增量型AGC模型稳定性研究

采用误差分析方法导出增量型自动厚度控制(AGC)模型设定序列的差分方程.根据该差分方程的特征值表达式讨论了AGC模型设定序列的稳定区域和稳态误差与AGC模型参数之间的关系.     

压力AGC通用表达形式的推导及分析

在对压力AGC模型分析的基础上,将压力AGC算法分为直接型和增量型两大类,建立了压力AGC调节过程中的出口厚度偏差差分方程,求解出压力AGC的通用表达形式,通过定义不同的系数,可推导出BISRAAGC,DAGC和GM-AGC等各种算法,并对每种算法的稳定性和快速性进行了评估,为在工程应用中AGC算法的选择提供了理论依据。     

基于逆系统方法的内模AGC控制方式

 针对压力AGC在提高厚度控制精度方面存在的问题,利用非线性弹跳方程,提出了基于逆系统方法的内模AGC控制方式。求得AGC系统的逆模型,逆模型与原系统串联为伪线性系统,将AGC系统中参数摄动、建模误差、干扰等不确定因素考虑进去,引入内模控制方案,理论证明逆系统内模控制对AGC系统控制的有效性。仿真结果验证了基于逆系统方法的内模AGC控制策略具有好的抗干扰性能,且系统鲁棒性强,控制算法简单易于现场实现。     

基于克隆算法的变结构模糊神经网络在AGC系统中的应用

遗传算法优化函数参数可能会出现不足,为此采用高斯变异与柯西变异相结合的克隆算法,优化了变结构模糊神经网络的参数,并基于此方法设计控制器,应用于AGC控制系统.仿真实验结果表明,应用克隆算法比遗传算法优化参数收敛速度更快,用于AGC控制性能更好.     

辊缝型监控AGC纯滞后补偿控制器的算法设计及应用

针对辊缝型监控AGC控制时的滞后特点,采用新型Smith预估策略对监控AGC系统进行纯滞后补偿.根据监控AGC各个主要控制环节的传递函数,推导纯滞后补偿控制器的理论预估模型.采用最优降阶算法对理论模型降阶,给出了基于最优降阶模型下纯滞后补偿控制器的离散化算法及设计步骤.现场实际应用效果表明:带有最优Smith预估算法的辊缝型监控AGC具有稳定性强、动态特性好及稳态精度高的优点.     

变刚度控制的收敛性与稳态特性分析

针对变刚度控制概念及公式推导中普遍存在的不确切问题,本文采用基于无穷级数的数学分析方法对变刚度控制的收敛性及稳态特性进行了研究,证明了其收敛条件即变刚度系数的取值范围与机架刚度系数及轧件塑性系数的关系.在收敛性证明的基础上建立了变刚度控制的稳态分析方法,推导了厚度偏差等变量的稳态数值计算公式,阐明了等效刚度概念的准确含义、理论表达式及其对AGC系统厚度控制性能的影响,并对变刚度控制刚度特性的分类进行了完整的描述.     

BISRA-AGC变刚度控制的综合分析

在压力AGC通用表达形式的基础上,考虑辊缝位置环,采用解析方式推导出BISRA-AGC变刚度控制的出口厚差表达式,并对控制算法的快速性、稳定性和终值误差进行定量分析。仿真结果与分析结论一致,所得结论可以作为工程上算法选择与参数设定的参考。     

中厚板轧机高精度厚度自动控制系统的开发及应用

为了同时满足同板差和异板差的指标要求,天津天铁冶金集团有限公司轧钢厂中厚板轧机自动厚度控制系统(AGC)分别采用了相对方式AGC和绝对方式AGC,其中,相对方式AGC能有效提高同板差精度,绝对方式AGC则可以保证轧出钢板的厚度与目标厚度基本一致。为此建立了钢板的高精度厚度计式模型,采用X射线测厚仪检测参数和各种补偿算法对计算模型进行修正。这种高精度的AGC系统投入使用后运行稳定,厚度精度取得了满意效果。     

压力AGC系统参数分析与实验验证

由压力AGC参数方程推出压力AGC正反馈“跑飞”(失稳)的临界条件:KB>1+M/Q并得到计算机仿真实验和轧机上实验证明,AGC参数方程还证明,当KB<1时存在一个临界值:KB<1-M/Q时发生振荡。由AGC系统传递函数推出一次调节达到稳态值的计算公式,消除了1-M/Q的临界值。     

压力AGC模型综述

在轧机厚度控制中,压力AGC应用广泛,形式多样,但由于缺乏统一的比较标准和系统的研究,给系统设计者对压力AGC形式的选择带来困难。提出了压力AGC的两个评价准则：厚度估计精度和系统动态特性,并对几种典型的压力AGC进行了分析和比较,简要证明了Gaugemeter AGC和Dynamic Set AGC是完全统一的,提出了一种新的压力AGC模型,分析表明,该系统具有很好的动静态特性。     

Application of Improved PSO Algorithm in Hydraulic Pressing System Identification

 In view of characteristics of particle swarm optimization (PSO) algorithm of fast convergence but easily falling into local optimum value, a novel improved particle swarm optimization algorithm is put forward, and it is applicable to identify parameters of hydraulic pressure system model in strip rolling process. In order to maintain population diversity and enhance global optimization capability, the algorithm is firstly improved by means of decreasing its inertia weight linearly from the maximum to the minimum and then combined with chaotic characteristics of ergodicity, randomness and sensitivity to initial value. When the improved algorithm is used to identify parameters of hydraulic pressure system, the comparison of simulation curves and measured curves indicates that the identification results are reliable and close to actual situation. A new method was provided for hydraulic AGC system model identification.     

基于模糊神经解耦控制的板型板厚控制系统仿真

面对板型板厚控制这一复杂、多变量耦合的非线性系统,本文提出了一种两级串联结构的模糊神经网络解耦控制策略,前级为自调整模糊控制器,后级为基于动态耦合特性的自适应神经网络解耦控制器,并从理论上证明了学习算法的收敛性。实现了无模型板型板厚综合控制。仿真结果表明,该控制系统收敛性好、抗干扰性强,取得令人满意的板型板厚控制精度。     

大型轧机AGC液压缸信号分析方法研究

为分析大型轧机AGC液压缸的信号,建立轧机液压AGC控制系统,以获取AGC液压缸的性能,从而量化AGC缸性能参数,以保证AGC液压缸的工作可靠性。为此分析下列参数对系统的影响伺服阀阀芯开口度、有杆腔无杆腔压力、AGC液压缸位移、和轧制力。采用基于时域数字特征的特征提取方法,并以时域信号的各种定量进行评判。以AGC缸无杆腔压力信号为例进行分析。结果表明,随着泄漏量增大AGC缸无杆腔到达150bar的时间逐渐延长,其结果分别是38.5s、46.7s、52.5s,系统响应变慢。并且AGC缸无杆腔的压力值随内泄漏故障程度增加,离散程度也逐渐增加。本文可供设计和使用人员参考。     

新型液压轧机控制方案的研究

针对板带轧机液压压下控制这一典型的电液位置伺服系统,分析了传统的PID控制方法和新颖的重复控制方法在AGC补偿和偏心补偿中的优缺点,提出了一种基于传统PI控制和嵌入式重复控制的新型复合控制方案,详细分析了系统的结构和重要控制参数的整定.研究结果表明:该控制方案结构简单、易于实现,具有良好的动态和稳态特性,同时实现了板带液压轧机的高效AGC补偿和高精度的偏心补偿.     

Stochastic Optimal CPS Relaxed Control Methodology for Interconnected Power Systems Using Q-Learning Method

This paper presents the application and design of a novel stochastic optimal control methodology based on the Q-learning method for solving the automatic generation control (AGC) under the new control performance standards (CPS) for the North American Electric Reliability Council (NERC). The aims of CPS are to relax the control constraint requirements of AGC plant regulation and enhance the frequency dispatch support effect from interconnected control areas. The NERC’s CPS-based AGC problem is a dynamic stochastic decision problem that can be modeled as a reinforcement learning (RL) problem based on the Markov decision process theory. In this paper, the Q-learning method is adopted as the RL core algorithm with CPS values regarded as the rewards from the interconnected power systems; the CPS control and relaxed control objectives are formulated as immediate reward functions by means of a linear weighted aggregative approach. By regulating a closed-loop CPS control rule to maximize the long-term discounted reward in the procedure of online learning, the optimal CPS control strategy can be gradually obtained. This paper also introduces a practical semisupervisory group prelearning method to improve the stability and convergence ability of Q-learning controllers during the prelearning process. Tests on the China Southern Power Grid demonstrate that the proposed control strategy can effectively enhance the robustness and relaxation property of AGC systems while CPS compliances are ensured.     

冷连轧机前馈及监控AGC研究与应用

介绍了1450 mm冷连轧机前馈及监控AGC的控制策略,提出监控AGC中包含的纯滞后环节会给产品质量带来较大的影响。利用Smith预估算法解决了厚度控制纯滞后的问题,实际生产表明,厚度控制系统的精度和稳定性均有较大提高。     

A double laser filtering approach to vibration noise rejection for hot-rolled strip flatness measurement

This paper proposes a new filtering method based on the Kalman filtering algorithm for hot-rolled strip flatness measurement system. The system involves processing slowly changing signal, which can be considered as a bounded random process, and its model parameters are completely unknown. The noise rejection strategy in double lasers can generate a compensation signal. Since the initial and accumulative error would lead to negative filter effect or even cause divergence, Kalman filter is integrated to effectively deal with the initial error and enhance convergence. In this setting, the noise rejection strategy is used as a prediction model to constitute a similar Kalman filter. The correlated error caused by measurement error is coped with by a compensation model based on the feature of correlated error to enhance the filter effect. Both theoretical analysis and simulations show that the new algorithm has a better filter effect than the traditional Kalman filtering algorithm for the system.