双塔式电压发生器地震反应计算

以直流电压发生器结构的抗震计算为例,讨论了采用不同模型对双塔结构地震反应计算结果的影响.数值分析的结果表明：当采用单体模型计算时,主结构的计算结果与整体模型的计算结果差异不大,而从结构采用单体模型计算时,地震反应的数值较整体模型计算结果大,说明当主从双塔结构作为整体抵抗地震作用时,主结构会分担一部分作用在从结构上的地震荷载.通过输入不同场地类型地震波的双塔结构地震反应计算,讨论了双塔式直流电压发生器结构采用单体结构计算模型时应取的修正系数.     

基于T-S模糊模型的辨识与控制

针对一类非线性系统,采用模糊辫识的方法建立系统的T-S模型,结合广义预测控制对模糊预测控制进行研究.应用模糊聚类和递推最小二乘法辫识T-S模糊模型的前提结构和结论参数.对于非线性系统来讲,T-5模糊模型有良好的描述特性,并结合广义预测控制的滚动优化,实现对非线性系统的有效控制.仿真结果说明了该算法的有效性.     

形状记忆合金在结构被动控制中的研究现状与存在问题

利用形状记忆合金材料的超弹性效应和高阻尼特性制成的形状记忆合金被动阻尼器,主要用于桥梁和框架等结构中控制结构的地震反应.阐述了形状记忆合金在被动控制方面的研究及应用现状,指出了进一步的研究方向.     

基于模型预测控制的阵风减缓技术研究

针对传统控制方法在系统存在约束时性能下降的问题,采用了基于模型预测控制的阵风减缓方法。模型预测控制采用在线滚动优化策略,相比传统控制方法,能够应对由于约束和干扰等导致的系统性能恶化。系统以某大型飞机为例,对飞机进行了考虑阵风效应的动力学方程建模。分别采用模型预测和线性二次高斯控制方法设计阵风减缓控制系统,仿真结果表明,模型预测控制在无约束时性能与传统线性二次高斯控制基本一致;而当系统存在约束时,模型预测控制相比线性二次高斯控制器具有更优的性能。     

一类带有输入和状态约束的非线性稳定性约束模型预测控制的研究

在稳定性约束模型预测控制中,稳定性约束可从一级传递到另一级来限制用可控结构形式表示的系统状态向量大小。本文探讨了把线性模型预测控制扩展到非线性稳定性约束模型预测的控制（SCMPC）;在扩展的情况下,模型预测控制的最优化可直接用于非线性系统模型的控制,同时提供了稳定性约束模型预测控制用于带有输入和状态约束的非线性系统控制算法并将该算法用于仿真。仿真结果表明该算法是有效的。     

预测控制在数控机床伺服系统中的应用研究

提出一种采用模型算法PI预测控制及其在数控机床伺服系统的应用方案,对预测控制三要素(预测模型、参考轨迹和控制律)作了详细的论述.模型算法PI预测控制使数控机床伺服系统实现了快速、精密的位置控制.经仿真表明,该系统具有较好的鲁棒性和跟踪性能.     

基于扩张状态观测器的海工栈桥液压系统模型预测控制

海工栈桥是一种在海上作业期间运输维护人员和设备转移的大型海洋工程装备。针对海工栈桥液压系统外部扰动大、模型参数不确定等问题,提出了一种基于扩张状态观测器的模型预测控制方法。以海工栈桥液压系统为研究对象,对其进行运动学分析和动力学建模,推导出系统的状态空间方程;在传统模型预测控制的基础上,采用扩张状态观测器对液压系统的外部负载扰动以及机械系统未建模部分进行估计,以提高模型预测控制的跟踪效果。仿真结果表明,扩张状态观测器对海工栈桥液压系统的状态和扰动具有很好的观测能力,有效提高了模型预测控制的稳定性和鲁棒性。     

TLCD基础隔震框架结构的减振控制研究

为了避免和减轻由过大隔震层位移引起的损害,对基础隔震框架结构装设调频液柱阻尼器(tuned liquid column damper,简称TLCD)后混合系统的减振效果进行研究。建立了单层和多层混合控制系统在地震作用下的运动方程,采用TLCD-结构体系转化为调频质量阻尼器(tuned mass damper,简称TMD)-结构体系的等效方法,利用TMD参数优化公式,得到单个TLCD初始设计参数,并采用状态空间方程得到多个TLCD最优设计参数。通过对某8层基础隔震结构进行模拟,证明了该理论设计方法的合理性。该混合结构不仅可以减小隔震层位移和加速度,而且对上部结构位移和加速度反应都能更有效的控制。     

预测控制在单元机组协调控制系统中的应用

预测控制是20世纪70年代中后期出现的一类新型计算机控制算法.它具有控制效果好、鲁棒性强等优点.采用了广义预测与动态矩阵混合控制算法,该算法中以误差预测补充基于模型的预测,是一种综合预测.文中对单元机组协调控制系统进行了仿真.仿真结果表明,该算法具有较好的控制品质,增强了系统的稳定性和鲁棒性.     

一类非线性系统的广义预测控制研究

提出一种针对双线性Hamm erste in模型的预测控制策略。该策略将双线性Hamm erste in模型中的无记忆非线性静态增益环节,改进成易于由中间变量求取控制量的环节,避免求解高阶方程根的困难,又对双线性环节采用双线性系统的广义预测控制。避免解非线性优化问题,使得到的中间变量的表达式具有解析形式。由于引入广义预测控制中多步预测的思想,抗噪声的能力显著提高。仿真结果验证了该策略的有效性。     

Improved design of constrained model predictive tracking control for batch processes against unknown uncertainties

In this paper, an improved constrained tracking control design is proposed for batch processes under uncertainties. A new process model that facilitates process state and tracking error augmentation with further additional tuning is first proposed. Then a subsequent controller design is formulated using robust stable constrained MPC optimization. Unlike conventional robust model predictive control (MPC), the proposed method enables the controller design to bear more degrees of tuning so that improved tracking control can be acquired, which is very important since uncertainties exist inevitably in practice and cause model/plant mismatches. An injection molding process is introduced to illustrate the effectiveness of the proposed MPC approach in comparison with conventional robust MPC.     

Output feedback model predictive control of hydraulic systems with disturbances compensation

Enhancing the robustness of output feedback control has always been an important issue in hydraulic servo systems. In this paper, an output feedback model predictive controller (MPC) with the integration of an extended state observer (ESO) is proposed for hydraulic systems. The ESO was designed to estimate not only the unmeasured system states but also the disturbances, which will be synthesized into the design of the output prediction equation. Based on the mechanism of receding horizon and repeating optimization of MPC, the output prediction equation will be updated in real time and the future behavior of the system will be accurately predicted since the disturbances are compensated effectively. Hence, the ability of the traditional MPC to suppress disturbances will be improved evidently. The experiment results show that the proposed controller has high-performance nature and strong robustness against various model uncertainties, which verifies the effectiveness of the proposed control strategy.     

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。     

Comparative Study of Trajectory Tracking Control for Automated Vehicles via Model Predictive Control and Robust H-infinity State Feedback Control

A comparative study of model predictive control(MPC) schemes and robust H_∞ state feedback control(RSC) method for trajectory tracking is proposed in this paper. The main objective of this paper is to compare MPC and RSC controllers' performance in tracking predefined trajectory under different scenarios. MPC controller is designed based on the simple longitudinal-yaw-lateral motions of a single-track vehicle with a linear tire, which is an approximation of the more realistic model of a vehicle with double-track motion with a non-linear tire mode. RSC is designed on the basis of the same method as adopted for the MPC controller to achieve a fair comparison. Then, three test cases are built in CarSim-Simulink joint platform. Specifically, the verification test is used to test the tracking accuracy of MPC and RSC controller under well road conditions. Besides, the double lane change test with low road adhesion is designed to find the maximum velocity that both controllers can carry out while guaranteeing stability. Furthermore, an extreme curve test is built where the road adhesion changes suddenly, in order to test the performance of both controllers under extreme conditions. Finally, the advantages and disadvantages of MPC and RSC under different scenarios are also discussed.     

Real-time comparison of a number of predictive controllers

Many model predictive control (MPC) algorithms have been proposed in the literature depending on the conditionality of the system matrix and the choice of its cost-function. This paper presents the newer MPC schemes such as extended predictive control (EPC) and shifted MPC as well as other well known forms. The control performance of these controllers are compared using two systems that are slow and fast reacting. The closed-loop responses are compared and the differences and similarities are explained on the basis of the structure of the control schemes. Disturbance rejection and the tracking of various setpoint trajectories are performed with good closed-loop results from all the controllers. It was found that the controllers that were specifically designed to reduce the system matrix ill-conditionality such as EPC and generalized predictive control provided better control performance when compared to other MPC methods.     

Grouped-neural network modeling for model predictive control

A group of feed-forward neural networks (NNs), each providing the prediction of an individual process output at a future step, is used as the dynamic prediction model for the model-based predictive control (MPC) scheme in the proposed work. These NNs are parallel (independent) rather than cascaded--they are trained and implemented in parallel. Therefore, the complexity and effort in the training stage is decreased and compounded error propagation is eliminated from the prediction. A new strategy of compensating for the process-model mismatch under this grouped-NN model structure is also developed. Effectiveness of the scheme as a general nonlinear MPC is demonstrated by simulation results.     

不可靠WSN时钟同步网络化输出反馈MPC量化分析

在Cyber-Physical环境下,时钟同步双向信息交换过程中,包含时钟信息的数据包丢失将对时钟同步性能产生影响。讨论了现代控制理论状态空间模型的输出反馈Tubes-MPC时钟同步方法。由分离原理,设计了本地化的状态估计器与控制器,实现了输出反馈Tubes-MPC时钟同步的指数稳定。以不完全量测下的观测模型为基础,定量分析了统计意义下的同步误差方差上界与下界,并采用MPC中Set-Theory-in-Control方法,将完全量测下的干扰误差集合运算于由丢包所引入的附加的估计误差集合,建立了集合约束下的模型预测优化模型。已构建的统一框架下的输出反馈Tubes-MPC时钟同步系统化方法,综合考量了控制理论在线计算复杂度与网络控制观点应用的可行性,对无线网络的不可靠性、网络规模、收敛性能具有鲁棒性,进一步容易扩展为网络级绝对时钟状态空间模型。     

预测控制的新进展

随着预测控制在工业过程中日益广泛的应用，近年来，有关预测控制理论与应用方面的研究也取得了长足的进步。本文就多年来，预测控制理论与应用的发展作了一个简要的回顾，并着重就预测控制的稳定性和鲁棒性及其所存在的困难作了分析，并对预测控制今后可能的发展方向作了进一步的探讨。     

基于变预测时域MPC的自动驾驶汽车轨迹跟踪控制研究

为了保证自动驾驶汽车轨迹跟踪的精度及行驶过程中的稳定性,提出一种基于车辆横向稳定状态在线识别和模糊算法的变预测时域模型预测控制（MPC）方法。针对车辆稳定状态的在线识别,采用k-means聚类算法对车辆行驶状态参数进行聚类分析,得到聚类质心,通过在线对比当前车辆状态量与不同聚类质心之间的欧氏距离获取车辆的实时安全等级。同时计算出当前车辆的轨迹跟踪横向偏移量,以这二者为输入,通过模糊控制算法在线计算出预测时域的变化量并输出给MPC控制器实现预测时域的自适应调整,最后求解出自动驾驶车辆跟踪轨迹的最优的控制序列,以达到在保持车辆稳定的前提下实现高精度轨迹跟踪控制的目的。CarSim/Simulink联合仿真结果表明,改进后的变预测时域MPC算法在提高自动驾驶汽车轨迹跟踪精度及横向稳定性方面的表现优于传统MPC控制器。     

Distributed fault-tolerant model predictive control for intermittent fault: A cooperative way

For intermittent actuator faults of large-scale system, a cooperative distributed fault-tolerant model predictive control (DFTMPC) is presented. The actuator plug and play strategy is adopted in the interconnected systems with physical coupling making fault estimation and controller redesign unnecessary. The actuator plug and play process is modeled as a distributed switching model, and there a theoretical stability analysis is provided with switching form of model predictive control (MPC) cost functions. The novel cooperative distributed fault-tolerant performance index is raised in a global view for distributed model predictive control. A simulation example is taken to show the e?ectiveness of the proposed method.