Modeling and control of a novel pressure regulation mechanism for common rail fuel injection systems

This paper presents the modeling and control of a novel pressure regulation mechanism for the common rail (CR) fuel injection system of internal combustion engines (ICE). The pressure pulsations inside the common rail caused by the incoming and outgoing flows negatively affect the accuracy of both injected fuel quantities and flow rates. The objective of this work is to design a new regulating mechanism to suppress the pressure pulsation in the rail. We first present the one-dimensional distributed model for the common rail developed by using fluid flow equations, which can capture the distributed dynamics of the pressure pulsations in the rail and validating it with a physics based model developed in AMESim®. We then propose the concept of an active fluid storage device like a piezoelectric actuator (PZT) to minimize the pressure fluctuations. The location of the actuator on the common rail has also been evaluated to maximize its effect. The periodic nature of the injection event due to the stroke by stroke engine operation generates pressure pulsations in the rail which are periodic when represented in the rotational angle domain. To leverage this unique dynamic phenomenon we design a time-varying internal model-based controller to compensate the pressure pulsations.     

最优数学模型筛选法确定流量系数的经验公式

应用简单的最小二乘回归分析原理对多个数学模型进行分析,得到了水利工程中有坎宽顶堰流量系数的经验公式,并对其精度进行了讨论.对指导实际工程意义重大.     

Actuator fault detection and estimation for the Lur’e differential inclusion system

This paper deals with actuator fault detection and estimation for the Lur’e differential inclusion system. An adaptive full-order observer is used to detect the occurrence of the actuator fault. Then, based on a reduced-order observer, an approach to estimate the actuator fault is presented. A simulation of rotor system is given to illustrate the effectiveness of the proposed method.     

Modeling the asymmetry in traffic flow (b): Macroscopic approach

In [H. Xu, H. Liu, H. Gong, Modeling the asymmetry in traffic flow (a): microscopic approach, J. Appl. Math. Model. (submitted for publication)], the asymmetric characteristic of traffic flow has been studied from a microscopic approach through the modeling of car-following behavior. This paper further discusses the asymmetric traffic flow modeling at the macroscopic scale. The microscopic asymmetric full velocity difference model is extended to a continuum traffic flow model to study the anisotropic characteristic and diffusive influence under various traffic conditions. In order to accurately solve the mathematical problem, a weighted essentially no-oscillatory (WENO) approach is applied. The performance of the model is then demonstrated through thorough evaluation against select classic models and field data. The macroscopic model is the first of its kind that is directly developed from an asymmetric car-following approach. The results show that the model is able to present many complex traffic phenomena observed in the field such as shock waves, rarefaction waves, stop-and-go waves and local cluster effects at a better level of accuracy than most of the existing models.     

一类不确定非线性系统的执行器故障模糊容错控制

针对一类状态不完全可测的不确定非线性系统,研究了带有执行器故障的容错控制问题.采用 T-S模型对非线性系统进行模糊建模,利用并行分布补偿(PDC)算法设计了状态现潮器和基于状态现 潮器的客错控制,给出了保证该模糊容错控制系统稳定的充分条件.根据李雅普诺夫稳定性理论和线性 矩阵不等式(LMI),证明了所提出的模糊容错控制方法不但使得模糊控制系统渐近稳定,而且能够取得 H∞性能指标.计算机仿真结果进一步验证了所提出方法的正确性.     

Adjustable dimension descriptor observer based fault estimation for switched nonlinear systems with partially unknown nonlinear dynamics

This paper focuses on the fault estimation problem for switched systems with partially unknown nonlinear dynamics, actuator and sensor faults, simultaneously. The fault estimation observers are constructed, in which the observer dimension is not fixed and can be selected in a certain range. Both the disturbance decoupling and disturbance attenuation are considered, where the unknown nonlinear dynamics can be decoupled and the effect of modeling error and measurement disturbance is attenuated. Based on the average dwell time and the piecewise Lyapunov function, the observer parameter matrices can be calculated by solving LMIs and matrix equations. Finally, two examples are listed to verify the proposed fault estimation approach.     

Mathematical modelling of a diesel common-rail system

In diesel common-rail systems, the exact knowledge of the injection pressure is important to accurately control the injected diesel mass and thus the combustion process. This paper focuses on the mathematical modelling of the hydraulic and mechanical components of a common-rail system in order to describe the dynamics of the diesel rail pressure. Based on this model, an average model is derived to reduce the model complexity and to allow for a fast calculation of the mass flow into the rail for different crank shaft revolution speeds and openings of the fuel metering unit. The main purpose of this average model is to serve as a basis for a model-based (non-linear) controller design. The stationary accuracy of the models is validated by means of measurement data.     

Forecasting seasonal time series data: a Bayesian model averaging approach

A flexible Bayesian periodic autoregressive model is used for the prediction of quarterly and monthly time series data. As the unknown autoregressive lag order, the occurrence of structural breaks and their respective break dates are common sources of uncertainty these are treated as random quantities within the Bayesian framework. Since no analytical expressions for the corresponding marginal posterior predictive distributions exist a Markov Chain Monte Carlo approach based on data augmentation is proposed. Its performance is demonstrated in Monte Carlo experiments. Instead of resorting to a model selection approach by choosing a particular candidate model for prediction, a forecasting approach based on Bayesian model averaging is used in order to account for model uncertainty and to improve forecasting accuracy. For model diagnosis a Bayesian sign test is introduced to compare the predictive accuracy of different forecasting models in terms of statistical significance. In an empirical application, using monthly unemployment rates of Germany, the performance of the model averaging prediction approach is compared to those of model selected Bayesian and classical (non)periodic time series models.     

Detection,isolation and identification of multiple actuator and sensor faults in nonlinear dynamic systems: Application to a waste water treatment process

The goal in many fault detection and isolation schemes is to increase the isolation and identification speed. This paper, presents a new approach of a nonlinear model based adaptive observer method, for detection, isolation and identification of actuator and sensor faults. Firstly, we will design a new method for the actuator fault problem where, after the fault detection and before the fault isolation, we will try to estimate the output of the instrument. The method is based on the formation of nonlinear observer banks where each bank isolates each actuator fault. Secondly, for the sensor problem we will reformulate the system by introducing a new state variable, so that an augmented system can be constructed to treat sensor faults as actuator faults. A method based on the design of an adaptive observers’ bank will be used for the fault treatment. These approaches use the system model and the outputs of the adaptive observers to generate residues. Residuals are defined in such way to isolate the faulty instrument after detecting the fault occurrence. The advantages of these methods are that we can treat not only single actuator and sensor faults but also multiple faults, more over the isolation time has been decreased. In this study, we consider that only abrupt faults in the system can occur. The validity of the methods will be tested firstly in simulation by using a nonlinear model of waste water treatment process with and without measurement noise and secondly with the same nonlinear model but by using this time real data.     

A Soft-Computing Algorithm-based Cognitive Observer for an Elastic Robotic Arm

This paper proposes the use of Support Vector Machine (SVM) algorithm for modeling and states estimation of an elastic robotic arm. Due to the complexity of the elastic robotic arm, an accurate mathematical model and large number of sensors are needed to achieve accurate estimation. Initially, it is assumed that all system states are measurable for a short period of time. Additionally, the system output and input signals are being continuously measured. The modeling module builds two models, the internal model which will capture the dynamics of the elastic robotic arm and the data-driven observer model which will estimate the system states. The internal model and data-driven observer are obtained based on the experimental measurements using SVM algorithm in contrast to classical methods that uses the mathematical system model to drive an observer. The internal model is able to make multi-steps ahead predictions of all system states; therefore it can be used to generate a suitable control strategy. Once the models are ready, the main sensors can be removed or turned off. The proposed modeling module will eliminate the need for mathematical modeling and reduces the number of permanent sensors needed. If the main sensors are removed completely, the hardware price can be radically reduced. The simulation result demonstrates the efficiency and high performance of the modeling module. (© 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Nonlinear Modeling and Tracking Control of a Hydraulic Rotary Vane Actuator

Rotary vane actuators as rotational drives provide rotational movements directly because they are constructed as a joint and actuator in one. So it is possible to pass on the disadvantageous transmission kinematics used with the so far usual differential cylinders at the arms of large manipulators. However, the use of hydraulic rotary vane actuators is associated with high internal oil leakage and/or high friction. Therefore, a nonlinear dynamic model for such an actuator, driving a rigid robot arm, as well as its nonlinear control are derived. To achieve tracking control a model based control law is set up using fundamental linear differential equations for the tracking error. The control law is implemented and tested on a testbed, the produced experimental results are presented. The same control algorithm can also be used to realize nonlinear disturbance attenuation for hydraulic rotary vane actuators via tracking control. (© 2005 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Modeling of a Pneumatic Hybrid Actuator using an Exponential Approach for Approximation of the Valve-Actuator Behaviour

The present paper describes a mathematical model for the control of a hybrid actuator consisting of a fluidic muscle and a linear pressure spring which is inflated by a proportional directional control valve in 3/3-way function. The device is applied for physical simulation of arbitrary force/displacement relationships. The mathematical model of the system fluidic muscle/valve consists of the approximation of the pressure time-history by an exponential function. The coefficients of the exponential function are identified from corresponding measurement and Least Squares minimization. The advantages of this approximation are that the differential equation for the pressure becomes simple and the computations more efficient. This makes the approach suitable for model-based control. The paper shows that with the proposed model sufficient accuracy can be achieved such as to have a good mathematical model for feedback linearization. For later use the device can be employed in fields of biomechanics as well as in general environments such as motion simulations. (© 2006 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Variational Phase Field Modeling of Fracture Caused by Fluid Transport in Poroelastic Solids

The numerical modeling of failure mechanisms due to fracture based on sharp crack discontinuities is extremely demanding and suffers in situations with complex crack topologies. This drawback can be overcome by recently developed diffusive crack modeling concepts, which are based on the introduction of a crack phase field. Such an approach is conceptually in line with gradient-extended continuum damage models which include internal length scales. In this paper, we extend our recently outlined mechanical framework [1–3] towards the phase field modeling of fracture in the coupled problem of fluid transport in deforming porous media. Here, extremely complex crack patterns may occur due to drying or hydraulic induced fracture, the so called fracking. We develop new variational potentials for Biot-type fluid transport in porous media at finite deformations coupled with phase field fracture. It is shown, that this complex coupled multi-field problem is related to an intrinsic mixed variational principle for the evolution problem. This principle determines the rates of deformation, fracture phase field and fluid content along with the fluid potential. We develop a robust computational implementation of the coupled problem based on the potentials mentioned above and demonstrate its performance by the numerical simulation of complex fracture patterns. (© 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Non-Gaussian non-stationary models for natural hazard modeling

This paper addresses the construction of probabilistic models for time or space dependent natural hazards. The proposed method uses Karhunen-Loève expansion in order to construct an empirical model matching the non-stationarity and the randomness of natural phenomena such as earthquakes or other complex environmental processes. The terms of the Karhunen-Loève expansion are identified directly from measured data. The approach is illustrated and its performance assessed through two academic examples. It is then applied to seismic ground motion modeling using recorded data.     

Toward Best Approximation of Nonlinear Systems: A Case of Models with Memory

The problem of nonlinear dynamical system modeling, considered in this paper, is motivated by restrictions arising in real-world tasks. The restrictions are that first, a system input cannot be entirely observed for one trial. Second, the system model must be subjected to the causality principle. Third, the input is corrupted by noise so that no relationship between the reference input and noise is known. Fourth, the model should have some degrees of freedom so that the associated accuracy can be regulated by a variation of these freedom degrees. We propose and justify new procedures for the nonlinear system modeling that are initialized by these motivations. The models are nonlinear and given by so called r-degree operators that can be reduced to a matrix form presentation. To satisfy the restrictions above, the matrices have special structures that we call the lower p-band matrices. The degree r of the models is the required degree of freedom. The rigorous analysis of errors associated with the presented techniques is given. Numerical experiments with real data demonstrate the efficiency of the proposed approach.     

Dynamic modeling of a high-precision self-moving stage with various frictional models

The piezoelectric actuator (PA) has been used for precision positioning from micro-meter down to nano-meter scale. In this paper, the frictional force is first described by the LuGre model and Leuven model, which allow accurate dynamic modeling both in the sliding and the presliding regimes without using switching functions. The hysteresis frictional parameters controlling the restoring force amplitude and the shape of hysteresis loop are investigated. Sequentially, the two frictional models are employed in the self-moving stage for high-precision positioning under different applied voltages. From the numerical results in the pre-sliding regime, it is shown that the hysteresis frictional force has critical influence on the final position of the micro- and nano-meter positioning.     

Analysis and Validation of a Mathematical Model for Intracranial Pressure Dynamics

Lumped parameter, compartmental models provide a promising Method for mathematically studying the dynamics of human intracranial pressure. In this modeling approach, a system of fully time-dependent differential equations for interacting compartmental pressures is obtained by considering the intracranial system to be confined within the almost-rigid skull and developing continuity equations associated with conservation of mass. Intracranial volumes and flows are related to compartmental pressure differences through compliance and resistance parameters. In the nonlinear case where compliances are not constant, there is a lack of physical information about these parameters. Consequently, it is vital that any mathematical model with an assumed pressure-dependent compliance be validated through comparison with experimental data. The present work develops a logistic representation for the compliance between the cerebrospinal fluid and brain matter compartments. The nonlinear mathematical model involving this logistic compliance is validated here by comparing its predicted response for bolus injections of cerebrospinal fluid to laboratory data generated in an animal model. Comparison with the animal studies fully supports the validity of the mathematical model with the logistic compliance.     

Weibull分布在新产品市场渗透研究中的应用拓展

在应用Weibull模型研究新产品市场渗透时,"永不采用人群"、消费者个体之间的差异、消费者群体之间的差异是研究新产品采用时需要考虑的三种因素。本文基于这三种因素分别建立了三个拓展的Weibull模型,并利用面板数据进行了实证研究,发现三种拓展之后的模型在数据拟合和数据预测方面均有显著的提高.然后,本文将三种因素整合至一个模型之中形成了一种新的综合Weibull模型,实证分析结果显示新的模型有很好的新产品市场渗透数据拟合和预测能力.     

Output feedback vibration control of a string driven by a nonlinear actuator

In this paper, we design an observer-based output feedback controller to exponentially stabilize a system of nonlinear ordinary differential equation-wave partial differential equation-ordinary differential equation. An observer is designed to estimate the full states of the system using available boundary values of the partial differential equation. The output feedback controller is built via the combination of the ordinary differential equation backstepping which is applied to deal with the nonlinear ordinary differential equation, and the partial differential equation backstepping which is used for the wave partial differential equation-ordinary differential equation. The controller can be applied into vibration suppression of a string-payload system driven by an actuator with nonlinear characteristics. The global exponential stability of all states in the closed-loop system is proved by Lyapunov analysis. The numerical simulation illustrates the states of the actuator, string, payload and the observer errors are fast convergent to zero under the proposed output feedback controller.     

Estimation of parameters in latent class models using fuzzy clustering algorithms

A mixture approach to clustering is an important technique in cluster analysis. A mixture of multivariate multinomial distributions is usually used to analyze categorical data with latent class model. The parameter estimation is an important step for a mixture distribution. Described here are four approaches to estimating the parameters of a mixture of multivariate multinomial distributions. The first approach is an extended maximum likelihood (ML) method. The second approach is based on the well-known expectation maximization (EM) algorithm. The third approach is the classification maximum likelihood (CML) algorithm. In this paper, we propose a new approach using the so-called fuzzy class model and then create the fuzzy classification maximum likelihood (FCML) approach for categorical data. The accuracy, robustness and effectiveness of these four types of algorithms for estimating the parameters of multivariate binomial mixtures are compared using real empirical data and samples drawn from the multivariate binomial mixtures of two classes. The results show that the proposed FCML algorithm presents better accuracy, robustness and effectiveness. Overall, the FCML algorithm has the superiority over the ML, EM and CML algorithms. Thus, we recommend FCML as another good tool for estimating the parameters of mixture multivariate multinomial models.