Air path identification of diesel engines by LPV techniques for gain scheduled control

Modelling is a key element to improve the performance of engine control systems, but many factors like non-linearity and complexity complicate the derivation of sufficiently precise physical models. This motivates an increasing interest in data based models. Linear models can successfully represent the engine operation in some reduced regions, but tend to fail when large operating regions must be considered. This motivates the interest in deriving and using gain scheduling models or their natural extension, the linear parameter varying (LPV) models. In this article we propose to model the air path of diesel engines using input–output LPV models with a physically motivated structure and parameters estimated from data. These models are shown to combine good precision with simplicity and allow the systematic design of optimal and robust control systems, and can be determined in a very short time if sufficient data are available.     

Mathematical modelling and dynamic simulation of multi-effect falling-film evaporator for milk powder production

Multiple-effect evaporators are widely used in dairies and food industries because they are appropriately suited for concentrating food solutions. Some mathematical models for multi-effect evaporators are reported in previous studies. But most of them are steady-state models, and there are no extensive studies on the dynamic behaviour of these evaporators. In this paper, two types of dynamic model, lumped and distributed, are developed for an industrial four-effect falling-film evaporator which is used to concentrate whole milk. These models are validated with data from an industrial unit. The results show that the distributed model has slightly better predictions than the lumped model, but the lumped model has comparable performance because its structure is simple and the needed simulation time is short in comparison with the distributed model.     

Fuzzy logic for auditory evoked response monitoring and control of depth of anaesthesia

This paper describes a self-organizing fuzzy model of patients undergoing surgery which was created from 10 clinical trials with off-line analysis during maintenance of anaesthesia using the drug propofol. The effects of patient sensitivity and surgical disturbances are also represented in this patient model. Hence, this model can be considered to be a qualitative pharmacologically related model for propofol during the anaesthetic maintenance stage. Furthermore, a closed-loop simulation has been designed to validate the patient model and compare the performance of a self-organizing fuzzy logic controller algorithm against a clinically derived linguistic controller. The successful results obtained provide proof-of-concept and encouragement to perform on-line clinical trials using fuzzy logic-based monitoring and control in operating theatre in the near future.     

Estimations of trout density and biomass: a neural networks approach

In this paper, we report the use of artificial neural networks to predict the density and biomass of trout in the Pyrenees mountains from 8 physical parameters of the environment. The results obtained with a three-layered neural network are presented. Studies have been undertaken with 1 or 4 variables in the output layer of the network. Results on the test set (generalization of models) are satisfactory with determination coefficients R² exceeding 0.76.     

Nonlinear modelling and control of a common rail injection system for diesel engines

Modern trends in designing mechatronic systems call for a synergic design of the separated subsystems (mechanic, electronic parts, control modules, etc.) concurring to the overall performance. Following this point of view, this paper presents a control oriented model and a nonlinear control design for a Common Rail injection system. First a model is developed, which is tuned in a virtual simulation environment, representing the injection system in details in a reliable replication of reality. Then a sliding mode control is developed. Both the model of the injection process and of the control law are validated by a virtual detailed simulation environment. The prediction capability of the model and the control efficiency are clearly shown.     

Fuzzy predictive temperature control for a class of metallurgy lime kiln models

The combustion temperature and progress control problems are key factors to ensure the production quality of metallurgy lime kiln. The combustion process of lime kiln is a nonlinear and large time‐delay thermal process, so it is difficult to achieve satisfactory results by the traditional proportional integral derivative control, fuzzy control, or predictive control. This article analyses physics and chemistry mechanism of the combustion process and expounds the complex nonlinear, multivariable and large time‐delay characteristics, and the control target of the production system. Then, the mathematical model of combustion control system is deduced in view of the requirements of simulation. Based on these, the fuzzy predictive control scheme is employed. Through simulation, the control algorithm is verified to be effective. Finally, the industrial sleeve kiln as a practical example is used to demonstrate the effectiveness and feasibility of the control algorithm. © 2016 Wiley Periodicals, Inc. Complexity 21: 249–258, 2016     

模糊互补判断矩阵的一个通用排序公式及其保序性研究

基于模糊互补判断矩阵的一致性转化,利用行和归一化方法,得到了模糊互补判断矩阵的一个排序公式,指出目前基于模糊加性一致的排序公式大多都是所提方法的特例,并给出了在增加一个或一组新元素时的保序性条件.