基于模糊模型参考学习控制的焦炉温度控制

焦炉是一个大惯性、纯滞后、非线性、时变和参数分布的系统,很难对其建立精确的数学模型.现有的线性系统控制方法对焦炉的温度控制存在精度不高、不能适应工况变化等问题.而模糊控制又因规则和隶属度函数的粗糙性导致系统在稳态时出现振颤,模型参考自适应控制需要对被控对象进行阶次辨识,而且算法复杂.针对上述问题,提出一种新的控制策略,使用模糊模型参考学习控制算法来对焦炉的温度进行控制,该方法结合模糊控制和自适应控制的优点,通过在线学习算法适应工况变化,提高了控制精度.对焦炉简化模型进行的仿真实验结果表明,系统具有良好的动态和稳态性能,有效减少了炉温波动,具有一定的推广应用价值.     

基于VRFT的IMC-PID方法及其在水厂浊度控制中的仿真研究

针对城市供水出水浊度过程的大惯性、大时滞、非线性以及时变等特点,将虚拟参考反馈整定数据驱动方法引入到IMC-PID控制器的整定中来,提出了一种不基于模型参数的VRFT-IMC参数整定方法.该方法集合了内模控制鲁棒性强和数据驱动控制的自适应性两方面的优点,仿真表明其控制性能优于普通IMC-PID控制器,可望用于出水浊度等...     

脂肪酸甲酯磺酸盐生产过程中和工序的pH值无模型自适应控制

脂肪酸甲酯磺酸盐制造技术的开发提出了对中和工序的非常苛刻的pH值控制要求。由于pH值的变化和反应具有严重非线性、大滞后、时变等特性,使传统PID控制几乎都不能满足要求。为了解决这个问题,设计并组装了一套基于无模型自适应技术(MFA)的控制系统,并在MES生产过程的中和工序中实施应用和运行。结果对比表明,无模型自适应控制较常规PID控制方式更适合于强非线性、大滞后、时变的pH值中和过程,精度高,控制稳定,并且无需过程参数辨识,操作简便。     

风力发电机组恒功率无模型自适应变桨距控制

风力发电系统是一个非线性、强耦合的系统,其风速的扰动性极强。无模型控制基于系统特性值动态估计,优化内部参数可使系统克服时变和非线性,具有比PID更好的抗干扰能力,且更适用于非线性、强耦合系统。最后,对比了在阶跃和随机风速下无模型与PID控制器的控制效果,并进行仿真研究,结果表明无模型控制能更好地保持发电机功率恒定。     

模糊预测函数控制在水泥回转窑分解炉温控系统中的应用研究

通过对水泥回转窑系统中预分解炉温度控制回路的特性分析,提出一种基于模糊模型的增量型预测函数控制算法,根据生料流量的波动来修正相应的预测函数控制律,并以Hoenywell公司的Plantscape集散控制系统为平台,开发了实时控制软件,实际应用表明对存在大迟后和参数时变的水泥回转窑系统,该算法能达到良好的控制效果。     

谷氨酸发酵过程的模型化和在线估计研究

提出了包括以菌体浓度Ｘ，基质浓度Ｓ，产物浓度Ｐ和排气二氧化碳为状态变量的谷氨酸发酵过程的动态时变模型，基于这个模型本文还提出了一种在线预报估计一些不能在线测量的生化状态变量的方法。     

蒸汽裂解模拟装置的二自由度内模控制

针对中石化某裂解装置的控制问题,通过实测模型,根据内模控制理论,提出一种二自由度的内模控制器的设计和参数整定方法.通过参数整定,所设计的控制器可以控制系统,同时具有良好的设定值跟踪特性和抑制干扰特性.将该二自由度控制器设计方法应用于蒸汽裂解装置中,减小了装置升温过程的波动,克服了保温过程的扰动,并且缩短了升温时间,提高...     

阳极焙烧炉炉温智能控制算法研究

针对阳极焙烧炉温度控制系统模型难以建立及系统具有严重时变、非线性、不确定强干扰、分布参数等特点,采用带时变遗忘因子的递推最小二乘法建立加热区焙烧炉工作点附近的CARMA模型。设计具有前馈补偿和负压协调控制功能的零极点配置PID预测智能控制器。算法考虑了控制对象的滞后、扰动、时变等因素和特殊工况因素,提高了控制系统的性能。仿真结果验证了算法的有效性。     

异步电机单神经元自适应智能控制系统

针对传统抽油电机控制器自适应性能较弱的特点,提出一种单神经元模型参考自适应控制方法,此法在模型参考自适应控制基础上,采用单神经元代替复杂神经网络,选择线性函数作为参考模型,并将速度变化考虑进误差函数,采取磁链开环、转速闭环的控制结构建立控制系统,建立基于此控制方法的异步电机控制系统仿真模型,并基于DSP构建实验控制系统,仿真及实验结果表明所述控制器输出平稳,具有参数及负载时变的自适应能力,应用前景广泛。     

一种基于VRFT的全格式无模型自适应控制

针对无模型自适应控制中的时变参数拟梯度向量估计值信息没得到充分利用的问题,提出一种基于VRFT（虚拟参考反馈调整方法）的无模型自适应控制方法.该方法利用VRFT中的参考模型思想,首先将非线性系统经全格式线性化后的线性模型与无模型自适应控制器构成的闭环传递函数作为参考模型,再以参考模型的输出与系统期望输出的误差作为控制器的输入,从而将拟梯度向量在过去时刻的估计值引入到无模型自适应控制设计中,提高了伪偏导数估计值信息利用率.同时消去控制律中的权重因子,避免因人为赋值而影响控制效果.     

Estimation of time-varying time delay and parameters of a class of jump Markov nonlinear stochastic systems

It is a challenging problem to estimate time-varying time delay and parameters, especially for systems subject to disturbances with unknown statistics in measurements. The desirable filter should be sensitive to unmodeled dynamics caused by random changes in time delay and parameters, and also be robust to disturbances. Recently, we proposed a finite-horizon robust Kalman filter (RKF) through designing and simultaneously minimizing the upper bounds of unknown covariances of prediction errors, filtering residuals and estimation errors. Unfortunately, unmodeled dynamics and disturbances must be hypothesized to be zero-mean white noises in the RKF. To cope with more general unmodeled dynamics and/or disturbances, a class of jump Markov stochastic systems (JMSS) subject to unmodeled dynamics and disturbances is considered in this article so that a priori system information, such as the value range of unknown and/or randomly changing parameters, can be introduced. Through combining the RKF with the interacting multiple model (IMM) estimation technique, a RKF/IMM algorithm is proposed for such JMSS. Then it is applied to estimate time-varying time delay and parameters of a continuous stirred tank reactor (CSTR) with sensors subject to Gaussian disturbances with unknown means and/or covariances. The RKF/IMM algorithm is compared with the extended Kalman filter (EKF), the strong tracking filter (STF) and the RKF through computer simulations. The results show that, in the case that measurement disturbances are zero-mean noise with unknown covariances, the RKF/IMM and RKF achieve almost the same accurate estimates, which are superior to those of the STF and EKF; in the case that such disturbances have unknown covariances and time-varying means, only the RKF/IMM remains the ability to estimate time-varying time delay and parameters. Furthermore the RKF/IMM has unique ability to identify the disturbance mean no matter whether it is constant or time-varying. Moreover, the RKF/IMM algorithm is shown having strong robustness against the a priori filter parameters, this may be attractive in practical applications.     

网络环境下实时控制系统的设计与调度

总结传统的数字控制系统以及集散控制系统中普遍采用的计算延迟补偿方法,提出两类改善网络化控制系统性能的设计方法,即优化网络调度与改善控制器性能的方法。进一步指出,网络调度与控制器协同设计的方法可以在保证系统总体性能优化的同时,降低对网络服务和系统控制各自过于苛刻的要求,降低系统的总成本。     

Stability of model predictive control with time-varying weights

In this paper, we show that the stability of constrained Model Predictive Control (MPC) systems can be guaranteed by using time-varying weights. It unifies two popular MPC algorithms with guaranteed stability - Infinite Horizon MPC and MPC with End Constraint. Use of time-varying weights may also be useful in analyzing stability properties of MPC for linear time-varying systems as well as uncertain linear systems.     

基于线性时变扰动模型的间歇过程最优迭代学习控制

A batch-to-batch optimal iterative learning control (ILC) strategy for the tracking control of product quality in batch processes is presented. The linear time-varying perturbation (LTVP) model is built for product quality around the nominal trajectories. To address problems of model-plant mismatches, model prediction errors in the previous batch run are added to the model predictions for the current batch run. Then tracking error transition models can be built, and the ILC law with direct error feedback is explicitly obtained. A rigorous theorem is proposed, to prove the convergence of tracking error under ILC. The proposed methodology is illustrated on a typical batch reactor and the results show that the performance of trajectory tracking is gradually improved by the ILC.     

一类非线性时滞过程的主动容错控制

Based on a nonlinear state predictor (NSP) and a strong tracking filter (STF), a sensor fault tolerant generic model control (FTGMC) approach for a class of nonlinear time-delay processes is proposed. First, the NSP is introduced, and it is used to extend the conventional generic model control (GMC) to nonlinear processes with large input time-delay. Then the STF is adopted to estimate process states and sensor biaz, the estimated sensor bias is used to drive a fault detection logic. When a sensor fault is detected, the estimated process states by the STF will be used to construct the process output to form a “soft sensor“, which is then used by the NSP (instead of the real outputs) to provide state predictors. These procedures constitute an active fault tolerant control scheme. Finally, simulation results of a three-tank-system demonstrate the effectiveness of the proposed approach.     

Application of model predictive control to the BSM1 benchmark of wastewater treatment process

Wastewater treatment processes are difficult to be controlled because of their complex and nonlinear behavior. This paper applied model predictive control (MPC) to the Benchmark Simulation Model 1 (BSM1) wastewater treatment process to maintain the effluent quality within regulations-specified limits. Good performance was achieved under steady influent characteristics, especially concerning the nitrogen-related species. In presence of influent disturbances, two approaches have been studied: the addition of a feedforward action based on the measurement of the influent flow rate; the use of nonlinear model predictive controller by addition of a penalty function. The effects of two approaches were visible on the decrease of ammonium and nitrogen concentration which were considered as being of major importance. The results show that MPC can be effectively used for control in wastewater treatment process. By comparing performances, the nonlinear model predictive control strategy with penalty function demonstrates best with small effluent quality index and acceptable aeration and pumping energy consumption.     

基于PWA融合模型的注塑过程保压段建模及控制策略

注塑过程是典型的批次过程,由注射、保压、冷却等阶段构成。保压段是决定制品质量的一个重要阶段,且具有较大的非线性和时变性。分段仿射(piece-wise affine, PWA)模型能够有效描述保压段的特性,但是PWA模型硬切换方式会导致输出跳变问题,因此提出一种基于时间划分的PWA融合模型,在子模型切换过程中引入切换区间,通过线性加权方法进行模型融合,并采用基于分离最小二乘的辨识方法进行参数估计。在此基础上,设计了基于内模整定的多模型PID控制方法。注塑机实验结果表明了算法的有效性。     

Effects of silica morphology on the shear-thickening behavior of shear thickening fluids and stabbing resistance of fabric composites

Shear-thickening fluid (STF) refers to a system where nonaggregating solid particles are suspended in a specific fluid. Due to its unique viscosity variation, STFs have attracted a lot of attention as soft body armor materials for impact protection. In this work, two kinds of STFs were designed using polyethylene glycol (PEG) and spherical or irregularly shaped silica particles. Rheological results showed that shear thickening can even occur at lower shear rates in the irregularly shaped silica-based STF; however, the thickening range of the spherical silica-based STF was broader. Meanwhile, the composites prepared with STFs and aramid fabrics exhibited better antistab properties than neat fabrics. Compared to composite prepared with irregularly shaped silica-based STF, spherical silica-based STF/aramid composite had better stabbing resistance. In addition to the binding effect of silica particles to the fiber bundle, it was considered that when the fabric composite was subjected to impact, spherical particles were easier to slide so that the STF was more likely to be thickened to achieve a greater viscosity, resulting in the better stabbing resistance performance. Our studies will provide guidance for the design of the high-performance soft body armor equipment. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020 , 137, 48809.     

连续反应釜温度控制系统的设计与仿真

针对连续反应釜中进行的化学反应特性,按照过程的动力学方程和能量平衡等关系,设计了一套基于西门子PCS7的连续化学反应釜(CSTR)温度控制系统。由于连续反应釜内温度的非线性、时变等特性,采用变结构模糊控制结合前馈、串级、分程等先进的控制方式,克服了传统PID算法参数调整复杂、超调量大的缺点。仿真结果表明:对温度采用变结构模糊控制能获得较好的稳态精度和动态特性,满足了反应釜内温度控制的要求。