闭环具有锯齿特性的PWM型DC-DC buck变换器周期解的存在性

应用周期方程方法研究具有锯齿波特性的闭环PWM型buck DC-DC变换器T周期解的存在性问题, 并给出了其在一个周期内仅有一次切换的T周期解存在的充分条件. 所给出的结果为闭环PWM型buck DC-DC变换器的控制器和锯齿波参数的设计提供了指导准则.     

Theoretical and experimental investigation of bidirectional Hopf bifurcations in cascade DC-DC buck converters

This paper deals with two types of bidirectional Hopf bifurcation phenomena in two-stage cascade DC-DC buck converter, which is a popular design solution for obtaining larger conversion rates in distributed power systems (DPS). By means of “exact” cycle-by-cycle numerical simulations, it is found that two types of bidirectional Hopf bifurcations that never occur in standalone buck converter are observed under the variations of some particular circuit parameters. Based on the derived averaged model, theoretical analysis is performed to identify the two types of bidirectional bifurcations by judging in what way the eigenvalue loci of the Jacobian move, and further to reveal their underlying mechanisms. In addition, some behavior boundaries are given in parameter space, and some rules are proposed to avoid the occurrence of the bidirectional Hopf bifurcations. Finally, experimental results are presented for verification purposes.     

Modeling and stability analysis of cascade buck converters with N power stages

This paper presents a method for stability analysis of N-cell cascade step-down buck converters, which is a kind of complex nonlinear system. A nonlinear model in the form of time-variant state equations is derived, and then the loop gain describing the overall system is introduced. With the help of them, we obtain the equations for phase cross over frequency and gain margin by small-signal perturbation technique, harmonic balance method, and inverse iteration method. Based on the equations, the overall cascade system stability can be analyzed regardless of the number of converters cascaded. Finally, the cascade buck converter with three power stages is used to demonstrate the effectiveness of the proposed stability analysis method.     

12V,3A电流模同步Buck DC-DC转换器功率级设计

采用0.35μm 18 V DPTM BCD工艺技术给出电流模降压型DC-DC转换器的功率级设计,该功率级可以输出3A负载电流,转换效率可达到94.5%。主要针对转换器中核心部分功率级进行设计,其中包括同步开关功率晶体管设计、片上电感电流检测电路、功率晶体管驱动电路设计以及功率级的版图设计考虑,最后给出了该功率级设计的测试结果 。     

Design and implementation of a neuro-adaptive backstepping controller for buck converter fed PMDC-motor

A neuro-adaptive backstepping control (NABSC) method using single-layer Chebyshev polynomial based neural network is proposed for the angular velocity tracking in buck converter fed permanent magnet dc (PMDC)-motor. Owing to their universal approximation property, neural networks have been utilized for approximating the unknown nonlinear profile of instantaneous load torque. The inherent computational complexity of the neural network based adaptive scheme has been circumvented through the use of orthogonal Chebyshev polynomials as basis functions. A detailed stability and transient performance analysis has been conducted using Lyapunov stability criteria. The proposed control scheme is shown to yield a superior output performance with enhanced robustness for wide variations in load torque and set-point changes, compared to existing conventional approaches based on adaptive backstepping. The theoretical propositions are verified on an experimental prototype using dSPACE, Control Desk DS1103 setup with an embedded TM320F240 Digital Signal Processor proving its applicability to real-time electrical systems. The efficiency of the proposed strategy is quantified using performance measures and are evaluated against the conventional adaptive backstepping control (ABSC) methodology. Ultimately, this investigation confirms the effectiveness of the proposed control scheme in achieving an enhanced output transient performance while faithfully realizing its control objective in the event of abrupt and uncertain load variations.     

Multitask learning and nonlinear optimal control of the COVID-19 outbreak: A geometric programming approach

We propose a multitask learning approach to learn the parameters of a compartmental discrete-time epidemic model from various data sources and use it to design optimal control strategies of human-mobility restrictions that both curb the epidemic and minimize the economic costs associated with implementing non-pharmaceutical interventions. We develop an extension of the SEIR epidemic model that captures the effects of changes in human mobility on the spread of the disease. The parameters of the model are learned using a multitask learning approach that leverages both data on the number of deaths across a set of regions, and cellphone data on individuals’ mobility patterns specific to each region. Using this model, we propose a nonlinear optimal control problem aiming to find the optimal mobility-based intervention strategy that curbs the spread of the epidemic while obeying a budget on the economic cost incurred. We also show that the solution to this nonlinear optimal control problem can be efficiently found, in polynomial time, using tools from geometric programming. Furthermore, in the absence of a straightforward mapping from human mobility data to economic costs, we propose a practical method by which a budget on economic losses incurred may be chosen to eliminate excess deaths due to over-utilization of hospital resources. Our results are demonstrated with numerical simulations using real data from the COVID-19 pandemic in the Philadelphia metropolitan area.     

Routine high-return human-competitive automated problem-solving by means of genetic programming

Genetic programming is a systematic method for getting computers to automatically solve problems. Genetic programming starts from a high-level statement of what needs to be done and automatically creates a computer program to solve the problem by means of a simulated evolutionary process. The paper demonstrates that genetic programming (1) now routinely delivers high-return human-competitive machine intelligence; (2) is an automated invention machine; (3) can automatically create a general solution to a problem in the form of a parameterized topology and (4) has delivered a progression of qualitatively more substantial results in synchrony with five approximately order-of-magnitude increases in the expenditure of computer time. These points are illustrated by a group of recent results involving the automatic synthesis of the topology and sizing of analog electrical circuits, the automatic synthesis of placement and routing of circuits, and the automatic synthesis of controllers as well as references to work involving the automatic synthesis of antennas, networks of chemical reactions (metabolic pathways), genetic networks, mathematical algorithms, and protein classifiers.     

Imparting desired attributes in structural design by means of multi-objective optimization

Commonly available optimization methods typically produce a single optimal design as a constrained minimum of a particular objective function. However, in engineering design practice it is quite often important to explore as much of the design space as possible, with respect to many attributes, to discover what behaviors are possible and not possible within the initially adopted design concept. This paper shows that the very simple method of the sum of weighted objectives is useful for such exploration. By geometrical argument it is demonstrated that if every weighting coefficient is allowed to change its magnitude and its sign then the method returns a set of designs that are all feasible, diverse in their attributes, and include the Pareto and non-Pareto solutions, at least for convex cases. Numerical examples in the paper include the case of an aircraft wing structural box with thousands of degrees of freedom and constraints, and over 100 design variables, whose attributes are structural mass, volume, displacement, and frequency. The weighted coefficients method is inherently suitable for parallel, coarse-grained implementation that enables exploration of the design space in the elapsed time of a single structural optimization.     

基于超越几何规划的热轧操作优化问题

针对钢铁热轧生产操作优化问题,建立热轧操作优化模型.该模型的难点是,模型具有高度非线性的特征,难以获得最优解.考虑模型数学表达式的结构特点,将操作优化模型等价转化为超越几何规划模型,由于获得的模型存在对数项,无法直接有效求解,利用模型的结构特点,通过数学变换和理论分析,转化为凸规划模型,从而利用凸规划软件获得最优解,为操作优化问题获得全局最优解提供一种新方法.     

Design of an arabic programming language (ARABLAN)

It was observed that school children in Arab countries find difficulty in learning programming in a language other than their native language. There are only few Arabic programming languages in the literature; most of which are never or partially implemented. In this paper, we present the design of a new Arabic programming language (ARABLAN) which is planned to be implemented for use in teaching programming for school children in Arab countries.     

Geometric mouldability analysis by geometric reasoning and fuzzy decision making

This paper presents a methodology for mouldability analysis by finding the optimal cavity design scheme (CDS) based on manufacturing and cost considerations using part geometry, where a CDS refers to a combination of the parting direction, parting line (PL), and undercut features (UF). The methodology takes advantage of geometric reasoning and fuzzy evaluation, and consists of two main stages: (1) generating all possible design alternatives, and (2) choosing the best alternative. In the first stage, after recognizing the potential UF from the given part, a spherical arrangement is constructed by partitioning the unit direction sphere using outward normals of the part's surfaces with the property that each cell in this arrangement has a unique combination of PL and UF set. Thus all design alternatives can be identified in O(ml²) time by visiting the cells in a certain order and updating the PL and UF set incrementally, where m and l are the number of the part's convex and overall surfaces, respectively. In the second stage, the fuzzy multiple attribute decision-making model is employed to choose the optimal scheme from the set of design alternatives with respect to a set of criteria related to the number and volume of undercuts, flatness of the PL, draw depth, and draft angle. This model allows designers to describe their preferences on different criteria in imprecise linguistic statements. Finally, the case studies show that the proposed methodology is very effective in finding the optimal CDS for the molded part and the final results conform to human designers' experience.     

Stability of an analog optimization circuit for quadratic programming

We study the stability of an analog optimization circuit that solves quadratic programming (QP) problems. The circuit dynamics are modeled as a switched affine system. A piece-wise quadratic Lyapunov function and the KYP lemma are used to derive the stability criterion. The stability criterion characterizes the range of critical circuit parameters for which the QP circuit is globally exponentially stable.     

Discovering approximate expressions of GPS geometric dilution of precision using genetic programming

Global Positioning System (GPS) has been used extensively in various fields. Geometric Dilution of Precision (GDOP) is an indicator showing how well the constellation of GPS satellites is organized geometrically, so as a reliability indicator presenting the GPS positioning accuracy. Traditional methods for calculating GPS GDOP need to solve the measurement equations where involve complicated matrix transformation and inversion. Some studies rephrase the calculation of GPS GDOP a regression problem and employ “black-boxed” machine learning methods for problem solving. However, the regression models obtained from such methods lack of expressivity for describing the relationships among variables. Making the structures of GDOP expressions visible is valuable because they can be further studied or tailored for specific GPS applications. This study employs the technique of genetic programming (GP) for the regression of GPS GDOP. The performance of GP working with various operators and parameter settings is studied and discussed. The experimental results show that GP generates precise models with better expressivity for GPS GDOP than other methods.     

基于多种语言混合编程的优化软件设计

针对爆炸结构的设计及优化,采用APDL、Matlab和VC 语言混合编程的方法优化软件的设计,不但可以方便地实现优化算法和设计友好的用户界面,还能实现数值模拟计算程序与优化程序联合使用,可以有效地缩短程序设计时间.对LS-DYNA分析计算结果的整理及结构材料信息文件的修改给出了解决办法,实现了全自动优化过程,提高了优化效率.优化软件的设计实例表明了该方法的可行性和有效性.     

Applying genetic programming and ant colony optimisation to improve the geometric design of a reflector

The lighting performance of an LED (light-emitting diode) flash is significantly influenced by the geometric form of a reflector. Previously, design engineers have usually determined the geometric design of a reflector according to the principles of optics and their own experience. Some real reflectors have then been created to verify the feasibility and performance of a certain geometric design. This, however, is a costly and time-consuming procedure. Furthermore, the geometric design of a reflector cannot be proven to be actually optimal. This study proposes a systematic approach based on genetic programming (GP) and ant colony optimisation (ACO), called the GP–ACO procedure, to improve the geometric design of a reflector. A case study is used to demonstrate the feasibility and effectiveness of the proposed optimisation procedure. The results show that all the crucial quality characteristics of an LED flash fulfil the required specifications; thus, the optimal geometric parameter settings of the reflector obtained can be directly applied to mass production. Consequently, the proposed GP–ACO procedure can be considered an effective method for resolving general multi-response parameter design problems.     

Integer programming models for logical layout design of modular machining lines

The preliminary design or reconfiguration of modular manufacturing lines is addressed. The modules are multi-spindle units. Each unit executes a subset of operations. The set of all available spindle units and their costs are known. The problem is to select the right spindle units and to arrange them into linear workstations. The objective is to design such a line respecting technological constraints and minimizing the investment cost. In this paper, some efficient formulations of linear integer programming (IP) models and specific techniques to reduce the calculation time are proposed. Experiments were carried out which highlight the performance of the models.     

CAD系统的二次开发在飞机构型优化中的应用

飞机概念设计优化目前存在的主要问题是分析对象的几何描述过于简单、描述不统一和修改困难,造成分析计算可信度不高而且不同学科分析模块间信息很难转换。从建立一个三维运输机参数模型入手,通过CAD系统的二次开发,将模型集成到设计优化框架中,应用高信度的计算流体方法（CFD）,以飞机起飞重量最小为目标完成构型优化。     

Automatic offloading of mobile applications into the cloud by means of genetic programming

The limited battery life of modern mobile devices is one of the key problems limiting their use. Even if the offloading of computation onto cloud computing platforms can considerably extend battery duration, it is really hard not only to evaluate the cases where offloading guarantees real advantages on the basis of the requirements of the application in terms of data transfer, computing power needed, etc., but also to evaluate whether user requirements (i.e. the costs of using the cloud services, a determined QoS required, etc.) are satisfied. To this aim, this paper presents a framework for generating models to make automatic decisions on the offloading of mobile applications using a genetic programming (GP) approach. The GP system is designed using a taxonomy of the properties useful to the offloading process concerning the user, the network, the data and the application. The fitness function adopted permits different weights to be given to the four categories considered during the process of building the model. Experimental results, conducted on datasets representing different categories of mobile applications, permit the analysis of the behavior of our algorithm in different applicative contexts. Finally, a comparison with the state of the art of the classification algorithm establishes the goodness of the approach in modeling the offloading process.