无模型控制器在气缸压力控制系统中的应用研究

气动伺服系统存在纯时延、非线性、时变等特点,传统的控制策略(如PID控制)在解决非线性系统时效果不理想,因此提出一种无模型控制算法。此方法在被控对象结构复杂、参数时变时控制效果较好。首先对气动伺服系统进行建模,建模过程包括阀口流量、比例流量阀及缸内压力建立一个二阶模型;其次设计无模型自适应控制器(Model-Free Adaptive Controller,MFAC)用于气动伺服系统压力控制;最后利用LabWindows/CVI平台进行试验验证。结果表明,针对气动伺服系统设计的无模型控制器是有效的,相比于传统PID控制有更快的响应速度和更高的控制精度。     

包钢连铸结晶器液位控制系统中控制器的设计应用

本文采用FUZZY－PID复合式自适应控制器来满足冶金连铸系统中对结晶器液位控制的要求，由于在长时间的连续铸钢过程中，塞棒头部受钢水的冲刷和腐蚀，使得塞棒头部为形较大，导致塞棒位置与钢水流量的特性关系发生较大变化，而这种变化无法用确定的数学关系式来描述，用常规PID控制器不能对此变化进行有效调节。本文采用模糊控制器对此变化作出反映，通过模糊控制运算结果来调节PID控制器的放大系数Kp始终在理想的范围内，从而使控制系统达到对结晶器液位控制的要求。     

Adaptive fuzzy sliding-mode control for the Ti6Al4V laser alloying process

It is well known that surface alloying quality may vary significantly with respect to process parameter variation. Thus a feedback control system is required to monitor the operating parameters for yielding a good quality control. Since this multi-input and multi-output (MIMO) system has nonlinear coupling and time-varying dynamic characteristics, it is very difficult to establish an accurate process model for designing a model-based controller. Hence an adaptive fuzzy sliding-mode controller (AFSMC) which combines an adaptive rule with fuzzy and sliding-mode control is employed in this study. It has an on-line learning ability for responding to a system’s nonlinear and time-varying behaviours. Two adaptive fuzzy sliding-mode controllers are designed for tuning the laser power and the traverse velocity simultaneously to tackle the absorptivitiy and geometrical variations of the work pieces. The simulation results show that good surface lapping performance is achieved by using this intelligent control strategy.     

动态非线性工艺系统建模技术的发展现状分析

分析基于人工神经网络的动态非线性工艺系统建模的发展现状,指出当前研究中存在的两大关键共性问题,即海量生产实绩样本的选择问题和动态连续时变特征参数的在线提取问题.     

液压弯辊系统的优化神经网络内模控制

针对轧机液压弯辊系统存在非线性、时变性等特点,采用基于前馈神经网络的内模控制方法,将优化网络用于神经网络辨识器和内模控制器的离线训练,采用学习率自适应调整的改进BP算法在线调整网络权值。仿真研究表明,将优化网络用于液压弯辊系统控制,提高了液压弯辊系统的动态响应速度和稳态跟踪精度,具有较强的快速性和鲁棒性,能够取得理想的控制效果。     

Model-free adaptive fractional order control of stable linear time-varying systems

This paper presents a new model-free adaptive fractional order control approach for linear time-varying systems. An online algorithm is proposed to determine some frequency characteristics using a selective filtering and to design a fractional PID controller based on the numerical optimization of the frequency-domain criterion. When the system parameters are time-varying, the controller is updated to keep the same desired performances. The main advantage of the proposed approach is that the controller design depends only on the measured input and output signals of the process. The effectiveness of the proposed method is assessed through a numerical example.     

Thermodynamic Behavior Research Analysis of Twin-roll Casting Lead Alloy Strip Process

The thermodynamic behavior of twin-roll casting(TRC) lead alloy strip process directly affects the forming of the lead strip, the quality of the lead strip and the production efficiency. However, there is little research on the thermodynamics of lead alloy strip at home and abroad. The TRC lead process is studied in four parameters: the pouring temperature of molten lead,the depth of molten pool, the roll casting speed, and the rolling thickness of continuous casting. Firstly, the thermodynamic model for TRC lead process is built.Secondly, the thermodynamic behavior of the TRC process is simulated with the use of Fluent. Through the thermodynamics research and analysis, the process parameters of cast rolling lead strip can be obtained: the pouring temperature of molten lead: 360–400 °C, the depth of molten pool: 250–300 mm, the roll casting speed: 2.5–3 m/min, the rolling thickness: 8–9 mm.Based on the above process parameters, the optimal parameters(the pouring temperature of molten lead:375–390 °C, the depth of molten pool: 285–300 mm, the roll casting speed: 2.75–3 m/min, the rolling thickness:8.5–9 mm) can be gained with the use of the orthogonal experiment. Finally, the engineering test of TRC lead alloy strip is carried out and the test proves the thermodynamic model is scientific, necessary and correct. In this paper, a detailed study on the thermodynamic behavior of lead alloy strip is carried out and the process parameters of lead strip forming are obtained through the research, which provide an effective theoretical guide for TRC lead alloy strip process.     

模糊无模型自适应控制在CSTR中的仿真研究

连续搅拌反应釜(Continuously Stirred Tank Reactor,CSTR)是工业生产中常用的设备,其温度系统具有强非线性、耦合性、时变性的特点,反应机理复杂,精确建模困难,传统控制方法很难满足生产需要。为解决上述问题,采用基于数据驱动的无模型自适应方法(Model Free Adaptive Control,MFAC)对反应釜出料温度进行控制。在SIMULINK环境下搭建反应釜数学模型,采用基本MFAC方法对其进行仿真研究,针对控制器参数调整复杂的问题,采用模糊控制方法对无模型控制器参数进行优化。仿真实验结果表明,基于模糊控制的MFAC方法改善了系统动静态性能指标,系统的抗干扰能力和自适应能力得到增强。该方法对反应釜实际生产具有一定的指导作用。     

铯源钢水液面控制系统的原理与故障分析

该系统可连续测量结晶器的钢水液面,并能在线切换不同钢坯断面参数,输出随液面高度线性变化的电压或电流模拟量,送给液位调节系统,从而实现自动控制拉坯或浇钢速度,并且使钢水液面稳定地保持在预定的高度上。因此可提高连铸机作业率,保持钢锭质量,并且减轻浇铸工人的劳动强度,同时大大减轻了维护人员的工作强度。     

Development of an adaptive radial basis function neural network estimator-based continuous sliding mode control for uncertain nonlinear systems

In this paper an adaptive neural network (NN)-based nonlinear controller is proposed for trajectory tracking of uncertain nonlinear systems. The adopted control algorithm combines a continuous second-order sliding mode control (CSOSMC), the radial basis function neural network (RBFNN) and the adaptive control methodology. First, a second-order sliding mode control scheme (SOSMC), which is published recently in literature for linear uncertain systems, is extended for nonlinear uncertain systems. Second, an adaptive radial basis function neural network estimator-based continuous second order sliding mode control algorithm (CSOSMC-ANNE) is adopted. In CSOSMC-ANNE control methodology, a radial basis function neural network with adaptive parameters is exploited to approximate the unknown system parameters and improve performance against perturbations. Also, the discontinuous switching control of SOSMC is supplanted with a smooth continuous control action to completely eliminate the chattering phenomenon. The convergence and global stability of the closed-loop system are proved using Lyapunov stability method. Numerical computer simulations, with dynamical model of the nonlinear inverted pendulum system, are presented to demonstrate the effectiveness and advantages of the presented control scheme.     

神经网络PID的无人动力伞偏航控制研究

对于具有非线性、时变和强耦合特性的动力伞系统,采用常规控制方法难以获得满意的控制效果,而采用神经网络与传统PID相结合作为控制器,使得所设计控制器能够针对非线性、时变的气动特性自适应地调整控制参数,实现对PID控制器参数的寻优整定,简化了设计过程。利用Matlab软件对系统进行仿真,给出了相应的仿真结果。     

贵钢30t EAF-LF(VD)-CCM工艺实践

首钢贵阳特殊钢有限责任公司第一炼钢厂通过对30t EBT电弧炉、35t精炼炉、一机一流连铸机生产的操作进行改进,成功实现了电弧炉、精炼炉、连铸机之间的顺利生产,可成功连浇10炉钢。同时对钢包、中间包进行改进和加强钢包的使用管理,在一炉钢的浇铸过程中,将中间包里钢水的温降控制≤15℃,连铸坯质量满足用户要求。     

基于比例阀的气动伺服系统神经网络控制方法的研究

研究了用神经网络PID控制器对基于比例阀的气动伺服系统进行控制的方法。用神经网络辨识器来逼近非线性动力学系统,并在线修改控制参数。实验及分析表明,适当的选择网络参数,经过充分的离线训练,该控制器可以进行在线的自适应控制,系统的控制精度和动态特性有明显提高,且在环境参数变化时,控制器具有在线自学习和自整定参数的能力。     

冷带轧机电液伺服系统广义预测控制应用研究

在冷带轧机电液伺服系统中应用广义预测控制理论直接算法,采用随机梯度估计法在线估计控制器参数模型,避免了在线求解Diophantine方程,减少了在线计算时间。该控制算法基于离散受控自回归积分滑动平均模型,吸取了自适应控制的优点,使控制系统具有较好的鲁棒性和智能性。仿真研究结果表明,该方法比目前带钢生产中广为采用的PID控制策略优越,使得系统的动态品质有了较大的改善,响应时间和控制精度都有所提高。     

基于神经网络PID的轧机AGC力控制

以PID控制器为基础,辅助以神经网络的自学习、自适应能力,将神经网络和PID控制融合为一体,设计出一种神经网络PID控制器,应用于具有高度非线性、时变性的板带轧机AGC力控制系统,仿真结果表明,能有效改善系统的动态品质,并具有良好的适应能力.     

用于气动伺服系统的自适应神经模糊控制器

研究了一种基于压力比例阀的气动伺服系统自适应神经模糊控制器。其中的神经网络辨识器（NNI）通过高线训练可以充分逼近非线性动态系统的模型,并能够在线调整模糊控制器的控制规则。系统的位置控制精度和伺服特性有了很大改善。试验结果表明,所提出的控制器对该气动伺服系统具有很好的控制特性以及很强的自适应能力。     

连铸连轧生产电工钢板的工艺技术优势

论述我国钢板生产技术的现状,分析电工钢现有生产工艺技术关键及其所存在的问题.阐述连铸连轧工艺技术的发展情况,研究和分析采用中薄板坯连铸连轧生产电工钢的技术优势及可行性,提出连铸连轧生产电工钢的课题开发研究方向.     

In-situ neural network process controller for copper chemical mechanical polishing

Process control is one of the key methods to improve manufacturing quality. This research proposes a neural network based run-to-run process control scheme that is adaptive to the time-varying environment. Two multilayer feedforward neural networks are implemented to conduct the process control and system identification duties. The controller neural network equips the control system with more capability in handling complicated nonlinear processes. With the system information provided by this neural network, batch polishing time (T) an additional control variable, can be implemented along with the commonly used down force (p) and relative speed between the plashing pad and the plashed wafer (v). Computer simulations and experiments on copper chemical mechanical polishing processes illustrate that in drafting suppression and environmental changing adaptation that the proposed neural network based run-to-run controller (NNRTRC) performs better than the double exponentially weighted moving average (d-EWMA) approach. It is also suggested that the proposed approach can be further implemented as both an end-point detector and a pad-conditioning sensor.     

用模式识别轧机尾部跑偏的自适应控制

针对铝带热连轧轧制过程中影响跑偏的非线性因素众多,很难建立相应的跑偏控制模型的特点,基于现场测试数据进行分析,通过分析跑偏信号的数据特征,建立轧制过程跑偏量值神经网络辨识模型,找出了尾部跑偏模式与F_4机架敏感轧制参数的非线性关系,给出可应用于实际轧制生产的自适应纠偏控制模型,并以一组实测数据进行分析,纠偏效果良好.     

基于径向基神经网络的直线超声电机位置控制

针对直线超声电机的精密位置控制,提出了一种基于径向基神经网络的自适应控制机制。鉴于直线超声电机工作原理,其运行状态必然受到摩擦、强非线性和时变等不确定性因素的干扰,为了对这些不确定性因素进行有效的逼近,采用了径向基神经网络。为了提高控制机制的自适应能力,首先利用来自试验数据的训练样本按正交最小二乘算法确定径向基神经网络的隐层单元的个数和相关参数,再按递推最小二乘法在线调整隐层与输出层之间的权重。试验结果表明,基于径向基神经网络的自适应控制器的性能不仅优于传统的PID控制和误差反向传播神经网络控制,而且具有很好的抗干扰能力。