Using constraints and their value for optimization of large ODE systems

We provide analytical tools to facilitate a rigorous assessment of the quality and value of the fit of a complex model to data. We use this to provide approaches to model fitting, parameter estimation, the design of optimization functions and experimental optimization. This is in the context where multiple constraints are used to select or optimize a large model defined by differential equations. We illustrate the approach using models of circadian clocks and the NF-κB signalling system.     

Robust energy-to-peak sideslip angle estimation with applications to ground vehicles

In this paper, the observer design problem for the sideslip angle of ground vehicles is investigated. The sideslip angle is an important signal for the vehicle lateral stability, which is not measurable by using an affordable physical sensor. Therefore, we aim to estimate the sideslip angle with the yaw rate measurements by employing the vehicle dynamics. The nonlinear lateral dynamics is modeled firstly. As the tyre model is nonlinear and the road adhesive coefficient is subject to a large variation, the nonlinear lateral dynamics is transformed into an uncertain model. Considering the variation of longitudinal velocity, an uncertain linear-parameter-varying (LPV) system is obtained. Based on the LPV model, a gain-scheduling observer is proposed and the observer gain can be determined with off-line computation and on-line computation. The off-line computation includes the calculation of a set of linear matrix inequalities and the on-line computation contains several algebraic operations. The proposed design methodology is applied to a four-wheel-independent-drive electric vehicle in simulation. It infers from different maneuvers that the designed observer has a good performance on estimating the sideslip angle.     

Interval functional observers for time-delay systems with additive disturbances

This article considers the design of interval functional observers to estimate a linear function of the state vector of time-delay systems subject to both input and output additive disturbances. Two novel functional observers are proposed and designed such that they bound the set of all admissible values of a linear function of the state vector at each instant of time. By contrast to interval observers currently available in the literature, both observers proposed in this article utilize multiple delayed output measurement and have a more general structure. This trade-off feature overcomes some drawbacks in previous work and enables interval functional observers to be designed for a wider class of time-delay systems. Conditions for the existence of interval functional observers are derived and an effective design algorithm for computing unknown observer matrices is provided. Two illustrative examples are given to show the advantages and effectiveness of our design method.     

Sensor fault estimation of PEM fuel cells using Takagi Sugeno fuzzy model

This paper presents a sensor fault estimation scheme for polymer electrolyte membrane (PEM) fuel cells using Takagi Sugeno (TS) fuzzy model. First, PEM fuel cell systems with sensor faults are modelled by TS fuzzy model. Next, by adding a first order filter, an augmented TS fuzzy system with actuator fault is obtained. Then, for the augmented system, an unknown input observer (UIO) and a fault estimator are developed. The UIO gains are computed by solving linear matrix equalities (LMEs) and linear matrix inequalities (LMIs). The UIO convergence and stability are analyzed and the performances of the proposed fault estimation scheme is demonstrated by numerical simulations for a PEM fuel cell system with return manifold pressure and hydrogen mass sensors.     

Recursive least squares estimation methods for a class of nonlinear systems based on non-uniform sampling

Many dynamic processes in practice have nonlinear characteristics and must be described by using nonlinear models. It remains to be a challenging problem to build the models of such nonlinear systems and to estimate their parameters. This article studies the parameter estimation problem for a class of Hammerstein-Wiener nonlinear systems based on non-uniform sampling. By means of the auxiliary model identification idea, an auxiliary model-based recursive least squares algorithm is derived for the systems. In order to enhance the computational efficiency, an auxiliary model-based hierarchical least squares algorithm is proposed by utilizing the hierarchical identification principle. The simulation results confirm the effectiveness of the proposed algorithms.     

Parameter identification for Wiener-finite impulse response system with output data of missing completely at random mechanism and time delay

In this paper, the parameter estimation issue of Wiener system with random time delay and missing output data is studied. The linear part of Wiener system is described by Finite Impulse Response (FIR) model. The mathematical formula of the Expectation Maximum algorithm to identify Wiener-FIR system that contains the random time delay and the nonlinear output data in missing completely at random mechanism is derived, which is never considered before. To obtain the unmeasurable intermediate variable in Wiener-FIR system, the idea of auxiliary model is adopted. The time delay and system parameters can be estimated simultaneously by this method. Numerical example and the identification of water tank system example are carried out, the effectiveness of the algorithm is proved.     

A new numerical algorithm to check the observability and bad-data processing ability of the metering scheme

This paper provides a unified approach for the optimization of measurements placements employed for power system online monitoring through state estimation. The proposed methodology, which can be suitable for the mixed measure system, preserves state estimation observability and bad-data processing capability by employing numerical algorithms for observability checking, critical measurements and critical couple identification. First, node injection radix measurements and measurement categories are defined. According to the above definitions, the coefficient matrix can be solved. The analysis on the column vectors of the coefficient matrix can determine each measurement classification. Furthermore, the numbers of each measurement class contains can determine bad-data processing capability. The observability can be checked by the type number of measurements. The proposed method is illustrated with the IEEE39-bus system and the IEEE118-bus system. Results from the case studies are presented to demonstrate that the approach adequately fulfills the desired properties related to observability, bad-data processing, cost, and robustness.     

Benefits of variable structure techniques for parameter estimation in stochastic systems using least squares method and instrumental variables

In this paper, we study the benefits of the equivalent control method, implemented on parameter estimation algorithms like the least squares method and instrumental variables on continuous‐time stochastic systems. Here, we show how the equivalent‐control, adapted to the stochastic case, improves the performance of these estimation algorithms in continuous‐time systems under white noise perturbations. Some numerical examples show the effectiveness of these techniques. Copyright © 2014 John Wiley & Sons, Ltd.     

Power system state estimation using a direct non-iterative method

This paper describes a new method for state estimation of a non-linear AC power system in a non-iterative manner. This method is based on the Kipnis–Shamir relinearization technique that is used to solve over-defined sets of polynomial equations. The technique transforms the equations to a higher dimensional linear space which allows the states to be solved in a non-iterative manner. Given accurate measurements, this new state estimation method provides the same results as traditional iterative state estimation methods, and the proposed method does not require an initial guess of system states nor does it have issues with convergence.     

SiC单晶片切割力建模与自适应控制

Si C单晶因优良的物理和机械性能而大量用于大功率器件和IC行业。但由于材料的高硬度和高脆性,使其加工过程变得很困难。为此,分析了Si C单晶片切割过程,建立切割过程模型,通过F检验法进行系统阶次辨识,采用遗忘因子递推最小二乘算法在线估计模型参数,建立进给量与切割力的差分方程,设计基于最小方差自校正的切割力控制器,并进行实验验证。结果表明:控制器能够很好的跟踪不同信号,具有良好的鲁棒性,提高了Si C单晶片的加工效率和表面质量。