修改的预测控制方法在智能结构振动抑制中的应用

在输出预测控制的基础上，提出了一种修改的预测控制算法，即在预测时域内，使系统的控制输入不再保持不变，而是在一定的时域内可以线性改变，这样提高了控制的灵活性和系统的动态性能。将修改的预测控制算法应用于悬臂梁的振动控制。仿真结果表明，基于新的预测控制算法更加有效地抑制了柔性结构的振动。     

弹性连杆机构振动的模型预测主动控制研究

基于机构离散状态空间模型,提出了抑制弹性连杆机构振动响应的模型预测主动控制方法。将复杂机构动力学模型中的非线性因素、耦合因素以及系统高阶模态的影响作为扰动,建立了预测机构动态变形响应的预测模型,采用带实时预测误差修正的模型预测控制方法对弹性连杆机构的振动进行了主动控制,降低了控制方法对复杂机构数学模型的要求,提高了控制系统的实时性。数值仿真和实验结果证明了该方法的有效性。     

转子系统振动的灰色预测控制研究

分析了转子系统灰色预测控制设计方法及应用。针对单盘对称转子轴承系统 ,建立了灰色模型 GM(1,1)为主体的灰色预测控制模块 ,进行了振动主动控制研究 ,仿真计算结果表明 ,将灰色预测控制理论与方法应用于转子系统振动主动控制是可行的、有效的     

非线性广义预测控制算法及其仿真研究

为了实现对非线性广义预测控制算法的仿真研究,采用神经网络建立非线性预测模型,在其工作点附近线性化,并对线性化模型进行广义预测控制,利用单片输出BP网络所辨识的非线性模型进行分析,提出了一种基于非线性广义预测前馈补偿控制律的补偿算法,改善了线性化所带来的模型失配误差.最后通过Matlab进行了仿真.仿真结果验证了算法的有效性.     

基于模型预测控制策略的协同自适应巡航控制仿真研究

本文基于模型预测控制原理设计了上层控制器,用来决策期望加速度以实现良好的跟车效果,并将期望加速度变化作为优化条件之一引入代价函数中,目的是为了更有效的优化油耗和乘坐舒适性,最后利用Simulink和CarSim的联合仿真验证所提出的控制器。从结果可以看到所设计的控制器能够很有效的控制跟随车辆跟随领航车迅速做出一系列加减速机动,可以在相同的道路条件下使车辆以更小的车间距保持队列行驶来增加道路容量,同时能降低油耗。通过加速度的变化结果可以看到燃油车的换挡操作会造成加速度的突变从而影响乘坐舒适。     

基于灰色预测的无模型控制在球磨机负荷控制中的仿真研究

火电厂钢球磨煤机的负荷对象具有大滞后、慢时变、强非线性等复杂特性,采用常规控制方法难以获得满意的控制效果,本文提出了基于灰色预测的无模型自适应负荷控制方法.该方法融合了无模型控制的自适应、抗干扰特性与灰色预测模型的预测时延、抑制超调和快速稳定特性,它将灰色模型的预测结果代替负荷对象输出测量值,再进行无模型自适应闭环控制.仿真结果表明这种控制方法系统响应快、超调小、鲁棒性好、抗干扰能力强,可以有效解决大滞后、非线性及适应性等问题.     

直线电机进给系统轮廓误差预测控制方法及仿真研究

为提高永磁同步直线电机驱动的进给系统轮廓轨迹跟踪精度和系统的动态性能,提出了一种显式模型预测交叉耦合控制方法（Explicit model predictive cross-coupled control, EMPCCC）。该方法结合显式预测控制原理与交叉耦合控制思想,对单轴电流和速度信号进行多步预测,将轮廓误差作为反馈量来修正预测控制的给定轨迹,达到轮廓误差预测控制的目的。基于MATLAB/Simulink搭建仿真模型,结果表明,所提EMPCCC方法能快速实现不同转速波形的无超调跟踪控制,且可以实时估计并补偿轮廓误差,提升不同轨迹的轮廓精度。     

智能结构振动控制

介绍了近年新发展起来利用机敏材料作为传感元件和致动元件的智能结构振动控制与传统振动控制系统的区别,阐述了智能结构振动控制基本组成、特性及控制形式和机理,对其中若干关键技术进行了分析和论述,分析了近年来发展状况和最新的进展,提出了若干发展思路     

基于变结构预测控制的解耦及其应用

提出了基于变结构预测控制的解耦控制方法,证明了该方法的理论基础,给出了具体的解耦控制算法,并成功应用在橡胶硫化仪温度控制系统中.     

时滞多变量系统的变结构预测控制

本文提出了一类时滞多变量系统的变结构预测控制方法,仿真效果证明了该方法对系统时滞具有较强的鲁棒性,对参数时变具有自适应性.     

Robust control of saturating systems with Markovian packet dropouts under distributed MPC

In this work, a robust control methodology is presented for saturating systems with packet dropouts under distributed model predictive control framework. The sequence of time instants when data dropout happens is modeled by a Markov chain. A packet dropout compensation strategy and an augmented Markov jump linear model are considered simultaneously. To design distributed model predictive controllers, the entire system is decomposed into coupled subsystems. Considering the influences of neighbor subsystems, a distributed predictive control synthesis involving packet dropouts and Markovian probabilities is developed by minimizing the worst-case performance index at each time instant. The input saturation constraints are also incorporated into the robust controller design under distributed model predictive control framework. Furthermore, both the recursive feasibility of the proposed robust control under distributed model predictive control and the closed-loop mean-square stability are proved. To show the effectiveness, the proposed methodology is validated by simulations on a continuous stirred tank reactor process and a DC control system.     

基于模型预测控制的协同式自适应巡航控制系统

在城市交通工况中,车辆的驾驶行为对其乘坐舒适性及燃油消耗有着很大的影响。因此提出一种在包含交通灯等信息的交通工况下的协同式自适应巡航控制系统,通过减少不必要的速度保持或加速来提升性能。系统通过处理当前交通信息的数据判断跟踪目标类别,运用模型预测控制来预测前车或车队未来状态,对不同的前方目标采用不同的权值来计算最优控制输入。通过控制车辆保持安全距离并在优化速度下行驶以实现多目标的优化。利用CarSim和Simulink联合仿真,仿真结果显示该控制系统在保证安全的前提下实现了主动的速度调节及目标的切换,在指定仿真工况中对比线性二次调节算法,加速度峰值、加速度变化率峰值及燃油消耗均有所降低,乘坐舒适性和燃油经济性得到较大提升。     

Adaptive MPC based on MIMO ARX-Laguerre model

This paper proposes a method for synthesizing an adaptive predictive controller using a reduced complexity model. This latter is given by the projection of the ARX model on Laguerre bases. The resulting model is entitled MIMO ARX-Laguerre and it is characterized by an easy recursive representation. The adaptive predictive control law is computed based on multi-step-ahead finite-element predictors, identified directly from experimental input/output data. The model is tuned in each iteration by an online identification algorithms of both model parameters and Laguerre poles. The proposed approach avoids time consuming numerical optimization algorithms associated with most common linear predictive control strategies, which makes it suitable for real-time implementation. The method is used to synthesize and test in numerical simulations adaptive predictive controllers for the CSTR process benchmark.     

Practical approach to tuning MPC

This paper presents the results of a heuristic approach for developing model predictive control (MPC) tuning rules. The tuning has been applied and tested in easy-to-use MPC. Process modeling in this MPC uses normalized input/ output range. As a result there is no need for tuning outputs, a procedure known as adjusting equal concern error. Penalties on moves are set as a function of process dead time as the primary factor, with some correction from process gain. The default calculation delivers robust control, which tolerates up to triple increase in process static gain. If control is too aggressive, further on-line adjustment can be done by set point reference trajectory. Test results show that this tuning is robust for process gain change, however, it is much less efficient in compensating for process dead-time changes. It was found that dead-time mismatch is much better compensated with the model correction filter. Combining the three handles, i.e., penalties on moves, reference trajectory, and model filter, easy and intuitively understandable MPC tuning was achieved. The findings are illustrated by numerous MPC simulated tests.     

基于弹簧的新型高楼逃生器

如何及时、有效、便捷的进行高楼紧急脱险,已经成为世界关注的重要话题.通过分析国内外现有的几种高楼逃生器,提出了一种新的设计思想--基于扭矩的新型的高楼逃生器.该设计成本低廉,结构紧凑、安全可靠、使用方便、平稳快速,适用于小型家庭紧急脱险使用.     

某自动化立体仓库的高层钢货架结构设计

介绍了某自动化立体仓库高层钢货架的设计;结合实际工程的设计,概括了该类结构在体系布置、内力分析及构件承载力验算等方面的基本原则,为以后同类工程的设计提供了范例和设计依据。     

MIMO model of an interacting series process for Robust MPC via System Identification

This paper discusses the empirical modeling using system identification technique with a focus on an interacting series process. The study is carried out experimentally using a gaseous pilot plant as the process, in which the dynamic of such a plant exhibits the typical dynamic of an interacting series process. Three practical approaches are investigated and their performances are evaluated. The models developed are also examined in real-time implementation of a linear model predictive control. The selected model is able to reproduce the main dynamic characteristics of the plant in open-loop and produces zero steady-state errors in closed-loop control system. Several issues concerning the identification process and the construction of a MIMO state space model for a series interacting process are deliberated.     

Experimental evaluation of control performance of MPC as a regulatory controller

Proportional-integral-derivative (PID) control is widely practised as the base layer controller in the industry due to its robustness and design simplicity. However, a supervisory control layer over the base layer, namely a model predictive controller (MPC), is becoming increasingly popular with the advent of computer process control. The use of a supervisory layer has led to different control structures. In this study, we perform an objective investigation of several commonly used control structures such as ‘Cascaded PI controller’, ‘DMC cascaded to PI’ and ‘Direct DMC’. Performance of these control structures are compared on a pilot-scale continuous stirred tank heater (CSTH) system. We used dynamic matrix control (DMC) algorithm as a representative of MPC. In the DMC cascaded to PI structure, the flow-loops are regulated by the PI controller. On top of that a DMC manipulates the set-points of the flow-loops to control the temperature and the level of water in the tank. The ‘Direct DMC’ structure, as its name suggests, uses DMC to manipulate the valves directly. Performance of all control structures were evaluated based on the integrated squared error (ISE) values. In this empirical study, the ‘Direct DMC’ structure showed a promise to act as regulatory controller. The selection of control frequency is critical for this structure. The effect of control frequency on controller performance of the ‘Direct DMC’ structure was also studied.     

基于线性时变模型预测控制的自主车辆轨迹跟踪控制器设计与验证

随着自动驾驶技术的快速发展,精确的轨迹跟踪已经成为汽车工业和学术领域公认的实现自主车辆运动控制的核心技术之一。为提高自主车辆轨迹跟踪的实时性与准确性,提出一种应用于自主车辆的线性时变模型预测跟踪控制器（Linear time-varying model predictive controller,LTV-MPC）设计方法。根据运动学原理建立某自主无人小车的二自由度运动学模型,其次,基于该模型构建车辆轨迹跟踪系统的误差模型并利用线性参数化理论对其进行离散化,在模型预测控制框架内将该轨迹跟踪控制器的设计转化为一个线性二次规划最优问题。在一个实际搭建的自主车辆试验平台上对所提出控制器的有效性进行不同预设参考路径轨迹下的实车验证,结果表明,该自主车辆能够对所预设的实际参考道路轨迹进行快速、准确的轨迹跟踪控制,且具有较好的行驶稳定性能。