A geometric approach for adaptive estimation of unknown growth kinetics in bioreactors

This paper proposes a new approach for the estimation of unknown and time-varying specific growth rate in fed-batch bioprocess. A novel adaptive estimation technique based on the concept of invariant manifold is proposed as an effective approach to estimate growth kinetic parameters. An asymptotic nonlinear observer is used to provide simultaneous on-line estimation of biomass concentration and growth kinetic. The method is easy to implement and requires only one tuning parameter. The effectiveness of the proposed algorithm is illustrated with representative bioreactor simulation examples.     

Systemic modelling and computational physiology: The application of Bond Graph boundary conditions for 3D cardiovascular models

This paper presents an application of Bond Graphs in physiological modelling. In this work, a Bond Graph model is utilised as boundary condition for a detailed model of an idealized mitral valve. Applications of this type fit within the framework described by the “Virtual Physiological Human” initiative. This supports the integration of physical, mechanical and biochemical models encompassing a range of different length and time scales to obtain predictive models of the human body. Because 3D detailed modelling and simulation is computationally intensive, a 3D computational model of a whole biological system is, by today’s standards, impossible to achieve. Due to their unique multi-physics nature of internal coherence, Bond Graphs are particularly suited to biological applications and can be coupled to 3D models and lumped parameter models. A specific application in cardiovascular modelling is demonstrated by focusing on a specific example; a 3D model of the mitral valve coupled to a lumped parameter model of the left ventricle.     

面向功率键合图的通用仿真软件的研究

该文介绍了一种面向功率键合图的通用的系统动态性仿真软件－ＰＢＧＳＩＭ。该软件在ＭＡＴＬＡＢ平台上二次开发而成。充分发挥了功率键合图法在分析系统动态特性方面的优势,可以处理包含各种环节的复杂系统,软件从系统的键合图模型出发,自动辨错揭示、自动建立数学模型（字符型和数值型）、进行仿真计算并按要求输出仿真结果,用户可以方便地修改元件参数或模型直至得到满意结果。还可以给定 经变量,对系统的动态特性进行参数     

Bayesian-like autoregressive spectrum estimation in the case of unknown process order

Initially, the problem of estimation of the spectral density function of a stationary multivariate autoregressive Gaussian process of unknown order is considered. Two new solutions to this problem are presented. The proposed estimators, suggested by the form of the Bayesian predictor in autoregressive systems, can be characterized as the average model spectrum and the spectrum corresponding to the "averaged model," with the averages being computed over the set of competitive autoregressive models of different orders and with respect to the sequence of the posterior probabilities of the process order given its observation history. The obtained results are then extended to the case of nonstationary autoregressive processes (identified by means of the exponentially weighted estimators) and more general weighting sequences. Although not Bayesian in the strict sense, the proposed approaches seem to be interesting from the theoretical point of view and give better results than the "classical" one. The efficient computational algorithms are indicated and the results of computer simulations are discussed.     

Bond Graph modeling for fault detection and isolation of a train door mechatronic system

In this paper, monitoring and diagnosis of a new generation of train door mechatronic system is proposed. Bond Graph methodology is applied to obtain a reference model. In addition to this reference model, a global model based-FDI (Fault Diagnostic and Isolation) is developed for the generation of fault indicators and residual thresholds in presence of door failures. The ability of the proposed diagnostic approach to detect train door failures is demonstrated. The main contribution of this paper concerns the implementation of FDI procedure on a train door instead of health monitoring as it is usually performed in maintenance field.     

基于FDA-KDE间歇过程在线监控

针对基于传统的多向主元分析(Multiway Principal Component Analysis,MPCA)方法用于间歇过程在线监控时需要对新批次未反应完的数据进行预估,从而易导致误诊断,且统计量控制限的确定是以主元得分呈正态分布为假设前提的缺陷,结合Fisher判别分析(Fisher Discriminant Analysis,FDA)在数据分类及非参数统计方法核密度估计(Kernel Density Estimation,KDE)在计算概率密度函数方面的优势,提出了一种FDA-KDE的间歇过程监控方法。该方法首先利用FDA求取正常工况数据和故障数据的Fisher特征向量和判别向量,获得Fisher特征向量的相似度:然后在提出偏平均集成平方误差(Biased Mean Integrated Squared Error,BMISE)交叉验证法确定KDE的带宽从而获得相似度统计量控制限的基础上,利用已获得的数据测量值对过程进行监控,避免了基于MPCA方法对未来测量值的预估;最后采用基于Fisher判别向量权重的贡献图方法来进行故障诊断。通过对青霉素发酵间歇过程应用表明,所提出的方法比传统的MPCA方法能更及时地监测出过程异常情况,更准确地判断异常发生的原因。     

键合图法在传感器优化配置中的应用

传感器优化配置在大型过程控制系统中具有重要的地位,但目前所采用的方法较复杂,不易在工程中应用.提出将基于键合图模型的系统结构特性分析应用于系统传感器优化配置中,具有实用性强、建模方便、简单易行的优点.通过举例验证了该方法在过程控制系统中的应用效果.     

Parallelization of lattice modelling for estimation of fracture process zone extent in cementitious composites

This paper is focused on the verification and validation of the developing technique for estimation of the extent (the size and shape) of the fracture process zone (FPZ) in quasi-brittle silicate-based specimens/structures during failure process (termed the ReFraPro – Reconstruction of Fracture Process – technique). Most experimental data published in the literature are incomplete for its sound validation; therefore, numerical simulations by means of physical discretization of continuum are used for supplementing the verification of the technique. A discrete spring network/lattice particle-type model formulated as a nonlinear dynamical system is utilized. Parallelized implementation within the CUDA environment helps to decrease the computational cost of the simulations to an admissible level. The conducted analysis demonstrates satisfactory agreement of the size and shape of the FPZ reconstructed by the ReFraPro technique with both the data of the performed simulations and selected experimental data from literature.     

基于键合图法的活塞泵建模与仿真

基于由荷兰Twente大学控制工程系研发的机电一体化建模软件20-sim,提出利用键合图法实现活塞泵的建模与仿真。在实例模型中,首先实现了单向阀的20-sim模块构造,继而在20-sim环境中建立了活塞泵的键合图模型,仿真结果理想地显示了概念设计阶段的控制效果。     

汽液两相流键合图建模与仿真

为了研究汽液两相流管道系统动态特性,从质量守恒、能量守恒和动量守恒方程出发,采用交错网格有限体积法,推导出汽液两相流通用微分方程的离散形式,结合键合图理论建立了分段集总参数键合图模型.为了求解工质热力性质,利用多领域统一建模语言Modelica建立了键合图模型的仿真程序,并以直流锅炉受热管为例,在Dymola平台上进行仿真.仿真结果反映了热负荷阶跃变化时管内压力、温度、含汽率动态变化与分布情况,为管道系统设计与分析提供了依据.     

Adaptive parameter estimation for a general dynamical system with unknown states

This paper is concerned with the design of a state filter for a time‐delay state‐space system with unknown parameters from noisy observation information. The key is to investigate new identification algorithms for interactive state and parameter estimation of the considered system. Firstly, an observability canonical state‐space model is derived from the original model by linear transformation for the purpose of simplifying the model structure. Secondly, a direct state filter is formulated by minimizing the state estimation error covariance matrix on the basis of the Kalman filtering principle. Thirdly, once the unknown states are estimated, a state filter–based recursive least squares algorithm is proposed for parameter estimation using the least squares principle. Then, a state filter–based hierarchical least squares algorithm is derived by decomposing the original system into several subsystems for improving the computational efficiency. Finally, the numerical examples illustrate the effectiveness and robustness of the proposed algorithms.     

Nonlinear observer for simultaneous states and unknown parameter estimation

This paper presents the design of an observer for the simultaneous estimation of states and unknown parameter for a class of nonlinear systems whose nonlinearity satisfies a bounded Jacobian condition. The paper presents two alternate observers based on the structure of the system. The conditions for the existence of these observers can be expressed as a linear matrix inequality and solved using standard solvers. The case of time-varying parameter and multiple unknown parameter have also been investigated. The use of the developed methodology is demonstrated through illustrative examples.     

State and unknown input estimation for linear discrete-time systems

In this paper, a state and unknown input delayed estimator is designed for discrete-time linear systems even if some well-known matching condition does not hold. This result is obtained using a constructive algorithm that analyzes the state observability and the left invertibility of the systems with unknown inputs and that provides a suitable canonical transformation for the design of the estimator.     

Partial state and unknown input estimation for time-delay systems

This article considers the problem of estimating a partial set of the state vector and/or unknown input vector of linear systems driven by unknown inputs and time-varying delay in the state variables. Three types of reduced-order observers, namely, observers with delays, observers without internal delays and delay-free observers are proposed in this article. Existence conditions and design procedures are presented for the determination of parameters for each case of observers. Numerical examples are presented to illustrate the design procedures.     

Advanced methods for the estimation of an unknown projection from a map

This article presents three new methods (M5, M6, M7) for the estimation of an unknown map projection and its parameters. Such an analysis is beneficial and interesting for historic, old, or current maps without information about the map projection; it could improve their georeference. The location similarity approach takes into account the residuals on the corresponding features; the minimum is found using the non-linear least squares. For the shape similarity approach, the minimized objective function ? takes into account the spatial distribution of the features, together with the shapes of the meridians, parallels and other 0D-2D elements. Due to the non-convexity and discontinuity, its global minimum is determined using the global optimization, represented by the differential evolution. The constant values of projection φ _k , λ _k , φ ₁, λ ₀, and map constants R,ΔX,ΔY, α (in relation to which the methods are invariant) are estimated. All methods are compared and the results are presented for the synthetic data as well as for 8 early maps from the Map Collection of the Charles University and the David Rumsay Map Collection. The proposed algorithms have been implemented in the new version of the detectproj software.     

Neural network modelling and control strategies for a pH process

The control of a pH process using neural networks is examined. The neural network as a universal approximator is used to good effect in this nonlinear problem, as is shown in the simulation results. In the modelling task, the dynamics of the process was carefully examined to determine a suitable structure for the net. In particular, a multilayer net consisting of two single hidden layers was constructed to reflect the Wiener model of the pH process. This led to much simpler training compared to similar modelling attempts by other researchers. For the control task, two schemes were simulated. In one approach, a net was used to deal with the static nonlinearity to achieve control over a wide working range. The dynamic controller used was the PID, with its parameters tuned on a relay auto-tuner. This control design was compared with the strong acid equivalent method. In the second approach, a direct model reference adaptive neural network control scheme was proposed. The training procedure uses the more efficient least squares algorithm developed by Loh and Fong.     

基于无极卡尔曼滤波算法的雅可比矩阵估计

在基于图像的机器人视觉伺服中,采用在线估计图像雅可比的方法,不需事先知道系统的精确模型,可以避免复杂的系统标定过程。为了有效改善图像雅可比矩阵的在线估计精度,进而提高机器人的跟踪精度,针对机器人跟踪运动目标的应用背景,提出了利用无极卡尔曼滤波算法在线估计总雅可比矩阵。在二自由度的机器人视觉伺服仿真平台上,分别用卡尔曼滤波器（KF）、粒子滤波器（PF）和无极卡尔曼滤波器（UKF）三种算法进行总雅可比矩阵的在线估计。实验结果证明,使用UKF算法的跟踪精度优于其他两种算法,时间耗费仅次于KF算法。     

Rapid estimation and detection scheme for unknown discretized rectangular inputs

An algorithm to detect unknown discretized rectangular inputs and to estimate the state simultaneously, has been described in this note. The discretized rectangular input is represented by a sequence of equal magnitude inputs to the state. Four alternate hypotheses are formed at every stage of observation and Bayes risks are compared to determine the correct one. A minimum variance estimate of the inputs is used to improve the estimate of the state. Computer results from a numerical example are shown to demonstrate that a possible divergence of the Kalman filter can be prevented by incorporating the detection scheme.     

含未知输入的广义系统的状态与输入估计

研究了含未知输入的广义系统的输入解耦观测器设计问题. 在系统脉冲能控的条件下通过系统输入-状态对的非奇异变换, 把此问题等价地转化为正常状态空间系统的相应问题. 用大家熟知的方法设计正常状态空间系统的观测器, 从而得到广义系统的输入解耦观测器. 然后用广义系统的观测器状态和系统输出的线性组合渐近估计系统的状态与未知输入.     

Petri net-based process monitoring: a workflow management system for process modelling and monitoring

Nowadays business process management is becoming a fundamental piece of many industrial processes. To manage the evolution and interactions between the business actions it is important to accurately model the steps to follow and the resources needed by a process. Workflows provide a way of describing the order of execution and the dependencies between the constituting activities of business processes. Workflow monitoring can help to improve and avoid delays in industrial environments where concurrent processes are carried out. In this article a new Petri net extension for modelling workflow activities together with their required resources is presented: resource-aware Petri nets (RAPN). An intelligent workflow management system for process monitoring and delay prediction is also introduced. Resource aware-Petri nets include time and resources within the classical Petri net workflow representation, facilitating the task of modelling and monitoring workflows. The workflow management system monitors the execution of workflows and detects possible delays using RAPN. In order to test this new approach, different services from a medical maintenance environment have been modelled and simulated.