飞机座舱压力专家模糊预控方法仿真研究

针对新型战机高空高速俯冲及俯冲转平飞情况下带来的座舱压力控制难题,提出一种飞机座舱压力专家模糊PID预控方法。在飞机高速俯冲时,基于压力调节系统时间延迟及飞机高度变化率改进常规模糊PID控制策略并提高座舱压力调整速度;在飞机状态转换时,利用专家控制器根据知识库及状态转换时间预测调整模糊PID控制策略,并引入重置机制以改善调整性能。经过知识库的动态学习,得出飞机状态转换时,采用模糊PID控制、模糊预控、重置PID控制参数的专家控制策略具有最佳的控制效果的结论。通过仿真实验验证了该方法的有效性。     

Adaptive fuzzy PI controller with shifted control singletons

It is well known the fact that the design of a fuzzy control system is based on the human expert experience and control engineer knowledge regarding the controlled plant behavior. As a direct consequence, a fuzzy control system can be considered as belonging to the class of intelligent expert systems. The tuning procedure of a fuzzy controller represents a quite difficult and meticulous task, being based on prior data regarding good knowledge of the controlled plant. The complexity of the tuning procedure increases with the number of the fuzzy linguistic variables and, consequently, of the fuzzy inference rules and thus, the tuning process becomes more difficult. The paper presents a new design strategy for such expert fuzzy system, which improves their performance without increasing the number of fuzzy linguistic variables. The novelty consists in extending the classic structure of the fuzzy inference core with an intelligent module, which tunes one of the control singletons, providing a significant simplification of the design and implementation procedure. The proposed strategy implements a logical, not physical, supplementation of the linguistic terms associated to the controller output. Therefore, a fuzzy rules set with a reduced number of linguistic terms is used to implement the expert control system. This logical supplementation is based on an intelligent algorithm which performs a shifting of only one of the control singletons (the singleton associated to the SMALL_ linguistic variable), its value becoming variable, a fact that allows an accurate control and a better performance for the expert control system. The logic of this intelligent algorithm is to initially provide a high controller output, followed by a slowdown of the control signal near to the operating set point. The main advantage of the proposed expert control strategy is its simplicity: a reduced number of linguistic terms, combined with an intelligent tuning of a single parameter, can provide results as accurate as other more complex available solutions involving tuning of several parameters (well described by the technical literature). Also, a simplification of the preliminary off-line tuning procedure is performed by using a reduced set of fuzzy rules. The generality of the proposed expert control strategy allows its use for any other controlled process.     

基于对象模型的自组织专家系统

在研制的一个基于对象模型的自组织专家系统中 ,通过对机器人的行走装置进行模型化 ,建立了对象的模糊知识库 ,并根据控制的目标 ,设计了推理机。系统无需精确的数学模型 ,能根据输入、输出变量 ,自动修改控制规则 ,达到优化控制的目的。     

Modelling the software development process using an expert simulation system having fuzzy logic

This paper describes an intelligent computerized tool designed to aid managers of software development projects in planning, managing and controlling the development process of medium- to large-scale software projects. Systems dynamics is used to model and simulate the dynamic process of software development. The software development process is affected by some imprecise and vague variables that are treated as fuzzy variables. The simulation model is integrated with two expert systems. The fuzzy input variables to the system dynamics simulation model are handled by an input expert system having fuzzy logic. This expert system is designed to check the consistency of input variables. The simulation results are analysed by an output expert system having fuzzy logic. This expert system is designed to make recommendations based on experimentation with the simulation model.     

基于对象模型的自适应模糊专家控制系统

文中介绍了一个基于对象模型的自适应模糊专家控制系统。在该系统里,能动地机器人的行走装置进行模型化,建立了对象的模糊知识库,并根据自适应模糊控制的目标设计了推理机。系统无需铁数学模型,能根据输入、输出变量自动个性控制规则,达到优化控制钵文还介绍了对象模型的模糊知识表示方法和模糊知识库的结构以及对象模型的模糊控制推理。     

Speed tracking control using an ANFIS model for high-speed electric multiple unit

The high-speed electric multiple unit (EMU) is a complex, uncertain and nonlinear dynamic system. The traditional approach to operating the high-speed EMU is based upon manual operation. To improve the performance of high-speed EMU, this paper develops a control dynamic model to capture the motion of the high-speed EMU and then uses it to design a desirable speed tracking controller for EMU. We exploit a data-driven adaptive neurofuzzy inference system (ANFIS) to model the running process. Based on the ANFIS model, we propose a generalized predictive control algorithm to ensure the high-precision speed tracking of the high-speed EMU. The simulation results on the actual CRH380AL (China railway high-speed EMU type-380AL) operation data show that the proposed approach could ensure the safe, punctual, comfortable and efficient operation of high-speed EMU.     

Knowledge based fuzzy control of systems

A method of fuzzy control of systems based on a concept of human thinking is investigated. The fuzzy controller consists of a knowledge base containing a number of time responses of a process. The elements of this base are cells in which an input and a corresponding output of a system are stored. The cells are organized according to different types of relations. The result is a knowledge structure which contains a model of the process to be controlled. The following phases are distinguished: the learning phase, during which the relation between the imput and the output of a system is learned and the knowledge structure is constructed; and a phase in which the knowledge structure is applied to control the process. The method also provides an indication of the reliability of the control action     

高速列车速度跟踪神经网络PID控制器的设计

高速列车速度跟踪控制系统是一个复杂的非线性系统,难以取得高精度的跟踪性能。为了减少速度跟踪误差,设计了高速列车神经网络PID控制器。首先建立了描述列车运行过程的单位移多质点模型,该模型考虑了列车的基本阻力和附加阻力以及车厢之间的相互作用力。然后阐述了BP神经网络PID控制,并设计了列车速度跟踪控制器,根据速度误差用神经网络PID控制决定牵引力和制动力。最后与模糊控制和常规PID控制进行了仿真对比,结果表明,神经网络PID控制具有很小的速度跟踪误差和优越的速度跟踪性能,可以满足列车正点运行的需求。     

A Fuzzy Petri Nets approach for railway traffic control in case of abnormality: Evidence from Taiwan railway system

Railway traffic control by dispatchers in case of abnormality is critical to assure the service quality of a railway system’s operation. However, this unique professional knowledge often lies in the dispatcher’s mind. Therefore, this study aims to transform a train dispatcher’s expertise into a useful knowledge rule. The fuzzy Petri Net approach is adopted to formulate the decision rules of train dispatchers in case of abnormality as the basis for future development of a dispatching decision support system. The dispatching decision rules, factors, and possible options when perturbation happens are collected via expert interviews and literature reviews. This study discusses the abnormal scenarios, including centralized traffic control system failure, automatic train protection failure, and locomotive failure. A case study of a line section of Taiwan’s railway network is implemented and the empirical result could be used as a reference in railway dispatching in case of abnormality.     

Robust adaptive fuzzy control scheme for nonlinear system with uncertainty

In this paper, a robust adaptive fuzzy control scheme for a class of nonlinear system with uncertainty is proposed. First, using prior knowledge about the plant we obtain a fuzzy model, which is called the generalized fuzzy hyperbolic model (GFHM). Secondly, for the case that the states of the system are not available an observer is designed and a robust adaptive fuzzy output feedback control scheme is developed. The overall control system guarantees that the tracking error converges to a small neighborhood of origin and that all signals involved are uniformly bounded. The main advantages of the proposed control scheme are that the human knowledge about the plant under control can be used to design the controller and only one parameter in the adaptive mechanism needs to be on-line adjusted.     

Robust adaptive fuzzy control scheme for nonlinear system with uncertainty

In this paper, a robust adaptive fuzzy control scheme for a class of nonlinear system with uncertainty is proposed. First, using prior knowledge about the plant we obtain a fuzzy model, which is called the generalized fuzzy hyperbolic model （GFHM）. Secondly, for the case that the states of the system are not available an observer is designed and a robust adaptive fuzzy output feedback control scheme is developed. The overall control system guarantees that the tracking error converges to a small neighborhood of origin and that all signals involved are uniformly bounded. The main advantages of the proposed control scheme are that the human knowledge about the plant under control can be used to design the controller and only one parameter in the adaptive mechanism needs to be on-line adjusted.     

Intelligent adaptive control of bioreactors

Supervised model-based self-tuning control of fermentation processes is addressed. The diversity, nonlinearity and time-varying nature of these processes make their control a challenging task. Conventional linear (PID) controllers with fixed parameters cannot meet the increasing performance requirements over the whole operating range. In the approach pursued in this research, a local linear model is identified at the current working point by using a limited amount input–output data obtained through an identification experiment. A linear controller is then tuned on the basis of this model. To minimize the intervention into the process operation, this tuning procedure is only initiated if the performance of the current controller deteriorates. To this end, a supervisory expert system is designed whose tasks are to monitor the process performance, design an appropriate identification experiment, validate the obtained model and tune the controller. The supervisory system is based on a combination of a state automaton with a rule-based fuzzy inference system. Experimental results have demonstrated the feasibility of this approach.     

智能集成控制技术在铝电解过程控制中的应用

研究了将专家系统、神经网络、模糊控制、混沌优化等先进智能技术相集成，共同完成铝电解生产过程控制的智能集成控制技术，建立了基于神经网络的电解槽参数预测模型，设计了槽况解析神经网络专家系统，设计了基于混沌优化的电解质温度模糊控制系统及电解槽极距专家模糊控制系统．实验结果表明，系统能有效地实现预焙铝电解槽智能集成控制，实现槽状态在线解析，并能达到优良的生产控制指标，为企业带来了巨大的经济效益和社会效益．     

面向安全评估的高速铁路CTCS-2列控系统安全性测试环境

高速铁路CTCS-2列控系统是典型的安全苛求系统。根据安全苛求系统的特点,针对高速铁路CTCS-2列控系统的安全性测试和评估需求,设计了场景—事件驱动的测试脚本语言SED_TSL,提出了高速铁路CTCS-2列控系统测试环境的功能、系统框架、测试策略,实现了基于SED_TSL的CTCS-2列控系统自动化测试环境,并投入到铁道部的CTCS-2列控系统产品制式检测中,有效地实现了列控系统产品的功能与安全性测试。     

Quasi sliding mode‐based single input fuzzy self‐tuning decoupled fuzzy PI control for robot manipulators with uncertainty

This paper presents a robust adaptive control strategy for robot manipulators, based on the coupling of the fuzzy logic control with the so‐called sliding mode control (SMC) approach. The motivation for using SMC in robotics mainly relies on its appreciable features. However, the drawbacks of the conventional SMC, such as chattering effect and required a priori knowledge of the bounds of uncertainties can be destructive. In this paper, these problems are suitably circumvented by adopting a reduced rule base single input fuzzy self tuning decoupled fuzzy proportional integral sliding mode control approach. In this new approach a decoupled fuzzy proportional integral control is used and a reduced rule base single input fuzzy self‐tuning controller as a supervisory fuzzy system is added to adaptively tune the output control gain of the decoupled fuzzy proportional integral control. Moreover, it is proved that the fuzzy control surface of the single‐input fuzzy rule base is very close to the input/output relation of a straight line. Therefore, a varying output gain decoupled fuzzy proportional integral sliding mode control approach using an approximate line equation is then proposed. The stability of the system is guaranteed in the sense of the Lyapunov theorem. Simulations using the dynamic model of a 3DOF planar manipulator with uncertainties show the effectiveness of the approach in high speed trajectory tracking problems. The simulation results that are compared with the results of conventional SMC indicate that the control performance of the robot system is satisfactory and the proposed approach can achieve favorable tracking performance, and it is robust with regard to uncertainties and disturbances. Copyright © 2011 John Wiley & Sons, Ltd.     

模糊推理协处理器芯片

模糊推理协处理器VLSI芯片F200采用0.8μm CMOS工艺,作为一种模糊 控制器,主要用于实时过程控制和其它适合的应用场合,例如机器人控制、分类器、专家系 统等.F200芯片支持多个模糊知识库工作,支持最常用的两种模糊模型,Mamdani和 Trakagi-Sugeno模型.芯片精度12位,主频20MHz,推理速度约为每秒1.2M条模糊规则.     

A reinforced learning control using iterative error compensation for uncertain dynamical systems

This paper investigates a learning control using iterative error compensation for uncertain systems to enhance the precision of a high speed, computer-controlled machining process. It is specially useful in mass-produced parts produced by a high-speed machine tool system. This method uses an iterative learning technique which adopts machine commands and cutting errors experienced from previous manoeuvres as references for compensation actions in the current manoeuvre. Non-repetitive disturbances and nonlinear dynamics of the cutting processes and servo systems of the machine which greatly affect the convergence of the learning control systems were studied in this research. State feedback and output feedback methods were used for controller design. Stability and performance of learning control systems designed via the proposed method were verified by simulations on a single degree of freedom servo positioning system.     

基于模糊自适应控制算法的微机配料系统的设计

针对传统配料系统的控制方法普遍存在控制精度不高、实时性差等缺陷,采用模糊理论和专家系统相结合的模糊自适应控制算法设计了一种模糊自适应微机配料系统。该配料系统采用原料设定值与实际给料值的偏差和偏差变化率作为模糊控制器输入,给/排料速度值作为输出,利用专家系统在线评判称量状态和控制精度,自动调整称量速度和飞料量控制,实现了精度、速度的最佳结合。实际应用表明,该算法在系统精度和速度上均有所提高,达到了设定的控制要求。