DCS在甲醇精馏回收系统中的应用

本文介绍了ＳＵＰＣＯＮＪＸ－１００在甲醇精馏回收系统中的应用，并就控制方案，运行情况，效果及ＤＣＳ的基本结构，模糊控制等作了分析与说明。关键词：ＤＣＳ模糊控制     

具有串联的结构系统的预测控制及并行设计

对具有串联结构的多干扰系统提出了在线ＤＭＣ控制方案，并在已有ＤＭＣ控制的基础上给出并行算法及结构上的设计，这种多闭环的近似模型的扩展ＤＭＣ方法使系统的稳定性，抗扰性及鲁棒性都达到了指标，由于设计中充分利用了系统的冗余信息及并行性，使系统的快速性得到很大提高。     

具有串联结构系统的预测控制及并行设计

对具有串联结构的多干扰系统提出了在线ＤＭＣ控制方案，并在已有ＤＭＣ控制的基础上给出并行算法及结构上的设计，这种多闭环的近似模型的扩展ＤＭＣ方法使系统的稳定性、抗扰性及鲁棒性都达到了指标。由于设计中充分利用了系统的冗余信息及并行性，使系统的快速性得到很大提高。     

小型DCS在工业锅炉控制中的应用

以２０ｔ／ｈ燃气炉为例，介绍小型ＤＣＳ（集散控制系统）应用于工业锅炉控制时的结构和功能，给出了典型回路的控制方案和利用热量信号实现风煤比寻优的算法。     

软PLC（SoftPLC）技术,产品及控制方案探讨

讨论了软ＰＬＣ（ＳｏｆｔＰＬＣ）技术的相关概念、组成结构,介绍了市场上出现的ＳｏｆｔＰＬＣ产品,探讨了软ＰＬＣ技术在工业领域的应用控制方案。     

不确定性机器人的神经网络补偿控制

本文研究具有不确定性的机器人的轨迹跟踪控制问题。提出了一种由计算力矩控制器和神经网络补偿控制器构成的控制方案。探讨了一种用神经网络估计机器人系统不确定性的途径。给出了神经补偿控制器的设计方法，并证明了闭环系统的收敛性。仿真结构表明所提方案具有很好的鲁棒性和抗干扰能力。     

一种基于Web技术的新型DCS结构与实现

作者根据对过程控制系统和当今计算机网络技术发展的掌握,提出了一种基于Ｗｅｂ的新型ＤＣＳ解决方案－Ｂｒｏｗｅｒ／Ｓｅｒｖｅｒ结构。该方案可以通过Ｉｎｔｅｒｎｅｔ实现对控制对象的远程监视和控制,文章对这一新系统与基于Ｃｌｉｅｎｔ／Ｓｅｒｖｅｒ结构的传统ＤＣＳ作一比较,并给出了一个实际应用的工程项目。     

机器人变结构自适应控制的研究

本文提出了一种新的机器人变结构自适应轨迹跟踪控制，根据滑模存在条件，独立地推导出了一种实时预测滑模参数Ｃ的递推算法，克服了机器人变结构控制中滑模参数选择的盲目性。同时，为了更有效地削弱抖振现象，本文提出了一种新的变边界层厚度的饱和函数方法。本文的控制方案既保证了系统的强鲁棒性，又能实现高精度的快速跟踪控制。以三自由度烟叶搬运机器人为对象的仿真实验结果，表明了本文所采取的控制方法的有效性和可行性。     

JDBC在Web数据库中的应用

本文分析了ＪＤＢＣ的结构，并提出了ＪＤＢＣ与数据库互连的方案，最后给出了一个ＪＤＢＣ与Ｗｅｂ数据库交互的实例。     

I／A S在延迟焦化装置中的应用

应用Ｉ／Ａ Ｓ对分馏塔顶压力与气压机入口流量选择控制，介绍工艺机理。改进前，后控制方案，利用计算模块ＣＡＬＣ实现程控开关，还介绍了气体报警在Ｉ／Ａ Ｓ上的实现。     

Prediction of a Lorenz chaotic attractor using two-layer perceptron neural network

This paper investigates the prediction of a Lorenz chaotic attractor having relatively high values of Lypunov's exponents. The characteristic of this time series is its rich chaotic behavior. For such dynamic reconstruction problem, regularized radial basis function (RBF) neural network (NN) models have been widely employed in the literature. However, author recommends using a two-layer multi-layer perceptron (MLP) NN-based recurrent model. When none of the available linear models have been able to learn the dynamics of this attractor, it is shown that the proposed NN-based auto regressive (AR) and auto regressive moving average (ARMA) models with regularization have not only learned the true trajectory of this attractor, but also performed much better in multi-step-ahead predictions. However, equivalent linear models seem to fail miserably in learning the dynamics of the time series, despite the low values of Akaike's final prediction error (FPE) estimate. Author proposes to employ the recurrent NN-based ARMA model with regularization which clearly outperforms all other models and thus, it is possible to obtain good results for prediction and reconstruction of the dynamics of the chaotic time series with NN-based models.     

带有脉冲模的广义控制系统设计

本文对带有脉冲模的广义控制系统给出了一种输入输出可逆变换标准结构分解。利用这种标准结构分解,使得广义控制系统设计变得非常直观和简单,易于工程实现。     

Simulating Robots Without Conventional Physics: A Neural Network Approach

The construction of physics-based simulators for use in Evolutionary Robotics (ER) can be complex and time-consuming. Alternative simulation schemes construct robotic simulators from empirically-collected data. Such empirical simulators, however, also have associated challenges. This paper therefore investigates the potential use of Artificial Neural Networks, henceforth simply referred to as Neural Networks (NNs), as alternative robotic simulators. In contrast to physics models, NN-based simulators can be constructed without requiring an explicit mathematical model of the system being modeled, which can simplify simulator development. The generalization abilities of NNs, along with NNs’ noise tolerance, suggest that NNs could be well-suited to application in robotics simulation. Investigating whether NNs can be effectively used as robotic simulators in ER is thus the endeavour of this work. Two robot morphologies were selected on which the NN simulators created in this work were based, namely a differentially steered robot and an inverted pendulum robot. Accuracy tests indicated that NN simulators created for these robots generally trained well and could generalize well on data not presented during simulator construction. In order to validate the feasibility of the created NN simulators in the ER process, these simulators were subsequently used to evolve controllers in simulation, similar to controllers developed in related studies. Encouraging results were obtained, with the newly-evolved controllers allowing experimental robots to exhibit obstacle avoidance, light-approaching behaviour and inverted pendulum stabilization. It was thus clearly established that NN-based robotic simulators can be successfully employed as alternative simulation schemes in the ER process.     

Neural network-based modelling of subsonic cavity flows

A fundamental problem in the applications involved with aerodynamic flows is the difficulty in finding a suitable dynamical model containing the most significant information pertaining to the physical system. Especially in the design of feedback control systems, a representative model is a necessary tool constraining the applicable forms of control laws. This article addresses the modelling problem by the use of feedforward neural networks (NNs). Shallow cavity flows at different Mach numbers are considered, and a single NN admitting the Mach number as one of the external inputs is demonstrated to be capable of predicting the floor pressures. Simulations and real time experiments have been presented to support the learning and generalization claims introduced by NN-based models.     

Temperature compensation of micromachined silicon hot wire sensor using ANN technique

Temperature is one of the most important factors influencing accurate silicon sensor devices and is one of the largest sources of error in measurement. In this paper, a model based on Neural Networks (NN), has been implemented to generate fluid velocity data, knowing fluid temperature measurements. The proposed model based on neural networks can provide the calibrated response characteristics irrespective of change in the sensor characteristics due to change in ambient temperature. The NN-based sensor model automatically calibrates and compensates with high accuracy for the nonlinear response characteristics and nonlinear dependency of the sensor characteristics on the environmental parameters. Through extensive simulated experiments, we have shown that the NN-based silicon hot wire sensor model can provide flow speed readout with a maximum full-scale error of only 1.5% over a temperature range from 0 to 40°C for nonlinear dependencies.     

Hybrid trigonometric compound function neural networks for tracking control of a nonholonomic mobile robot

The purpose of this paper is to propose a hybrid trigonometric compound function neural network (NN) to improve the NN-based tracking control performance of a nonholonomic mobile robot with nonlinear disturbances. In the mobile robot control system, two NN controllers embedded in the closed-loop control system have the simple continuous learning and rapid convergence capability without the dynamics information of the mobile robot to realize the tracking control of the mobile robot. The neuron functions of the hidden layer in the three-layer feedforward network structure consist of the compound cosine function and the compound sine function combining a cosine or a sine function with a unipolar sigmoid function. The main advantages of this NN-based mobile robot control system are better real-time control capability and control accuracy by use of the proposed NN controllers for a nonholonomic mobile robot with nonlinear disturbances. Through simulation experiments applied to the nonholonomic mobile robot with the nonlinear disturbances of dynamics uncertainty and external disturbances, the simulation results show that the proposed NN control system of a nonholonomic mobile robot has better real-time control capability and control accuracy than the compound cosine function NN control system of a nonholonomic mobile robot and then verify the effectiveness of the proposed hybrid trigonometric compound function NN controller for improving the tracking control performance of a nonholonomic mobile robot with nonlinear disturbances.     

A Neuro-interface with fuzzy compensator for controlling nonholonomic mobile robots

This paper describes a control method for mobile robots represented by a nonlinear dynamical system, which is subjected to an output deviation caused by drastically changed disturbances. We here propose some controllers in the framework of neuro-interface. It is assumed that a neural network (NN)-based feedforward controller is construcetd by following the concept of virtual master-slave robot, in which a virtual master robot as a feedforward controller is used to control the slave (i.e., actual) robot. The whole system of the present neuro-interface consists of an NN-based feedforward controller, a feedback PD controller and an adaptive fuzzy feedback compensator. The NN-based feedforward controller is trained offline by using a gradient method, the gains of the PD controller are to be chosen constant, and the adaptive fuzzy compensator is constructed with a simplified fuzzy reasoning. Some simulations are presented to confirm the validity of the present approach, where a nonholonomic mobile robot with two independent driving wheels is assmued to have a disturbance due to the change of mass for the robot.     

Neural network-based sliding mode adaptive control for robot manipulators

This paper addresses the robust trajectory tracking problem for a robot manipulator in the presence of uncertainties and disturbances. First, a neural network-based sliding mode adaptive control (NNSMAC), which is a combination of sliding mode technique, neural network (NN) approximation and adaptive technique, is designed to ensure trajectory tracking by the robot manipulator. It is shown using the Lyapunov theory that the tracking error asymptotically converge to zero. However, the assumption on the availability of the robot manipulator dynamics is not always practical. So, an NN-based adaptive observer is designed to estimate the velocities of the links. Next, based on the observer, a neural network-based sliding mode adaptive output feedback control (NNSMAOFC) is designed. Then it is shown by the Lyapunov theory that the trajectory tracking errors, the observer estimation errors asymptotically converge to zero. The effectiveness of the designed NNSMAC, the NN-based adaptive observer and the NNSMAOFC is illustrated by simulations.     

Fibre Bragg gratings in structural health monitoring—Present status and applications

In-service structural health monitoring (SHM) of engineering structures has assumed a significant role in assessing their safety and integrity. Fibre Bragg grating (FBG) sensors have emerged as a reliable, in situ, non-destructive tool for monitoring, diagnostics and control in civil structures. The versatility of FBG sensors represents a key advantage over other technologies in the structural sensing field. In this article, the recent research and development activities in structural health monitoring using FBG sensors have been critically reviewed, highlighting the areas where further work is needed. A few packaging schemes for FBG strain sensors are also discussed. Finally a few limitations and market barriers associated with the use of these sensors have been addressed.     

Dynamically balanced optimal gaits of a ditch-crossing biped robot

This paper deals with the generation of dynamically balanced gaits of a ditch-crossing biped robot having seven degrees of freedom (DOFs). Three different approaches, namely analytical, neural network (NN)-based and fuzzy logic (FL)-based, have been developed to solve the said problem. The former deals with the analytical modeling of the ditch-crossing gait of a biped robot, whereas the latter two approaches aim to maximize the dynamic balance margin of the robot and minimize the power consumption during locomotion, after satisfying a constraint stating that the changes of joint torques should lie within a pre-specified value to ensure its smooth walking. It is to be noted that the power consumption and dynamic balance of the robot are also dependent on the position of the masses on various links and the trajectory followed by the hip joint. A genetic algorithm (GA) is used to provide training off-line, to the NN-based and FL-based gait planners developed. Once optimized, the planners will be able to generate the optimal gaits on-line. Both the NN-based and FL-based gait planners are able to generate more balanced gaits and that, too, at the cost of lower power consumption compared to those yielded by the analytical approach. The NN-based and FL-based approaches are found to be more adaptive compared to the other approach in generating the gaits of the biped robot.