Rigorous Modeling of Real-time System Based on UML and PVS

Rigorous modeling could improve the correctness and reduce cost in embedded real-time system development for models could be verified. Tools are needed for rigorous modeling of embedded real-time system. UML is an industrial standard modeling language which provides a powerful expressi-veness, intuitive and easy to use interface to model. UML is widely accepted by software developer. However, for lack of precisely defined semantics, especially on the dynamic diagrams, UML model is hard to be verified. PVS is a general formal method which provides a high-order logic specification language and integrated with model checking and theorem proving tools. Combining the widely used UML with PVS, this paper provides a novel modeling and verification approach for embedded real-time system. In this approach, we provide 1) a timed extended UML statechart for modeling dynamic behavior of an embedded real-time system; 2) an approach to capture timed automata based semantics from timed statechart; and 3) an algorithm to generate a finite state model expressed in PVS specification for model checking. The benefits of our approach include flexible and friendly in modeling, extendable in formalization and verification content, and better performance. Time constraints are modeled and verified and its a highlight of this paper.     

Discrete Time Optimal Adaptive Control for Linear Stochastic Systems

The least-squares (LS) algorithm has been used for system modeling for a long time. Without any excitation conditions, only the convergence rate of the common LS algorithm can be obtained. This paper analyzed the weighted least-squares (WLS) algorithm and described the good properties of the WLS algorithm. The WLS algorithm was then used for adaptive control of linear stochastic systems to show that the linear closed-loop system was globally stable and that the system identification was consistent. Compared to the past optimal adaptive controller, this controller does not impose restricted conditions on the coefficients of the system, such as knowing the first coefficient before the controller. Without any persistent excitation conditions, the analysis shows that, with the regulation of the adaptive control, the closed-loop system was globally stable and the adaptive controller converged to the one-step-ahead optimal controller in some sense.     

Temperature modeling and control of Direct Methanol Fuel Cell based on adaptive neural fuzzy technology

Aiming at on-line controlling of Direct Methanol Fuel Cell （DMFC） stack, an adaptive neural fuzzy inference technology is adopted in the modeling and control of DMFC temperature system. In the modeling process, an Adaptive Neural Fuzzy Inference System （ANFIS） identification model of DMFC stack temperature is developed based on the input-output sampled data, which can avoid the internal complexity of DMFC stack. In the controlling process, with the network model trained well as the reference model of the DMFC control system, a novel fuzzy genetic algorithm is used to regulate the parameters and fuzzy rules of a neural fuzzy controller. In the simulation, compared with the nonlinear Proportional Integral Derivative （PID） and traditional fuzzy algorithm, the improved neural fuzzy controller designed in this paper gets better performance, as demonstrated by the simulation results.     

A model-based evaluati6n system of enterprise

This paper analyses the architecture of enterprise modeling, proposes indicator selection principles and indicator decomposition methods, examines the approaches to the evaluation of enterprise modeling and designs an evaluation model of AHP. Then a model-based evaluation system of enterprise is presented to effectively evaluate the business model in the framework of enterprise modeling.     

Application of uncertainty reasoning based on cloud model in time series prediction

Time series prediction has been successfully used in several application areas, such as meteoro-logical forecasting, market prediction, network traffic forecasting, etc. , and a number of techniques have been developed for modeling and predicting time series. In the traditional exponential smoothing method, a fixed weight is assigned to data history, and the trend changes of time series are ignored. In this paper, an uncertainty reasoning method, based on cloud model, is employed in time series prediction, which uses cloud logic controller to adjust the smoothing coefficient of the simple exponential smoothing method dynamically to fit the current trend of the time series. The validity of this solution was proved by experiments on various data sets.     

Temperature Control and Simulation of TemperatureAltitude Test System

The temperatureAltitude Test System (TATS) supplies various testing environments. The traditional PID method controls the temperature in TATS TemperaturePressure Cabin (TPC) over a long adjusting time and with a large overshoot. In order to solve this problem simply, a temperature control strategy with temperature difference corresponding factors is presented through a dynamic analysis and modeling of TPC temperature change. The TPC temperature descending process is simulated, and the results show that this control strategy can allot the proportion of PID heating controller and PID cooling controller in the whole control process and TPC temperature can be controlled at a set point quickly and effectively.     

Particle methods for multiscale simulation of complex flows

The multi-scale structures of complex flows have been great challenges to both theoretical and engineering researches, and multi-scale modeling is the natural way in response. Particle methods (PMs) are ideal constitutors and powerful probes of multi-scale models, owing to their physical insight and computational simplicity. In this paper, the role of different PMs for multi-scale modeling of complex flows is critically reviewed and possible development of PMs in this background is prospected, with the emphasis on pseudo-particle modeling (PPM). The performances of some different PMs are compared in simulations and new development in the fundamentals and applications of PPM is also reported, demonstrating PPM as a unique PM for multi-scale modeling.     

Intelligent Scheduling Controller Design for Networked Control Systems Based on Estimation of Distribution Algorithm

The use of communication networks in control loops has gained increasing attention in recent years due to its advantages and flexible applications. The network quality-of-service （QoS） in those socalled networked control systems always fluctuates due to changes of the traffic load and available network resources, This paper presents an intelligent scheduling controller design approach for a class of NCSs to handle network QoS variations, The sampling period and control parameters in the controller are simultaneously scheduled to compensate for the network QoS variations. The estimation of distribution algorithm is used to optimize the sampling period and control parameters for better performance. Compared with existing networked control methods, the controller has better ability to compensate for the network QoS variations and to balance network loads. Simulation results show that the plant setting time with the intelligent scheduling controller is reduced by about 64.0% for the medium network load and 49.1% for high network load and demonstrate the effectiveness of the proposed approaches.     

Neural—networks—based Modelling and a Fuzzy Neural Networks Controller of MCFC

Molten Carbonate Fuel Cells(MCFC) are produced with a highly efficient and clean power generation technology which will soon be widely utilized.The temperature characters of MCFC stack are briefly analyzed.A radial basis function (RBF) neural networks identification technology is applied to set up the temperature nonlinear model of MCFC stack,and the identification structure,algorithm and modeling training process are given in detail.A fuzzy controllery of MCFC stack is designed.In order to improve its online control ability,a neural network trained by the I/O data of a fuzzy controller is designed.The neural networks can memorize and expand the inference rules of the fuzzy controller and substitute for the fuzzy controller to control MCFC stack online,A detailed design of the controller is given,The validity of MCFC stack modilling based on neural networks and the superior performance of the fuzzy neural networks controller are proved by Simulations.     

An approach to 3D NURBS modeling of complex fault network considering its historic tectonics

Fault disposal is a research area that presents difficulties in 3D geological modeling and visualization. In this paper, we propose an integrated approach to reconstructing a complex fault network (CFN). Based on the non-uniform rational B-spline (NURBS) techniques, fault surface was constructed, reflecting the regulation of its spatial tendency, and correlative surfaces were enclosed to form a fault body model. Based on these models and considering their historic tectonics, a method was put forward to settle the 3D modeling problem when the intersection of two faults in CFN induced the change of their relative positions. First, according to the relationships of intersection obtained from geological interpretation, we introduced the topological sort to determine the order of fault body construction and rebuilt fault bodies in terms of the order; then, with the disposal method of two intersectant faults in 3D modeling and applying the Boolean operation, we investigated the characteristic of faults at the intersectant part. An example of its application in hydropower engineering project was proposed. Its results show that this modeling approach can increase the computing efficiency while less computer memory is required, and it can also factually and objectively reproduce the CFN in the engineering region, which establishes a theoretical basis for 3D modeling and analysis of complex engineering geology.     

一种数控火焰切割机CNC系统

利用工业个人计算机（ＩＰＣ）开发的数控火焰切割机ＣＮＣ系统,除具有适合于数控火焰切割机工艺特点的控制功能外,还具有图形编程、仿形编程、加工程序屏幕模拟仿真和实时多任务处理等功能,插补计算采用了差分插补法,能对所有的二次曲线进行直接插补。     

四旋翼飞行器动力系统模型参数辨识实验研究

四旋翼飞行器动力系统是飞行器的重要组成部分,也是影响控制精度的重要因素。通常在动力系统建模时直接采用机理建模法,但在实际中动力系统的选用和安装都会影响到动力系统模型,故直接采用机理建模方法准确性不高。本文基于动力系统机理模型得到动力系统模型结构,然后在搭建的测试实验平台上完成了转速数据的测量,根据实验数据通过ARX模型辨识出动力系统的模型参数,建立出四旋翼飞行器动力系统模型并设计了PI控制器。通过系统仿真与实际测量验证,结果表明：对应用参数辨识后的四旋翼动力系统模型进行控制时,系统的快速性和稳定性都有明显的提高。     

用于系统模型层验证的模型转换的研究

传统以代码为中心的数控系统开发方式导致了系统测试滞后,系统性能难以保证。针对这种情形,提出利用模型转换将领域模型转换到第三方验证工具(Matlab 或UPPAAL)进行系统性能早期测试,从模型层保证系统性能。讨论了采用基于元模型层规则定义、语义动态添加和基于设计模式的操作等策略,以此满足实际模型转换的具体要求,给出了其框架。并指出根据实际需求采用不同的实现步骤和技术手段完成模型转换。从数控系统工作模式模型到StateFlow模型的转换实例从建模语言构建、映射规则定义、算法设计等几方面验证了上述模型转换理论框架和实现手段的可行性和正确性。     

基于峰值控制的交错Boost变换器建模与设计

交错Boost变换器作为中大功率开关电源前级PFC技术的核心部分,其数学建模对开关电源的设计至关重要。本文基于峰值电流控制技术,提出了一种交错Boost变换器的建模与设计方案。运用时间平均等效原理,导出了该方案的完整交流小信号模型,设计了控制器补偿网络,并制作了一台800 W的实验样机。测试结果表明,该方案具有峰值限流好、动态响应快、输出电压波动小等优点。     

基于集结模型的精馏塔产品质量非线性鲁棒控制

将奇异摄动方法应用于一个二元精馏塔的模型降阶 ,得到了一个用于控制器设计的集结简化模型。考虑到模型的误差和其他不确定性 ,设计了一个基于状态反馈的非线性鲁棒控制器。由于难以实时测量中间状态 ,又设计了一个非线性Luenberger型的观测器 ,使该控制器基于输出动态反馈。应用结果表明 ,该非线性鲁棒控制器对干扰和模型误差具有鲁棒控制作用     

插床的数控化改造

自行研制开发了基于PC机+PMAC运动控制卡的开放式三坐标CNC系统,对普通插床进行了数控化改造.该CNC系统采用模块化设计,具有实时加工控制、图形自动编程、列表曲线拟合编程、模拟仿真和加工轨迹动态跟踪显示等多种功能,已成功应用于罗茨泵系列转子异型面的数控化加工.     

基于滑模控制的数控机床伺服系统的摩擦补偿

以数控机床伺服系统为研究对象,对其建立了数学模型,并结合Lu Gre摩擦模型,设计了参数在线估计的PID滑模面自适应控制器.并采用李亚普诺夫函数证明系统的渐进稳定性.最后通过系统的MATLAB仿真及物理实验进一步证明了系统具有良好的跟踪性能.     

具有通信约束的网络化系统动态输出反馈控制

针对一类存在访问约束和时变时延的网络化控制系统,研究了其中的动态输出反馈控制器的设计问题.考虑到介质访问约束服从Markov分布和时变时延的上界小于一个采样周期的情况,将网络化控制系统建模为一类具有不确定性的离散时间Markov跳变系统.根据Lyapunov稳定性理论与状态增广方法,给出了闭环网络化控制系统渐近稳定的充分条件,并设计了动态输出反馈控制器,使得闭环系统渐近稳定.最后通过仿真验证了所提方法的有效性.     

实时特征插补原理研究

提出了面向实时加工的基于特征的产品模型概念,讨论了基于二级工艺规划原理和三级插补原理的实时特征插补原理,以建立基于实时特征插补原理的新型数控系统．为实现运动控制、加工过程控制和在线质量控制的集成,建立智能加工的理论及其系统打下基础．     

嵌入式数控系统的结构可靠性分析

当前对数控系统可靠性研究主要是一种滞后的可靠性研究,是对于成熟产品使用中出现的故障的研究,侧重于系统维修方面,而很少从可靠性工程的角度来研究.为此,对处于可靠性工程早期阶段的嵌入式数控系统的结构可靠性进行了分析和建模,给出基于实时操作系统(RTOS)平台的嵌入式数控系统层次化模块结构图,在对数控系统功能和实际开发过程分析的基础上,对传统的Littlewood模型进行了改进,得到了改进的Littlewood模型,即Improved-Littlewood模型,并将模型用于一个嵌入式数控系统的结构可靠性设计阶段,给出了模型中各种参数矩阵的确定方法,最后得到系统的结构失效密度模型和可靠度模型,验证了方法的可行性.