轮胎硫化的建模与控制

本文提出一个轮胎硫化的建模与控制方案,它包括硫化温度场离线数值解和硫化时间实时控制两部分内容.数值解采用有限元法,其结果能够满足实时控制的需要.建立在此基础上的控制方案可以根据硫化温度的采样值实时计算硫化程度,进而控制硫化时间以使轮胎质量达到最佳.数值解和控制方案是作为一个整体统一考虑的.     

胶鞋硫化自控系统的设计

“硫化”是各种橡胶制品生产过程中的一道重要工序。橡胶的硫化过程,就是将已成型的橡胶(生胶)制品,置于高温环境,使之改变其物理性能的过程。这一过程与环境温度、硫化时间直接有关,硫化温度过高或硫化时间过长,会造成产品的“过硫”;反之,若硫化温度过低或硫化时间过短,则可能导致产品“欠硫”。只有正确的掌握硫化温度、时间,才能获得物理性能最佳的橡胶制品。胶鞋的硫化过程是在硫化罐中进行的。罐内设有排气管,通过排管对罐内空气进行     

硫化机群测控系统的研究与设计

在汽车内胎的生产过程中,内胎硫化的工艺要求是,硫化温度158?C±2?C,并在此温度下硫化5分钟。这就要求对硫化机的硫化温度、硫化时间等参数进行严格、精确的控制。本文介绍了利用一台微机对内胎硫化机群作温度巡回检测与控制,严格按照内胎硫化工艺的数学模型,精确地控制每台硫化机的硫化温度和时间。该系统已投入应用,运行稳定、可靠,取得了很好的经济效益。     

可编程控制器在内胎硫化中的应用

本文给出了基于ＰＬＣ的内胎等效硫化控制系统，介绍了等效硫化原理、控制算法、硬件和软件结构。     

轮胎硫化的脉冲式热源控制

轮胎硫化是产品加工的最后也是最关键的一道工序，现一般进行的热硫化均不能使轮胎内部均匀处于正硫化阶段，尽管可以在配方和硫化工艺上做大量工作，都难以使该问题得到很好解决。本文在简述了轮胎的传热和硫化机理之后，提出了变设定温度的硫化过程，由该方案实施的双模硫化机不仅可以使轮胎内部均匀处于正硫化，而且可以大大节约能源，约可节能２０％左右。     

平板硫化机微机控制器

在橡胶制品的生产过程中,硫化是直接影响橡胶制品质量的关键环节,而硫化温度和硫化时间的控制是硫化过程的关键所在。通常用二位式温控仪控制硫化温度,由于电热平板的热惯性,用这种方法会产生严重的温度波动。硫化时间往往采用时间继电器控制,在温度波动大的情况下,实际硫化时间得不到充分的补偿。上述问题都会影响产品质量。为此,我们研制了平板硫化机微机控制器,实现硫化温度和时间的控制,保证了平板的温度控制在设定值附近。当温度在设定值上下微小波动时,硫化时间可以按硫化工艺进行修正,实现等效硫化,从而保证了产品质量。     

可编程控制器在内胎等效硫化过程控制中的应用

介绍内胎硫化生产中等效硫化原理的应用以及可编程控制器（PLC）在内胎等效硫化中的应用。     

可编程控制器在内胎等效硫化过程控制中的应用

介绍内胎硫化生产中等效硫化原理的应用以及可编程控制器（ＰＬＣ）在内胎等效硫化中的应用。     

基于神经网络的橡胶硫化控制系统

提出一种基于神经网络的橡胶硫化控制方案,通过RBF神经网络辨识对象模型, 并设计了基于BP网络的PID控制算法.仿真结果表明, 该系统能够适应硫化过程的非线性及时变特性.     

橡胶硫化过程智能控制系统

以工控机为核心的橡胶硫化过程智能控制系统，具有数据采集、超限报警、硫化效应计算，以及手动、自动控制切换等功能，同时对硫化过程中的温度采用了先进的模糊控制策略，在提高了控制精度的同时，也提高了硫化产品的质量。软件采用面向对象的VC 高级语言开发，利用多线程、ActiveX控件、动态链接库等技术，不仅具有友好的人机界面，同时也提高了控制系统的实时性和稳定性。     

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.