间歇过程点对点迭代学习控制的初始状态误差分析

针对间歇过程点对点跟踪控制问题,在轨迹更新的迭代学习控制算法框架下,针对非理想初始状态情况下3种不同的初始误差,通过2D Roesser模型对其进行描述并分析其收敛性。给出了不同的情况下系统相对参考轨迹的零误差跟踪或者收敛到特定邻域的条件,在零误差跟踪不能实现的情况下给出了邻域的范围。通过数值模型仿真验证了给出的收敛条件和收敛边界,并分析了不同因素对收敛边界的影响。     

基于T-S模糊神经网络的AGV轨迹跟踪研究

针对AGV在轨迹跟踪过程中出现的问题,通过对小车运动学模型进行分析,采用T-S模糊神经网络模型,设计了一种模糊神经网络控制器,利用Matlab软件进行仿真,并与普通PID控制器进行对比。仿真结果表明:基于T-S的模糊神经网络控制器提高了AGV小车运行的稳定性和鲁棒性,优于普通控制算法。     

浅层水平井丛式井组轨迹控制技术

通过对浅层水平井丛式井组施工和轨迹控制的难点的分析,提出了相应的技术措施,特别是针对浅层水平井丛式井组防碰形式严峻,浅层造斜困难,施工速度快,油层地质条件复杂等给出了相应的施工措施。并通过实例分析,说明了浅层水平井丛式井组轨迹控制的关键技术,最后总结给出了浅层水平井丛式井组施工的结论与建议。     

胜利西部新区排601区块浅层水平井轨迹控制技术

通过对排601区块地质特点,水平井设计特点的分析,总结得出了排601区块浅层水平井施工和轨迹控制的难点,针对该区块浅层水平井施工和轨迹控制的难点提出了相应的技术措施,特别是针对排601区块浅层水平井防碰形式严峻,浅层造斜困难,施工速度快,油层地质条件复杂等给出了相应的施工措施。并通过实例分析,说明了该区块浅层水平井轨迹控制的关键技术,最后总结给出了排601区块浅层水平井施工的结论与建议。     

密封环支撑边界条件对机械密封端面变形的影响

机械密封的密封环是通过辅助O形圈支撑在轴上或者密封腔内,不同的结构设计会改变密封环支撑边界。针对三种机械密封结构模型,利用ANSYS有限元分析软件,模拟机械密封摩擦副端面的变形,讨论了橡胶O形密封圈不同受力边界条件下机械密封端面变形的规律。研究发现当动环、静环均采用SiC时,在静态（结构分析）时,该三种不同支撑结构的摩擦副端面均形成发散间隙,端面变形受支撑边界接触应力的影响较大;热结构耦合分析发现其动环、静环端面间隙呈收敛间隙运转时,热边界条件影响更大。当动环采用石墨,静环采用SiC时,发现其端面间隙可能为收敛型也可能为发散型,这与支撑边界有关。故密封环支撑边界条件的不同会影响动环端面变形,同时动、静环材料的弹性模量对端面的变形有较大影响,从而会影响密封性能。该研究对机械密封设计有指导意义。     

连续油管在作业过程中的受力分析

本文对连续油管作业过程中的受力进行了分析,连续油管除受拉力、接触力、摩擦力外还受到各种附加作用力,根据分析建立了根据井眼轨迹计算的管柱摩阻模型,分析了连续油管下入,上提时的边界条件。分析了如何利用此模型计算连续油管的最大下入深度。同时针对模型对连续油管下放和起出进行了实例验证。     

基于无约束迭代学习的间歇生产过程优化控制

针对基于迭代学习控制的间歇过程优化控制算法难以进行收敛性分析的难题,本文基于数据驱动的神经模糊模型提出一种新颖的间歇过程无约束迭代学习控制方法,通过调节因子的变化去除了约束条件,使控制轨迹在批次轴上收敛,并创新性地对优化问题的收敛性给出了严格的数学证明。在理论研究的基础上,将本文提出的算法用于间歇连续反应釜的终点质量控制研究,仿真结果验证了本文算法的有效性和实用价值,为间歇过程的优化控制提供了一条新途径。     

基于神经网络优化的永磁同步直线电机迭代学习位置控制方案

针对开环迭代学习控制的局限性和跟踪轨迹收敛速度慢的问题,提出利用径向基函数网络优化控制律的开闭环迭代学习控制算法。在每次迭代过程中,用神经网络拟合合适的学习增益,并分别采用开环迭代学习、开闭环迭代学习和优化的开闭环迭代学习算法在永磁同步直线电机中进行了位置控制仿真,结果表明:优化后的算法比传统方法具有更快的收敛速度和更小的位置跟踪误差。     

间歇过程的批间自优化控制

针对间歇过程的实时优化问题,提出了一种基于最优性条件近似法的批间自优化控制策略。首先获取标称工作点的最优输入轨迹形态,将其参数化为少量决策变量,简化问题复杂度。然后根据参数化后的决策变量得到批间优化的最优性条件,并建立批次终端可测变量和最优性条件之间的回归模型,将其作为被控变量进行批间跟踪控制。对一个间歇反应器进行了仿真研究,结果表明方法能有效实现间歇过程的批间自优化控制。     

提高低速非达西渗流模型求解精度的一种方法

研究了低速非达西试井解释模型,并利用Laplace变换求得了该模型在Laplace空间下的解析解,对低速非达西模型的求解过程和结果做了进一步的完善。通过对第二类虚宗量贝塞尔函数的积分算法的研究,给出了其在大自变量和小自变量俩种不同情况下的积分算法,分析了两种不同算法在不同情况下的适应程度,结合大量的计算和比较对两种算法的实用范围进行了结合统一,给出了能够精确计算任意自变量的方法,完善了在低速非达西模型中长期存在的一个因计算精度不够而导致典型曲线振荡的问题。最后利用数值反演算法绘制了典型曲线,并和以前算法所得到的典型曲线进行了对比,验证了本文的正确性。     

分散模型跟踪滑模控制器在陶瓷取坯机械手中的应用

首先从理论上研究了一类非线性大系统的分散自适应滑模控制方案 ,通过理论分析证明了该分散自适应滑模控制系统是全局稳定 ,跟踪误差可收敛到零的一个邻域内。针对现有的陶瓷取坯机械手在运行过程中出现的位置误差大、不稳定等问题 ,采用滑模控制对陶瓷取坯机械手进行了仿真应用 ,收到了良好的效果。     

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.     

基于PSO-控制变量参数化混合策略的间歇化工过程优化控制

控制变量参数化方法作为一种化工过程动态优化的梯度搜索算法，其求解效率过于依赖初始给定轨迹。目前初始轨迹一般都是设定在边界值或中间值，缺乏科学依据，从而大大影响了算法的收敛速度。针对这一问题，提出了一种粒子群优化(PSO)与控制变量参数化方法混合的策略，首先利用粒子群优化对间歇化工过程最优控制量进行求解，结果作为控制变量参数化方法初始给定轨迹，进行二次优化。双层优化的混合策略提高了控制变量参数化方法的收敛速度和粒子群优化算法的求解精度。将混合策略应用于两个间歇化工过程优化控制实例，仿真结果表明了该算法对求解化工过程动态优化问题具有可行性和有效性。     

基于分布函数矩的聚合物分子量分布预测控制

聚合物分子量分布（molecular weight distribution,MWD）是聚合产物重要的质量指标,由于无法在线测量,使得直接质量控制至今难以实现。在利用Legendre正交多项式组合神经网络建立聚合反应分子量分布灰箱模型的基础上,把MWD这个三维空间控制问题解构为以其矩向量为特征的二维时间域的控制问题,提出了通过控制分布的矩值实现分子量分布的预测控制方法。目标函数以矩值误差平方和为基础,考虑控制变量的约束条件,同时引入可测低阶矩的修正项,使得分子量分布的部分闭环反馈控制得以实现。该方法以实验室规模的苯乙烯聚合过程为对象进行了仿真建模与控制研究,获得良好的控制效果,证明了方法的有效性。     

A MODEL-BASED DIRECT ADAPTIVE PI CONTROLLER FOR NONLINEAR PROCESS CONTROL

This article proposes a model-based direct adaptive proportional-integral (PI) controller for a class of nonlinear processes whose nominal model is input-output linearizable but may not be accurate enough to represent the actual process. The proposed direct adaptive PI controller is composed of two parts: the first is a linearizing feedback control law that is synthesized directly based on the process's nominal model and the second is an adaptive PI controller used to compensate for the model errors. An effective parameter-tuning algorithm is devised such that the proposed direct adaptive PI controller is able to achieve stable and robust control performance under uncertainties. To show the robust stability and performance of the direct adaptive PI control system, a rigorous analysis involving the use of a Lyapunov-based approach is presented. The effectiveness and applicability of the proposed PI control strategy are demonstrated by considering the time-dependent temperature trajectory tracking control of a batch reactor in the presence of plant/model mismatch, unanticipated periodic disturbances, and measurement noises. Furthermore, for use in an environment that lacks full-state measurements, the integration of a sliding observer with the proposed control scheme is suggested and investigated. Extensive simulation results reveal that the proposed model-based direct adaptive PI control strategy enables a highly nonlinear process to achieve robust control performance despite the existence of plant/model mismatch and diversified process uncertainties.     

Estimator-based control of reactive distillation system: Application of an extended Kalman filtering

This paper demonstrates that a state estimator can be successfully designed and implemented in a feedback control system of reactive distillation. The work of the state estimator is to provide the state compositions that are required to be used in the controller for necessary action. The control performance of a system that relies on the state estimator is examined and compared to a system that takes direct measurement from the process assuming the availability of a perfect online analyzer. It is found that the estimator-based system is robust against a moderate measurement errors and erroneous initial conditions. If the state estimator is designed from a highly erroneous process model, noisy measurements and approximate initial conditions, the use of estimator together with an online analyzer (for easily measured states) is recommended to achieve an effective control of a reactive distillation system.     

Gaussian Semi-parametric Estimation of Fractional Cointegration

Abstract. We analyse consistent estimation of the memory parameters of a nonstationary fractionally cointegrated vector time series. Assuming that the cointegrating relationship has substantially less memory than the observed series, we show that a multi-variate Gaussian semi-parametric estimate, based on initial consistent estimates and possibly tapered observations, is asymptotically normal. The estimates of the memory parameters can rely either on original (for stationary errors) or on differenced residuals (for nonstationary errors) assuming only a convergence rate for a preliminary slope estimate. If this rate is fast enough, semi-parametric memory estimates are not affected by the use of residuals and retain the same asymptotic distribution as if the true cointegrating relationship were known. Only local conditions on the spectral densities around zero frequency for linear processes are assumed. We concentrate on a bivariate system but discuss multi-variate generalizations and show the performance of the estimates with simulated and real data.     

Design and Analysis of Integrated Predictive Iterative Learning Control for Batch Process Based on Two-dimensional System Theory

Based on the two-dimensional (2D) systemtheory, an integrated predictive iterative learning control (2D-IPILC) strategy for batch processes is presented. First, the output response and the error transition model predictions along the batch index can be calculated analytically due to the 2D Roesser model of the batch process. Then, an integrated framework of combining iterative learning control (ILC) andmodel predictive control(MPC) is formed reasonably. The output of feedforward ILC is estimated on the basis of the predefined process 2D model. By minimizing a quadratic objective function, the feedback MPC is introduced to obtain better control performance for tracking problem of batch processes. Simulations on a typical batch reactor demonstrate that the satisfactory tracking performance as well as faster convergence speed can be achieved than traditional proportion type (Ptype) ILC despite the model error and disturbances.     

ROBUST CONTROL OF BATCH REACTORS

The design of robust nonlinear feedback controller is analysed for a trajectory tracking in a single-input single-output nonlinear state variable system x = f(x) + g(x)u, y=cx which arises in nonlinear chemical processes particularly in batch reactor control problems. Simulation results for the batch reactor temperature tracking problem show the effectiveness of the control scheme and its robustness to modelling errors. The method is also applicable to multi-input multi-output system where the number of inputs is equal to that of outputs. The controller design is also analyzed for situations wrier: the kinetics, the activation energy and Ihe heat of reaction are unknown and also only limited measurement of state-variables are available. The method of Youcef-Toumi and Ito (1987) is applied to such problems and the effectiveness of control system is shown by simulation.     

P-隐式广义预测控制在球磨机制粉系统中的应用

球磨机制粉系统多变量、强耦合的特点使得传统的控制策略难以取得良好的控制效果。通过选择合适的权矩阵,预测控制可以实现间接解耦控制,但系统的跟踪性能将大大降低。提出了带比例调节器的隐式广义预测控制(P-隐式GPC)算法,将其他通道对当前通道的影响作为扰动,从而实现近似解耦。在目标函数中用控制量的增量代替控制量,使控制增量变化不至过于剧烈,新增加的比例调节器使设定值与实际值的偏差实时改变,增强了系统的跟踪性能。将该算法应用到球磨机制粉系统取得了良好的控制效果。