Effect Of Parameter Perturbations Research Articles

Dynamic Performance Analysis of Semiactive Vehicle ISD Suspension Based on the Power-Driven-Damper Strategy

In this paper, the vehicle ISD (inerter-spring-damper) suspension and power-driven-damper control strategy are combined to the suspension design, and the power-driven-damper semiactive ISD suspension is proposed. The dynamic models of the passive suspension S1 and two semiactive ISD suspensions S2 and S3 are established. Based on the port-controlled Hamiltonian theory, the power-driven-damper semiactive control strategy is designed by analyzing the power transfer of suspension S3. Then, the parameters of the two models are optimized by the particle swarm optimization algorithm, and the optimization results show that the suspension S3 has better performance. The influence of the semiactive damping coefficient, the spring stiffness, and the inertance on the vibration suppression performance is investigated based on the suspension S3. The effect of parameter perturbation on power-driven-damper semiactive vehicle ISD suspension illustrates that the designed semiactive vehicle ISD suspension has better ride comfort in a wider range frequency and good robust performance.

Dynamic In Silico Model of Type 2 Diabetes Treated With Metformin Combined With Exercise: A Sobol Sensitivity Analysis

Background: The widespread adoption of a sedentary lifestyle characterized by the consumption of a high-energy diet combined with a lack of physical activity has resulted in a rise in the prevalence of metabolic disorders like type 2 diabetes (T2D) globally. The data published by the International Diabetes Federation (IDF) every year catches the attention of the world. Insulin resistance results in the dysregulation of the control mechanism that leads to T2D. In addition to metformin as a globally prescribed drug, physical exercise is recommended for the treatment of T2D as an alternative therapy. In today’s systems biology era, sensitivity analysis plays a major role in providing deeper insights into the metabolic insulin signalling pathways (ISPs). Methods: Here, the complex in silico dynamic model of the effect of alternative therapies on T2D was examined. In order to obtain more quantitative information about the signal transduction network of the ISPs and their interaction, a global sensitivity analysis of the in silico model was carried out using the MATLAB tool. Results: This study sheds light on the effect of parameter perturbation on the ISP via GLUT4 translocation in T2D patients performing physical exercise and using metformin. It also allows for the selection of a variety of parameters for in vivo or in vitro studies in the future on the basis of the impact of parameter variations on each component of the pathway. Conclusion: The Sobol index for each condition was noted after introducing perturbations in the most sensitive parameters. These results can provide experimental guidance on how the variations in model inputs have affected the model outputs.

Data-Driven Iterative Learning Predictive Control for Power Converters

This letter proposes a data-driven iterative learning predictive control architecture for power converters. The main objectives of this letter are to enhance the robustness and remain the high performance of finite control-set model predictive control (FCS-MPC) under unmodeled dynamics and parameter mismatch conditions. More specifically, an iterative dynamic linearization technique is utilized to equivalently reformulate the nonlinear power converter system at each operating point. Based on this, a model-free adaptive control scheme is presented to iteratively determine the optimal control actions. Due to the incorporation of iterative learning control and data-driven concept into the FCS-MPC framework, the effect of parameter perturbations can be alleviated in the proposed method, while creating a positive effect on the tracking error. Finally, a convergence analysis is provided and experimental investigations on a three-level neutral-point-clamped (NPC) converter confirm the effectiveness of the proposed method.

Grid connection technique based onμtheory for a two‐stage PV structure

There is greater flexibility in selecting the direct-current (DC) bus voltage in a two-stage photovoltaic (PV) grid-connected structure than in a single-stage one. The independent DC/DC and DC/alternating current (AC) control in traditional control methods changes the duty cycle of the first-stage DC/DC, which affects the control of the secondary DC/AC and even stabilises the DC bus voltage between the two stages. Therefore, two-stage converters should be considered and controlled in a unified manner. Here, the authors introduce a robust μ value control method based on the hybrid sensitivity theory to control a two-stage PV power generation system. Considering the effect of parameter perturbation on the modelling of the DC/DC average state model, the proposed method improves the DC bus voltage stability of a PV system and the power quality of the grid-connected current while achieving the maximum power point tracking performance of the PV. MATLAB simulations using three PV operating conditions demonstrate that the proposed control method can effectively reduce the fluctuation of the DC bus voltage between the two stages, the harmonic distortion rate of the grid-connected current, and the power oscillation time when the working conditions change.

Robust Power Control for OFDM-Based Cognitive Radio Networks with QoS Guarantee

Traditional power control schemes in Orthogonal Frequency Division Multiplexing based Cognitive Radio Networks (CRNs) are achieved under perfect channel state information (e.g., exact parameter information). Due to the effect of channel estimation errors and feedback delays, however, channel uncertainties are inevitable in practical CRNs. In this paper, considering bounded uncertainties of channel gains, a robust power control algorithm is proposed to minimize the total transmit power of Secondary Users (SUs) subject to interference power constraint of primary user and quality of service constraint of SU where the non-convex optimization problem is converted into a convex optimization problem that is solved by using robust optimization theory. Computer experiments demonstrate the effectiveness of the proposed algorithm by comparing with the non-robust algorithm in the aspect of suppressing the effect of parameter perturbation.

Sensitivity analysis of continuous-time models for ecological and evolutionary theories

Sensitivity analyses are of paramount importance in ecological and evolutionary theories, but their application to continuous time models has been virtually ignored from these fields. We present a simple and general method that makes this analysis possible for any model specified by a system of ordinary differential equations, using the direct method from mathematical theory. The resulting analysis may be used to study the effect of parameter perturbation on the whole trajectories of the state variables as well as for deriving the sensitivity of composite metrics such as the population growth rate. We also present methods for analyzing the sensitivity of discrete events within a continuous-time framework, such as the age at maturation, where timing may be affected by the perturbation. These methods are applied to a model for the energetics of individual growth, reproduction, and mortality. The method is versatile and can be applied to study transient as well as asymptotic dynamics, and its application may benefit many fields of ecology and evolution.

Nonlinear Ensemble Parameter Perturbation for Climate Models

Abstract Model parameters can introduce significant uncertainties in climate simulations. Sensitivity analysis provides a way to quantify such uncertainties. Existing sensitivity analysis methods, however, cannot estimate the maximum sensitivity of the simulated climate to perturbations in multiple parameters. This study proposes the concept of nonlinear ensemble parameter perturbation (NEPP), which is independent of model initial conditions, to estimate the maximum effect of parameter perturbations on simulating Earth’s climate. The NEPP is obtained by solving a maximization problem, whose cost function is defined by the maximum deviation of a unique ensemble of short-term predictions with large enough members caused by parameter perturbations and whose optimal solution is obtained by an ensemble-based gradient approach. This method is used to investigate the effects of NEPP on the climate of the Lorenz-63 model and a complex climate model, the Grid-Point Atmospheric Model of IAP LASG, version 2 (GAMIL2). It is found that the NEPP is capable of estimating the maximum change in climate simulation caused by perturbations in multiple parameters when the Lorenz-63 model is used. With a low computational cost, the NEPP can cause remarkable changes in the climatology of GAMIL2. The results also illustrate that the effects of parameter perturbations on short-term weather predictions and those on long-term climate simulations are correlated.

Mixed H<SUB align="right">2/H<SUB align="right">∞ control for electromagnetic active suspension

This paper presents a mixed H2/H∞control strategy under main/ torque-tracking loop structure for an electromagnetic active suspension. First, the modelling of the electromagnetic actuator is conducted. Then, the main/torque-tracking loop structure is proposed, in which the main loop calculates the demand torque for electromagnetic actuators based on a full-car suspension model, using mixed H2/H∞ control strategy. Body acceleration, suspension working space, dynamic tyre displacement and demand torque are considered as H2 performance indices, and suspension working space and demand torque are also H∞ performance indices. The torque-tracking loop applies hysteresis current control for electromagnetic actuators to track the demand torque calculated by the main loop. Finally, simulation results demonstrate that the designed controller can greatly improve ride comfort and is less conservative than H∞ controller. The effects of parameter perturbations and dynamic response of suspension system are also studied, indicating that good robustness can be achieved.

Asymptotic parameter sensitivity for polynomial dynamical systems

The effect of parameter perturbations on the trajectories of dynamical systems has been extensively studied in the literature. The effect of parameter values on the number and type of rest-points in a dynamical system has received less attention. For systems that exhibit input multiplicities, the sensitivity of system trajectories to parameter values can be discontinuous when state trajectories are near the boundary between the basins of different attractors. In this work, a computational scheme for conducting a single and joint parameter sensitivity analysis for systems with multiple steady states is presented. The proposed approach is computationally efficient and relies on algebraic geometric tools to obtain a simplified polynomial representation of the system at steady state. The proposed approach is applied to a model of the human hypothalamic-pituitary-adrenal axis and is shown to be relevant to the development of mechanistic models of chronic diseases.

Sensitivity analysis of equilibrium in density‐dependent matrix population models

AbstractWe consider the effects of parameter perturbations on a density‐dependent population at equilibrium. Such perturbations change the dominant eigenvalue λ of the projection matrix evaluated at the equilibrium as well as the equilibrium itself. We show that, regardless of the functional form of density dependence, the sensitivity of λ is equal to the sensitivity of an effective equilibrium density , which is a weighted combination of the equilibrium stage densities. The weights measure the contributions of each stage to density dependence and their effects on demography. Thus, is in general more relevant than total density, which simply adds all stages regardless of their ecological properties. As log λ is the invasion exponent, our results show that successful invasion will increase , and that an evolutionary stable strategy will maximize . Our results imply that eigenvalue sensitivity analysis of a population projection matrix that is evaluated near equilibrium can give useful information about the sensitivity of the equilibrium population, even if no data on density dependence are available.

FREE AND FORCED RESPONSE OF MISTUNED LINEAR CYCLIC SYSTEMS: A SINGULAR PERTURBATION APPROACH

The effect of small deterministic parameter perturbations on the forced response of nearly periodic structures with cyclic symmetry is investigated. The general methodology developed herein is applicable to anyn-degree-of-freedom, strongly coupled cyclic system with two arbitrary and independent variations in system parameters that destroy the cyclic symmetry. The specific system studied may be regarded as a simplified model of a strongly coupled bladed disk assembly. Singular perturbation methods along with modal expansion analysis are applied to gain a physical insight into the effects of parameter perturbations on the eigenvalues, the eigenvectors as well as the forced response amplitudes. The study shows that, under appropriate conditions, the splitting and veering of eigenvalues due to mistunings or small parameter variations increases the amplitude of vibration of some blades significantly compared to what would be predicted by an analysis of the perfectly tuned system. Furthermore, modal bifurcations lead to uneven vibration amplitudes irrespective of the stiffness of the coupling springs. The variations in blade amplitudes are also found to be strongly dependent on damping, and the type of engine order excitation applied to the system for the same set of mistuning parameters.

Effects of parametric perturbations on the onset of chaos in the Josephson-junction model: Theory and analog experiments.

We propose an analog apparatus simulating a standard Josephson junction, which allows easy experimental manipulations and measurements. We center our attention on the onset of chaotic responses, in particular on its dependence on the different physical parameters, and we obtain quite good agreement with other existing data. Great attention is also paid to compare the results with the theoretical threshold of chaos we deduce from Melnikov's method: The agreement is globally as good as possible, taking into account the approximations and the validity of the method; in particular, we obtain a description of the behavior of the threshold for small frequencies, and of the important role played by the dc bias term. We also discuss, both theoretically and experimentally, the case of a Josephson junction to which a periodic modulating term has been added.

On the synthesis of the least sensitive control

Publisher Summary This chapter discusses the synthesis of the least sensitive control. It presents the concept of sensitivity in the synthesis of control policy when the controlled process is subjected to unpredictable environmental changes. It discusses the possible effects of small parameter perturbations, the missing of the desired state of the process and change of the process performance index. In the subsequent considerations these effects are used as a quantitative measure of process performance. By imposing the requirement that the control policy should be chosen so as to minimize the effects of parameter perturbations, a reduced sensitivity can be attained. The compound control that is obtained by combining the sensitivity criterion with some other criteria of optimality is optimal and less sensitive to small parameter variations.