车载冰箱变频控制器的开发

根据车载冰箱的功能需求,以德州仪器公司的TMS320F28035芯片为主控制芯片,设计一款能根据用户的指令及冰箱的温度控制无刷直流电机变频调速的控制器;此控制器由3个模块组成:一是开关电源模块,采用boost升压电路,将12/24V的汽车电源电压升至40V供给电机驱动模块;二是电机驱动模块,通过反电动势法检测无刷直流电机的位置,控制电机启停及变频调速;三是通信模块,采用抗干扰能力强的485通信方式实现控制器与上位机的通信;实验结果表明,控制器的可靠性高,制冷能力强,最低温度可低于-19°C,比普通车载冰箱更节能,满足了使用的要求。     

可编程脉冲产生器的设计及应用

文中针对脉冲产生器的市场供应与实际应用情况,给出了一种以单片机为CPU核心的可编程脉冲产生器的设计思路,阐述了完成该产生器的硬件结构和软件设计方法,并给出相应的硬件电路和软件设计流程;为了验证设计的合理性,将其应用于某测控系统的步进电机控制器中,并进行实验测试,结果表明该设计具有低成本、高可靠性等特点,特别是其脉冲宽度与周期可编程预置,不仅增加了设备的使用灵活性,而且扩大了应用范围,可推广应用于多种功能的测控系统中。     

基于Ziegler-Nichols频率响应方法的自适应PID控制

提出一种基于Ziegler-Nichols频率响应方法的自适应PID控制器,它通过控制回路正常运行中的过程对象输入输出数据在线辨识出过程对象重要的临界频率响应特性,然后基于Zieger-Nichols整定规则或改进的方法在线更新PID控制器参数.PID控制器的自适应过程不需要系统的任何先验知识,也不需要建立任何对象模型,可以保证控制回路始终运行在最佳状态.仿真实验表明了自适应PID控制的有效性和可行性.     

基于ENC424J600的以太网与串行接口转换技术

论述了在STM32系列的MCU上移植TCP/IP协议栈LwIP,并利用新型以太网控制器ENC424J600实现网络数据传输功能。该系统主要完成的功能是接收上位机发送的数据,并将这些数据转换成通用串行接口输出。ENC424J600是Microchip新推出的一款以太网控制器,其速度优于ENC28J60,同时为用户提供两种接口模式。本文介绍了其基于SPI接口模式下的实现过程,详细描述了该网卡驱动的硬件电路和软件流程。     

以ARM为核心的嵌入式体感遥控器设计

介绍一种以ARM为核心的嵌入式服务机器人体感遥控器的设计。硬件上,本遥控器采用具有ARM Cortex—M3内核的STM32F103C8T6作为核心处理器,选用ST公司的iNEMO惯性导航模块进行手部姿态的识别,同时还具有LCD显示模块、无线收发模块和电源模块;软件上,采用嵌入式操作系统μC／OS—Ⅱ实现多任务的调度和外围设备的管理。经实验验证,本遥控器具有高稳定性、高实时性、高可靠性、低误码率等优点。     

Improved results on frequency‐weighted balanced truncation and error bounds

In this paper, we present some new results on frequency‐weighted balanced truncation which is a significant improvement on Lin and Chiu's frequency‐weighted balanced truncation technique. The reduced‐order models, which are guaranteed to be stable in the case of double‐sided weighting, are obtained by direct truncation. Two sets of simple, elegant and easily calculatable a priori error bounds are also derived. Numerical examples and comparison with other well‐known techniques show the effectiveness of the proposed technique. Copyright © 2011 John Wiley & Sons, Ltd.     

An on-line learning neural controller for helicopters performing highly nonlinear maneuvers

This paper presents an on-line learning adaptive neural control scheme for helicopters performing highly nonlinear maneuvers. The online learning adaptive neural controller compensates the nonlinearities in the system and uncertainties in the modeling of the dynamics to provide the desired performance. The control strategy uses a neural controller aiding an existing conventional controller. The neural controller is based on a online learning dynamic radial basis function network, which uses a Lyapunov based on-line parameter update rule integrated with a neuron growth and pruning criteria. The online learning dynamic radial basis function network does not require a priori training and also it develops a compact network for implementation. The proposed adaptive law provides necessary global stability and better tracking performance. Simulation studies have been carried-out using a nonlinear (desktop) simulation model similar to that of a BO105 helicopter. The performances of the proposed adaptive controller clearly shows that it is very effective when the helicopter is performing highly nonlinear maneuvers. Finally, the robustness of the controller has been evaluated using the attitude quickness parameters (handling quality index) at different speed and flight conditions. The results indicate that the proposed online learning neural controller adapts faster and provides the necessary tracking performance for the helicopter executing highly nonlinear maneuvers.     

基于PR控制器的直接转矩控制策略研究

针对传统直接转矩控制中采用开关表控制造成转矩和电流脉动,以及传统调节器不能实现对交流输入信号的无静差控制等问题,基于电机空间电压矢量的转矩和磁链2个分量解耦的控制方式和PR控制器能够在静止坐标系下实现对交流输入信号的无静差控制,将PR控制器用于永磁同步电机的直接转矩控制中,并由此设计出磁链和转矩的双PR控制器。同时,在定子磁链的观测中,采用基于转子位置和定子电流的新型定子磁链估计方法。试验结果证明,将PR控制器对交流输入信号的无静差跟踪特性应用于基于空间电压矢量调制的直接转矩控制中,系统能获得优良的动态和静态响应,取得了显著的应用成效,由此验证了所提方法的正确性和可行性。     

CLP(ℜ) and some electrical engineering problems

The Constraint Logic Programming Scheme defines a class of languages designed for programming with constraints using a logic programming approach. These languages are soundly based on a unified framework of formal semantics. In particular, as an instance of this scheme with real arithmetic constraints, the CLP() language facilitates and encourages a concise and declarative style of programming for problems involving a mix of numeric and non-numeric computation.In this paper we illustrate the practical applicability of CLP() with examples of programs to solve electrical engineering problems. This field is particularly rich in problems that are complex and largely numeric, enabling us to demonstrate a number of the unique features of CLP(). A detailed look at some of the more important programming techniques highlights the ability of CLP() to support well-known, powerful techniques from constraint programming. Our thesis is that CLP() is an embodiment of these techniques in a language that is more general, elegant and versatile than the earlier languages, and yet is practical.An earlier version of this paper appeared in the proceedings of the 4th International Conference on Logic Programming, Melbourne, May 1987. Much of this work was carried out while the authors were at Monash University, Melbourne, Australia.     

Higher-Order and Modal Logic as a Framework for Explanation-Based Generalization

Certain tasks, such as formal program development and theorem proving, fundamentally rely upon the manipulation of higher-order objects such as functions and predicates. Computing tools intended to assist in performing these tasks are at present inadequate in both the amount of knowledge they contain (i.e., the level of support they provide) and in their ability to learn (i.e., their capacity to enhance that support over time). The application of a relevant machine learning technique—explanation-based generalization (EBG)—has thus far been limited to first-order problem representations. We extend EBG to generalize higher-order values, thereby enabling its application to higher-order problem encodings.Logic programming provides a uniform framework in which all aspects of explanation-based generalization and learning may be defined and carried out. First-order Horn logics (e.g., Prolog) are not, however, well suited to higher-order applications. Instead, we employ Prolog, a higher-order logic programming language, as our basic framework for realizing higher-order EBG. In order to capture the distinction between domain theory and training instance upon which EBG relies, we extend Prolog with the necessity operator of modal logic. We develop a meta-interpreter realizing EBG for the extended language, Prolog, and provide examples of higher-order EBG.