一种前馈广义预测控制算法的研究及其仿真

研究了原油常压蒸馏塔的前馈广义预测控制算法（ＧＰＣ）,克服了可测扰动对系统的作用,建立了以进料温度为可测扰动的常压塔常一线温度控制的ＣＡＲＩＭＡ模型,实现了前馈ＧＰＣ算法的仿真,验证了该算法对具有可测扰动的过程系统具有良好的控制作用,使系统具有良好的鲁棒性。     

双线性自适应前馈控制及其应用

本文对存在可测干扰和界扰动的双线性系统提出了一种逢适应前馈控制算法，证明了算法的稳定性。     

广义预测极点配置前馈控制的一种算法

本文提出了广义预测极点配置前馈自校正控制算法，计算机仿真结果表明，该算法控制质量好，能够消除系统可测扰动对输出的影响。     

多输入预报自校正前馈控制器及其在高频焊管焊接过程控制中的应用

本文针对高频焊管焊接过程的控制,提出了一种适用于具有多种扰动对象的多输入预报自校正前馈控制器,其特点是综合了广义最小方差控制的最优性、极点配置控制的鲁棒性和多输入前馈控制的抗扰性等优点。该控制器采用间接算法实现,在线选择二次型性能指标中加权项。它不仅可以消除可测干扰的影响,消除稳态跟踪误差,而且即使用于非最小相位系统也具有全局收敛特性。理论分析和仿真实验表明,提出的控制器具有良好的控制性能,可以适应焊接过程环境的变化,从而获得满意的控温效果。     

高频焊管焊接过程的多模自达应前馈控制

本文针对高频焊管焊接过程的控制，基于离散MRAC简便算法和仿人智能控制算法[1,.2]，提高了一种适用于具有多个确定性扰动对象的多模自适应前馈控制算法。该算法综合了离散MRAC，多输入前馈控制和开关－比例－保持模型控制各自的优点，可以消除可测干扰的影响，易于工程实现，针对焊接过程数学模型的计算机仿真结果表明，该算法具有较好的跟踪随性能和强的抗扰能力，适用于控制高频焊管的焊接过程。     

高频焊管焊接过程的多模自适应前馈控制

本文针对高频焊管焊接过强的控制,基于离散MRAC简便算法和仿人智能控制算法,提出了一种适用于具有多个确定性扰动对象的多模自适应前馈控制算法。该算法综合了离散MRAC、多输入前馈控制和开关——比例——保持模式控制各自的优点,可以消除可测干扰的影响,易于工程实现。针对焊接过程数学模型的计算机仿真结果表明,该算法具有较好的跟随性能和强的抗扰能力,适用于控制高频焊管的焊接过强。     

结合前馈调参与迭代学习的数据驱动控制方法

在前馈控制中, 需要尽可能的去除前馈控制器对系统模型的需求, 同时保证高精度和鲁棒性. 本文提出了 一种数据驱动的将迭代前馈调参与迭代学习控制进行结合的方法, 通过引入基函数参数化的前馈控制器和输入整 形滤波器, 使用梯度下降法求解最优系统前馈控制器, 消除期望轨迹引入的扰动; 通过迭代学习控制, 消除系统重复 性扰动, 进一步提高控制精度. 算法具有不依赖系统模型, 高精度, 适用于变轨迹任务的优点. 文中给出了相应的仿 真, 并应用到一个直线电机系统, 通过实验验证了算法的有效性.     

一种新的多模自适应前馈控制器

本文针对高频焊管焊接过程的控制,提出了一种适用于具有多个确定性扰动对象的多模自适应前馈控制器,它的特点是综合了从参考模型取状态的MRAC、多输入前馈控制和开关-比例-保持模式控制各自的优点。该控制器不仅可以消除可测干扰的影响,具有较好的控制性能,而且也适用于非最小相位系统。文中针对焊接过程的数学模型,给出了仿真实例研究。     

具有前馈自校正控制及其应用

在具有Ｋ步增量预估器的自校正控制中引入前馈控制，使其不仅对负荷扰动有较强的克服务能力，而且能消除可测干扰的影响。     

带神经网络观测器的永磁同步电机极点配置自校正前馈控制

由递归神经网络构成综合负载转矩观测器,从而把综合负载转矩视为可测干扰,并利用极点配置自校正前馈控制策略实现了永磁同步电机的速度控制,解决了永磁同步电机系统中参数变化和负载扰动等不确定性问题.仿真结果表明该方法具有较强的鲁棒性.     

前馈控制及其工业应用

本文应用实验及模拟计算机的模拟,研究了试验型换热器的前馈一反馈控制,介绍了系统设计、前馈动态补偿器的参数整定和控制品质的改善.此外,还研究了连续消毒塔、动力锅炉和原油分馏塔的动态前馈-反馈控制,同时提出了应用动态前馈补偿克服由虚假被控信号所引起的反馈误动作和非标准型前馈控制.     

Improving feedforward disturbance compensation capabilities in Generalized Predictive Control

This paper deals with the measurable disturbance rejection problem in Generalized Predictive Control (GPC). First, it is analyzed how the unconstrained GPC algorithm with implicit disturbance compensation can be interpreted as a typical feedback plus feedforward control scheme, where the main feature is that the feedforward action includes future estimations of the measurable disturbances. Then, it is shown that classical GPC cannot always eliminate the effect of measurable disturbances even using perfect disturbance models and having exact disturbance estimations along the prediction horizon. To overcome this problem a particular GPC tuning condition is proposed, which allows the improved GPC controller to eliminate the disturbance effect even in those cases where causality and instability problems can appear in the relation between the dynamics of the load disturbance and the process output with the dynamics of the control signal and the process output. Since the new tuning condition for disturbance compensation in GPC leads to a high bandwidth in the feedback loop, a two degrees of freedom control scheme within the Filtered Smith Predictor (FSP)-based GPC framework has been implemented to improve the robustness capabilities of the control law. Simulation examples are presented to show the main advantages of the proposed control scheme, including a realistic simulation based on a greenhouse climate control problem where estimators for the main process disturbances are also designed.     

Adaptive positioning control of an ultrasonic linear motor system

A 3PRR parallel precision positioning system, driven by three ultrasonic linear motors, was designed for use as the object stage of a scanning electron microscope (SEM). To improve the tracking accuracy of the parallel platform, the positioning control algorithms for the drive joints needed to be studied. The dead-zone phenomenon caused by static friction reduces the trajectory tracking accuracy significantly. Linear control algorithms such as PID (Proportion Integration Differentiation) are unable to compensate effectively for the dead-zone nonlinearity. To address this problem, two types of feedforward compensation control algorithms have been investigated. One is constant feedforward with the integral separation PID; the other is adaptive feedback and feedforward based on the model reference adaptive control (MRAC). Simulations and experiments were conducted using these two control algorithms. The results demonstrated that the constant feedforward with integral separation PID algorithm can compensate for the time-invariant system after identifying the dead-zone depth, while the adaptive feedback and feedforward algorithm is more suitable for the time-varying system. The experimental results show good agreement with the simulation results for these two control algorithms. For the dead-zone nonlinearity caused by the static friction, the adaptive feedback and feedforward algorithm can effectively improve the trajectory tracking accuracy.     

Comparison of single loop and QDMC-based control of crude oil distillation towers

Significant economic benefits can result from improved control of crude towers because of their large throughput. In this paper, both conventional PI control, with and without decoupling, and model based QDMC control are applied to crude tower product quality variables. The conventional methods treated involve BLT tuning, detuned Ziegler-Nichols control, and detuned Ziegler-Nichols control plus decoupling. The various control methods are tested on a detailed non-linear simulation of a crude tower. It is shown that significant dynamic two-way interaction exists, particularly in the upper crude tower product quality loops, even though interaction is essentially one-way throughout the tower near steady state. Because of the dynamic interaction, PI controllers must be substantially detuned over what would be required for a one-way interacting system. Decoupling is shown to help improve the performance of the PI quality loops. QDMC is shown to give good transient performance, and it is considered easier to apply than traditional PI methods.     

原油减压蒸馏塔侧线温度多变量预测控制

本文研究了原油减压蒸馏塔侧线抽出温度多变 量控制问题．针对减压塔各个侧线之间存在单向关联的特点，通过建立各个侧线以及相邻侧 线之间的温度动态特性，设计了减压塔侧线温度多变量预测控制系统．在某炼油厂减压塔中 应用表明，采用本文提出的侧线温度多变量预测控制，具有较显著的控制品质．     

NEURAL PREDICTIVE FORCE CONTROL FOR A HYDRAULIC ACTUATOR: SIMULATION AND EXPERIMENT

This paper proposes a neural-based predictive control algorithm for online control of a force-acting industrial hydraulic actuator. In the algorithm, a multilayer feedforward neural network is employed to modeling the highly nonlinear hydraulic actuator. The nonlinear neural model is instantaneously linearized at each sampling point. Estimated parameters from the linearized model are used in the generalized predictive control (GPC) algorithm to control the contact force. Simulation and experimental results show that the neural-based predictive controller can adapt to different environments and keep the contact force in a desired value despite high nonlinearity and uncertainty in the hydraulic actuator system.     

基于ANN的非线性系统GPC算法及仿真研究

将神经网络（ANN）技术应用于常规GPC算法，设计了基于ANN的非线性系统GPC结构方案，并对其控制原理和控制算法进行研究,基于ANN高度非线性映射等特性，运用数字仿真方法，对所设计的控制结构方案进行仿真研究,仿真结果显示，基于ANN的非线性系统GPC结构方案合理可行，并取得了满意的控制效果．     

执行器约束下基于数据驱动的参数化前馈控制器设计

针对执行器约束下非重复性点到点运动的轨迹跟踪问题, 提出了一种在执行器约束下基于数据驱动的参数化输入整形滤波器和前馈控制器优化设计算法. 首先对输入整形滤波器以及前馈控制器进行参数化, 然后在目标函数中加入控制信号变化量与控制信号能量的约束, 再采用基于数据驱动的迭代寻优算法得到最优参数, 在该参数下可以实现满足执行器约束条件下的运动控制系统轨迹最优跟踪性能. 并且由于采用了前馈参数化设计方法, 在点到点轨迹发生变化时所提出算法依然能够保持良好的轨迹跟踪性能. 仿真与实验结果表明在执行器约束下所提出算法能够实现最优点到点轨迹跟踪性能, 并且对非重复性点到点轨迹跟踪具有一定的鲁棒性.     

基于预报器的综合LQG自校正控制器*

本文推导了一类多项式LQG自校正控制算法,采用了增量和非增量两种模型形式。全面考虑了权P(z~(-1)),Q(z~(-1))和滤波多项式T(z~(-1))的作用,能控制存在有色噪声和前馈干扰的复杂过程。与广义预测控制仿真比较,结果表明这两种控制器均有良好的适应能力和强的鲁棒性。     

基于神经网络监督控制的拥塞控制算法研究

提出了一个基于神经网络控制的主动队列管理（AQM）算法;研究了TCP/AQM拥塞控制系统的可逆性,并利用一种神经网络监督控制结构进行了AQM算法的设计。算法由一个三层前馈结构的神经网络控制器（neural network controller,NNC）和一个反馈控制器（feedback controller,FC）组成。NNC作为一个前馈控制器,通过FC产生的教师信号进行学习,以建立被控对象的逆动力学模型。仿真结果表明,提出的算法与PI（proportional-integral）算法相比,无论在瞬态性能