Nonlocal Quadratic Nonlinearity

The Cauchy problem for the Fokker-Plank-Kolmogorov equation with a nonlo- cal nonlinear drift term is reduced to a similar problem for the correspondent linear equa- tion. The relation between symmetry operators of the linear and nonlinear Fokker-Plank- Kolmogorov equations is considered. Illustrative examples of the one-dimensional symmetry operators are presented.

Output feedback exponential stabilization of uncertain chained systems

This paper deals with chained form systems with strongly nonlinear disturbances and drift terms. The objective is to design robust nonlinear output feedback laws such that the closed-loop systems are globally exponentially stable. The systematic strategy combines the input-state-scaling technique with the so-called backstepping procedure. A dynamic output feedback controller for general case of uncertain chained system is developed with a filter of observer gain. Furthermore, two special cases are considered which do not use the observer gain filter. In particular, a switching control strategy is employed to get around the smooth stabilization issue (difficulty) associated with nonholonomic systems when the initial state of system is known.

Quantum random walks and piecewise deterministic evolutions.

In the continuous space and time limit, we show that the probability density to find the quantum random walk (QRW) driven by the Hadamard "coin" solves a hyperbolic evolution equation similar to the one obtained for a random two-velocity evolution with spatially inhomogeneous transition rates between the velocity states. In spite of the presence of a nonlinear drift term, it is remarkable that the QRW position can easily be described in simple analytical terms. This allows us to derive the quadratic time dependence of the variance typical for the QRW.

Robust adaptive control of nonholonomic systems with uncertainties

Robust adaptive control of nonholonomic systems in chained form with linearly parameterized and strongly nonlinear disturbance and drift terms is dicussed. The novelty of the proposed method is a combined use of the state-scaling and the back-stepping procedure.

Modeling the term structure of interest rates with general short-rate models

Abstract. We propose a model of the term structure of interest rates with generally specified processes of the short-rate. As documented by many studies, it is important to model the short-rate generally to capture its observed behavior. This adequate specification is essential for term structure models to explain yields accurately. With such general conditions, however, term structure models cannot be obtained explicitly. In this paper we derive an analytical model of the term structure by locally approximating a short-rate process with nonlinear drift and diffusion terms. The empirical analysis confirms that the proposed model outperforms the existing affine term structure model.