一种解决预测控制输入信号受约束问题的方法

本文通过适当选取指标函数中各加权多项式，提出一种解决预测控制输入信号受约束问题的方法，由于使指标函数取最小值的当前控制信号与将来的控制信号无关，因而使控制算法大大简化。     

航空通信电台控制卡的设计

机载航空通信系统中的通信电台在地面维修、检查与测试时，需要提供电台工作需要的控制信号，建立电台的工作环境。在对机载航空通信系统控制关系和控制逻辑研究的基础上，采用ISA总线，研制出符合通信控制系统控制协议的控制卡，利用控制卡和控制软件，给出符合航空电台工作的串行控制信号和并行控制信号，控制电台工作状态，实现航空电台的地面控制和测试。     

调制信号波形任意的直接数字频率合成器的设计

本文提出了采用直接数字频率合成技术产生调制信号的新方法，用两个双口存储器分别存储频率调制信号序列和载波信号序列，频率调制信号序列与载波控制字在加法器相加，结果作为相位累加器的频率控制码，相位累加器输出信号对载波波形存储器寻址，经过数／模转换和低通滤波后，实现直接数字频率合成，该方法能产生载波波形和频率任意，调制波形任意的高精度调频信号，整个设计在一片可编程逻辑器件（CPLD）上实现，工作模式由计算机控制。     

UC3717对二相步进电机的控制

该文介绍了UC3717对二相步进电机的控制方案，以及80C196KC如何实现对UC3717硬件电路的驱动，简单地介绍了控制二相步进电机运转的驱动程序。UC3717是一种用来驱动二相步进电机的专用芯片，它根据信号发生电路产生的信号和信号的频率，产生驱动二相步进电机的驱动信号，从而控制电机的运行。步进电机的运行状态有转动和锁定，控制其运行的程序为步进电机驱动程序，可以通过设定韧值达到控制电机运转的目的。     

如何保证信号在远程传输中的质量？——赵洪海：“要获得理想监控图像质量，必须综合考虑各方面因素”

长期以来，在远程传输中，信号质量的优劣一直是人们关注的热点。近几年，随着监控技术的飞速发展，人们对高质量的信号需求也不断提高。监控系统中的信号可分为图像、控制，声音三类。控制信号一般是控制远端采集视频信号的摄像头云台转动，它的数据率较低，所占的带宽不高，但是其重要性并不会因此减少。相反，如果处理不好，就会对整个系统的稳定运行造成极大的影响。     

交流伺服系统模糊滑模控制器设计

针对交流伺服系统的位置控制，设计出一种新型滑模控制器，以前馈信号作为平均控制，采用模糊推理来调节开关控制的幅度，仿真研究表明，该控制器能较好地实现对指令信号的跟踪，具有较强的抗参数摄动和抗干扰能力，表现出良好的控制性能。     

交叉口信号控制方案描述仿真模型

提出了一种交叉口多相位信号控制方案描述仿真模型，该模型较好地反映交叉口信号控制方案及其在仿真时段内的动态变化过程，为交通仿真模型真实地反映路网交通状态以及利用仿真系统评价交叉口信号控制方案奠定了基础。     

扫视眼动系统的仿真研究

眼动系统在神经系统研究及临床应用上都具有重要作用。该文针对其中的扫视眼动系统，采用仿真的方法对其特性进行了部分研究，得出如下结论：①通过对各种切换方式（包括一、二、三次切换）神经控制信号对系统影响的仿真研究，进一步阐述了神经控制信号是一次切换的时间最优控制信号；②通过神经控制信号脉冲幅度Ｆｐ及切换时间ｔ１对模型输出影响的研究，明确提出了扫视系统神经控制信号的随机性与扫视眼动过程确定性的统一观点；③     

PLC在电梯控制中的应用

在用PLC实现电梯控制的基础上，利用旋转编码器的脉冲信号构成位置反馈，实现电梯的位移控制。通过PLC程序设计实现楼层计数、换速信号、门区和平层信号的数字控制，取代井道位置检测装置，提高了系统的可靠性和平层精度。     

PLC在发电机励磁调节的应用

电力系统中的励磁控制多用微机励磁调节器。介绍了用PLC实现发电机励磁调节功能的一种方法。这种方法以宽度可调的脉冲信号为励磁调节控制信号，反馈信号来自上住机向PLC串口发送的实时数据，避免了使用模拟量模块，所实现的恒无功控制的精度较高，调节时间在规范要求之内。     

智能控制：从学习控制到平行控制

20世纪60年代,学习控制开启了人类探究复杂系统控制的新途径,基于人工智能技术的智能控制随之兴起.本文以智能控制为主线,阐述其由学习控制向平行控制发展的历程.本文首先介绍学习控制的基本思想,描述了智能机器的架构设计与运行机理.随着信息科技的进步,基于数据的计算智能方法随之出现.对此,本文进一步简述了基于计算智能的学习控制方法,并以自适应动态规划方法为切入点分析非线性动态系统自学习优化问题的求解过程.最后,针对工程复杂性与社会复杂性互相耦合的复杂系统控制问题,阐述了基于平行控制的学习与优化方法求解思路,分析其在求解复杂系统优化控制问题方面的优势.智能控制思想经历了学习控制、计算智能控制到平行控制的演化过程,可以看出平行控制是实现复杂系统知识自动化的有效方法.     

Stochastic optimal control of networked control systems with control packet dropouts

This paper considers the stochastic optimal control problem for networked control systems(NCSs)with control packet dropouts.The proportional plus up to the third-order derivative(PD3)compensation strategy is adopted to compensate for control packet dropouts at the actuator by using the past control packets stored in the buffer.Based on the strategy,a new NCS structure model with packet dropouts is provided,where the packet dropout is assumed to obey the Bernoulli random binary distribution.In terms of the given model,the stochastic optimal control law is proposed. Numerical examples illustrate the effectiveness of the results.     

Supervisory control of switching control systems

In this paper, the problem of designing a switching policy for an adaptive switching control system is formulated as a problem of supervisory control of a discrete-event system (DES). Two important problems in switching control are then addressed using the DES formulation and the theory of supervisory control under partial observation. First, it is verified whether for a given set of controllers, a switching policy satisfying a given set of constraints on the transitions among controllers exists. If so, then a minimally restrictive switching policy is designed. Next, an iterative algorithm is introduced for finding a minimal set of controllers for which a switching policy satisfying the switching constraints exists. It is shown that in the supervisory control problem considered in this paper, limitations on event observation are the factors that essentially restrict supervisory control. In other words, once observation limitations are respected, limitations on control will be automatically satisfied. This result is used to simplify the proposed iterative algorithm for finding minimal controller sets.     

From robust control to adaptive control

The paper emphasizes the interaction between robust control, identification in closed loops and adaptive control. Robust control and recent algorithms developed for plant model identification in closed loops have led to new designs of adaptive control systems. Their performances are further enhanced by the use of multiple-model adaptive control, based on switching and tuning. These developments are illustrated by their application to the control of a flexible transmission system.     

Hyperbolic optimal control and fuzzy control

In this paper, we consider a new approach to fuzzy control which entails the formulation of a novel state-space representation and a new form of optimal control problem. Basically, in this new formulation, linear functions in the conventional state-space representation and cost functional are replaced by hyperbolic functions. We give a solution for this new, infinite-time, optimal control problem, which we call hyperbolic optimal control. Furthermore, we show that the resulting optimal controller is in fact a Mamdani-type fuzzy controller with Gaussian membership functions and center of gravity defuzzification. These results enable us to investigate analytically important issues, such as stability and robustness, pertaining to fuzzy controllers as well as add a powerful theoretical framework to the field of fuzzy control     

Survey on iterative learning control,repetitive control,and run-to-run control

In this paper, three control methods—iterative learning control (ILC), repetitive control (RC), and run-to-run control (R2R)—are studied and compared. Some mathematical transformations allow ILC, RC, and R2R to be described in a uniform framework that highlights their similarities. These methods, which play an important role in controlling repetitive processes and run-based processes, are collectively referred to as learning-type control in this paper. According to the classification adopted in this paper, learning-type control has two classes—direct form and indirect form. The main ideas and designing procedures for these two patterns are introduced, separately. Approximately 400 papers related to learning-type control are categorized. Statistical analysis of the resulting data reveals some promising fields for learning-type control. Finally, a flowchart based on the unique features of the different methods is presented as a guideline for choosing an appropriate learning-type control for different problems.     

Generalized control systems,boundary control systems,and delayed control systems

We investigate the relationships between the three classes of systems mentioned in the title: we show that systems with delays in control are a special instance of boundary control systems, and a boundary control system produces a generalized control system when projected onto its (unstable) eigenspaces. We use this observation to investigate the action of feedback on the dynamical behavior of systems with boundary controls. In particular, the well-known fact that spectral controllability is necessary and sufficient for a system with delays in control to be stabilizable is derived from a general rather than from anad hoc method. This paper was written according to the programs of the GNAFA-CNR group, with the financial support of the Italian “Ministero della Pubblica Istruzione.”     

Embedded model control: reconciling modern control theory and error-based control design

The paper addresses the problem of reconciling the modern control paradigm developed by R. Kalman in the sixties of the past century, and the centenary error based design of the proportional, integrative and derivative (PID) controllers. This is done with the help of the error loop whose stability is proved to be necessary and sufficient for the close loop plant stability. The error loop is built by cascading the uncertain plant to model discrepancies (causal, parametric, initial state, neglected dynamics), which are driven by the design model output and by arbitrary bounded signals, with the control unit transfer functions. The embedded model control takes advantage of the error loop and its equations to design appropriate algorithms of the modern control theory (state predictor, control law, reference generator), which guarantee the error loop stability and performance. A simulated multivariate case study shows modeling and control design steps and the coherence of the predicted and simulated performance.