Intelligent technology-based control of motion and vibration using MR dampers

Due to their intrinsically nonlinear characteristics,development of control strategies that are implementable and can fully utilize the capabilities of semiactive control devices is an important and challenging task.In this study,two control strategies are proposed for protecting buildings against dynamic hazards,such as severe earthquakes and strong winds,using one of the most promising semiactive control devices,the magnetorheological (MR) damper.The first control strategy is implemented by introducing an inverse neural network (NN) model of the MR damper.These NN models provide direct estimation of the voltage that is required to produce a target control force calculated from some optimal control algorithms.The major objective of this research is to provide an effective means for implementation of the MR damper with existing control algorithms.The second control strategy involves the design of a fuzzy controller and an adaptation law.The control objective is to minimize the difference between some desirable responses and the response of the combined system by adaptively adjusting the MR damper.The use of the adaptation law eliminates the need to acquire characteristics of the combined system in advance. Because the control strategy based on the combination of the fuzzy controller and the adaptation law doesn't require a prior knowledge of the combined building-damper system,this approach provides a robust control strategy that can be used to protect nonlinear or uncertain structures subjected to random loads.

Identification of Nonlinear Dynamic Systems Using Diagonal Recurrent Neural Networks

In order to apply a new dynamic neural network- Diagonal Recurrent Neural NetWork (DRNN) to the system identificationof nonlinear dynamic Systems and construct more accurate system models, the structure and learning method (DBP algorithm) of theDRNN are Present6d. Nonlinear system characteriStics can be identified by presenting a set of input / output patterns tO the DRNN andadjusting its weights with the DBP algorithm. Experimental results show that the DRNN has good performances in the identification ofnonlinear dynamic systems in comparison with BP networks.

Modeling and Algorithm Application of Weapon Assignment System

In order to improve weapon assignment(WA)accuracy in real scenario,an artificial neural network(ANN)model is built to calculate real-time weapon kill probabilities.Considering the WA characteristic,each input representing one assessment index should be normalized properly.Therefore,the modified WA model is oriented from constant value to dynamic computation.Then an improved invasive weed optimization algorithm is applied to solve the WA problem.During search process,local search is used to improve the initial population,and seed reproduction is redefined to guarantee the mutation from multipoint to single point.In addition,the idea of vaccination and immune selection in biology is added into optimization process.Finally,simulation results verify the model′s rationality and effectiveness of the proposed algorithm.

Deep Learning Based Model Predictive Control for a Reverse Osmosis Desalination Plant

Reverse Osmosis (RO) desalination plants are highly nonlinear multi-input-multioutput systems that are affected by uncertainties, constraints and some physical phenomena such as membrane fouling that are mathematically difficult to describe. Such systems require effective control strategies that take these effects into account. Such a control strategy is the nonlinear model predictive (NMPC) controller. However, an NMPC depends very much on the accuracy of the internal model used for prediction in order to maintain feasible operating conditions of the RO desalination plant. Recurrent Neural Networks (RNNs), especially the Long-Short-Term Memory (LSTM) can capture complex nonlinear dynamic behavior and provide long-range predictions even in the presence of disturbances. Therefore, in this paper an NMPC for a RO desalination plant that utilizes an LSTM as the predictive model will be presented. It will be tested to maintain a given permeate flow rate and keep the permeate concentration under a certain limit by manipulating the feed pressure. Results show a good performance of the system.

Gait planning and intelligent control for a quadruped robot

We present a method for designing free gaits for a structurally symmetrical quadruped robot capable of performing statically stable, omnidirectional walking on irregular terrain. The robot＇s virtual model is constructed and a control algorithm is proposed by applying virtual components at some strategic locations. The deliberative-based controller can generate flexible sequences of leg transferences while maintaining walking speed, and choose optimum foothold for moving leg based on integration data of exteroceptive terrain profile. Simulation results are presented to show the gait＇s efficiency and system＇s stability in adapting to an uncertain terrain.

Hierarchical CNNPID Based Active Steering Control Method for Intelligent Vehicle Facing Emergency Lane-Changing

To resolve the response delay and overshoot problems of intelligent vehicles facing emergency lane-changing due to proportional-integral-differential(PID)parameter variation,an active steering control method based on Convolutional Neural Network and PID(CNNPID)algorithm is constructed.First,a steering control model based on normal distribution probability function,steady constant radius steering,and instantaneous lane-change-based active for straight and curved roads is established.Second,based on the active steering control model,a three-dimensional constraint-based fifth-order polynomial equation lane-change path is designed to address the stability problem with supersaturation and sideslip due to emergency lane changing.In addition,a hierarchical CNNPID Controller is constructed which includes two layers to avoid collisions facing emergency lane changing,namely,the lane change path tracking PID control layer and the CNN control performance optimization layer.The scaled conjugate gradient backpropagation-based forward propagation control law is designed to optimize the PID control performance based on input parameters,and the elastic backpropagation-based module is adopted for weight correction.Finally,comparison studies and simulation/real vehicle test results are presented to demonstrate the effectiveness,significance,and advantages of the proposed controller.

Intelligent vectorial surrogate modeling framework for multi-objective reliability estimation of aerospace engineering structural systems

To improve the computational efficiency and accuracy of multi-objective reliability estimation for aerospace engineering structural systems,the Intelligent Vectorial Surrogate Modeling(IVSM)concept is presented by fusing the compact support region,surrogate modeling methods,matrix theory,and Bayesian optimization strategy.In this concept,the compact support region is employed to select effective modeling samples;the surrogate modeling methods are employed to establish a functional relationship between input variables and output responses;the matrix theory is adopted to establish the vector and cell arrays of modeling parameters and synchronously determine multi-objective limit state functions;the Bayesian optimization strategy is utilized to search for the optimal hyperparameters for modeling.Under this concept,the Intelligent Vectorial Neural Network(IVNN)method is proposed based on deep neural network to realize the reliability analysis of multi-objective aerospace engineering structural systems synchronously.The multioutput response function approximation problem and two engineering application cases(i.e.,landing gear brake system temperature and aeroengine turbine blisk multi-failures)are used to verify the applicability of IVNN method.The results indicate that the proposed approach holds advantages in modeling properties and simulation performances.The efforts of this paper can offer a valuable reference for the improvement of multi-objective reliability assessment theory.

基于Power Bus总线技术的智能灌溉系统

以进一步提升智能灌溉系统的运行作业效率为目标,选取Power Bus总线技术作为优化原理,针对灌溉系统的通信控制展开设计。考虑灌溉系统的核心组成与功能配置,搭建网络通信能耗控制模型,对灌溉系统的网络节点进行最优化布局,形成以Power Bus总线为控制主线的系统运行模式,展开灌溉作业通信控制试验。结果表明:基于Power Bus总线技术的灌溉系统设计方案正确可行,有效降低了数据丢包率,系统通信成功率相对可提升6.75%,整体系统的综合灌溉效率可达96.61%,满足智能灌溉的设计指标要求;系统运行稳定率保持在95%以上,系统各组件响应及时稳定,具有高度的可用性与可靠性,且灌溉水资源得到最大化与均匀化的运用。

Identifying the validity domain of machine learning models in building energy systems

The building sector significantly contributes to climate change.To improve its carbon footprint,applications like model predictive control and predictive maintenance rely on system models.However,the high modeling effort hinders practical application.Machine learning models can significantly reduce this modeling effort.To ensure a machine learning model’s reliability in all operating states,it is essential to know its validity domain.Operating states outside the validity domain might lead to extrapolation,resulting in unpredictable behavior.This paper addresses the challenge of identifying extrapolation in data-driven building energy system models and aims to raise knowledge about it.For that,a novel approach is proposed that calibrates novelty detection algorithms towards the machine learning model.Suitable novelty detection algorithms are identified through a literature review and a benchmark test with 15 candidates.A subset of five algorithms is then evaluated on building energy systems.First,on two-dimensional data,displaying the results with a novel visualization scheme.Then on more complex multi-dimensional use cases.The methodology performs well,and the validity domain could be approximated.The visualization allows for a profound analysis and an improved understanding of the fundamental effects behind a machine learning model’s validity domain and the extrapolation regimes.

Safe operation of online learning data driven model predictive control of building energy systems

Model predictive control is a promising approach to reduce the CO 2 emissions in the building sector.However,the vast modeling effort hampers the widescale practical application.Here,data-driven process models,like artificial neural networks,are well-suited to automatize the modeling.However,the underlying data set strongly determines the quality and reliability of artificial neural networks.In general,the validity domain of a machine learning model is limited to the data that was used to train it.Predictions based on system states outside that domain,so-called extrapolations,are unreliable and can negatively influence the control quality.We present a safe operation approach combined with online learning to deal with extrapolation in data-driven model predictive control.Here,the k-nearest neighbor algorithm is used to detect extrapolation to switch to a robust fallback controller.By continuously retraining the artificial neural networks during operation,we successively increase the validity domain of the artificial neural networks and the control quality.We apply the approach to control a building energy system provided by the BOPTEST framework.We compare controllers based on two data sets,one with extensive system excitation and one with baseline operation.The system is controlled to a fixed temperature set point in baseline operation.Therefore,the artificial neural networks trained on this data set tend to extrapolate in other operating points.We show that safe operation in combination with online learning significantly improves performance.

基于计算机网络控制的智能喷灌系统应用研究

为了实现植株根部含水率的精确控制,基于ZigBee技术建立了智能喷灌系统。计算表层含水率变化量、平均温度、日照长度与根部含水率变化量的相对关联系数,选定表层含水率变化量为自变量参数,采用线性拟合的方法,建立根部含水率变化量模型;探究喷灌阀控制信号和喷灌流量之间的关系,进而建立喷灌阀控制信号与表层含水率变化量之间的模型;设计系统工作流程,实现根部含水率精确控制。对网络系统监测表层含水率的精度和根部含水率恒定控制进行测试,表明系统具有良好的工作性能。

计及占用影响的集成楼宇暖通空调负荷群配电网优化方法

提出了一种考虑人员占用影响的集成楼宇暖通空调(heating,ventilation and air conditioning,HVAC)负荷群配电电网优化方法。首先,依据建筑围护热阻热容网络和配电网支路潮流方程构建了集成暖通空调负荷群配电网数学模型。其次,利用梯形隶属度函数模糊化占用人数并考虑其对暖通空调负荷温度调控的影响,形成包含占用松弛函数约束的暖通空调负荷群与配电网联合优化方法。最后,通过模型预测控制方法实现了集成楼宇暖通空调负荷群配电网的滚动优化控制。改进的IEEE33节点配电网系统仿真结果表明,所提出的计及占用影响的联合优化模型,在楼宇侧能够满足人员热舒适性,同时减少楼宇暖通空调负荷能耗,在电网侧则减少配电网线路损耗和节点电压的波动,有效提高楼宇侧和电网侧整体的节能水平。

基于DRNN神经网络的造纸过程定量水分解耦控制分析

阐述造纸过程定量水分的控制技术,利用Matlab建立定量水分数学模型,分别采用常规PID算法和DRNN神经网络算法对定量水分耦合模型进行解耦控制,探讨神经网络来整定PID控制器参数,不依赖控制对象的数学模型就可以实现解耦控制。仿真结果表明,DRNN神经网络算法响应速度更快,自适应能力显著增强,可进一步改善系统的动态性能。

铅基固废协同熔炼过程在线智能优化控制

铅基固废是生产端和消费端产生的常见固体废物,具有很强的污染性,其绿色处理技术是实现含铅废物污染防治的关键环节。双底吹炼铅工艺协同处理铅基固废是一种经济合理的铅基固废处理方式,相对于传统矿铅冶炼生产操作更为复杂,为确保生产状况处于稳定、经济的状态,对工艺控制系统有了更高要求。中国恩菲工程技术有限公司研发了一套铅基固废协同熔炼过程在线智能优化控制系统,整个系统由在线优化控制系统、智能预警与高效监控系统和系统数据库集成,主要实现以下功能:(1)针对铅基固废协同熔炼过程自动化水平不高的问题,基于冶炼过程机理与生产运行大数据,应用计算机建模和神经网络方法建立了冶炼过程关键参数预测模型,开发了一套铅基固废协同熔炼在线优化控制系统;(2)以车间实景模型为载体,建立了铅基固废协同熔炼智能预警与高效监控系统,实现了工业信号、场景及流程的数字化与可视化;(3)控制系统集成了现场相关软硬件,详细展示各模块间的数据交互与协同运行情况,形成了智能、高效、安全的协同熔炼车间。

基于FNN的城市景观照明智能节能控制方法仿真

为了降低景观照明用电能耗,提升节能效果,提出基于FNN的城市景观照明智能节能控制方法。分析城市景观照明需求,依据分析结果建立景观照明区域划分模型,利用上述模型将城市景观照明区域划分为开启区域和关闭区域,并在开关区域之间建立缓冲区域,规避城市景观照明控制时出现的延迟误差问题;采集景观照明设备运行状态数据,通过FNN网络对其实施训练学习,获取完整的设备状态数据集;基于获取的数据集通过模糊神经网络设计节能控制器,并利用以上节能控制器实现城市景观照明智能节能控制。实验结果表明,使用该方法对景观照明开展智能节能控制时,调光时长、照明时间以及用电能耗均得到了良好控制,说明其能够满足照明节能需求。

基于长短时记忆网络的结构动态载荷预测方法

[目的]针对传统代理模型无法处理具有时间依赖性的动态过程和异构数据的问题,提出一种基于长短时记忆网络(LSTM)的动态载荷代理模型方法。[方法]代理模型包含载荷特征编码和载荷响应解码2个模块。首先,通过载荷特征编码模块的LSTM对动态外载荷时间序列进行特征提取;然后,将外载荷时序特征与结构参数特征进行融合,由载荷解码模块的LSTM进一步进行特征提取并生成最终输出,从而综合考虑动态外载荷时间序列和结构参数一维特征的异构数据输入,预测结构内力响应时间历程;最后,在有限元仿真数据集上对模型进行精度评估,并与其他代理模型方法进行对比。[结果]结果显示,该动态载荷代理模型的平均精度可达98%,高于其他对比方法,且计算速度相较于有限元方法更快。[结论]所提方法可解决时序-非时序异构数据的代理模型问题,具有精度高、效率高的优点,在快速迭代计算场景下能够发挥较大作用。

基于多层ReLU网络的楼宇暖通空调系统能量管理策略

提升现代智能楼宇的能量管理能力,是电力紧平衡背景下促进电网节能增效的重要举措。文中提出一种数据驱动的楼宇暖通空调系统节能控制策略,避免了传统模型预测控制对精确热力学建模的依赖。首先,在搭建楼宇热网络仿真模型的基础上,采用延时嵌入法构造输入特征,建立基于修正线性单元的多层神经网络模型,实现室内温度时间序列的多步预测。然后,针对电网的分时电价,构建滚动时域优化模型,并将其重构为混合整数线性规划的形式,实现有限控制周期内优化模型的高效求解。最后,基于Simscape搭建楼宇热仿真模型,验证了所提控制策略的有效性。仿真结果表明,所提策略能够满足温度舒适度要求,并提高楼宇的经济运行水平。

建筑结构BIM正向设计的发展困境、关键技术与应用实践

建筑信息模型(BIM)正向设计是推动建筑业数字化转型的核心技术,是智能建造和智能运维的起点,但应用和推广仍面临阻力。总结了建筑结构BIM正向设计发展困境,指出走出困境的关键技术:面向大体量复杂建筑的高效BIM底座、面向有限元计算的拓扑关系实时维护系统、基于BIM的智能设计平台。结果表明:自研BIM底座突破了主流BIM平台性能瓶颈;拓扑关系实时维护系统实现了BIM模型和有限元模型的“双模一体”;智能设计平台建立了梁、板、柱、墙的智能配筋设计解决方案,设计了维护和管理人为干预配筋意图的机制,确保数据的一致性和可追溯性,在高效应对项目频繁修改与变更的同时,实现“图模一致”。最终,集成各项技术开发了建筑结构BIM正向设计平台,并在多个项目中应用,为智能建造、智能运维提供了完备、可靠的数字化模型。

基于BIM的卢赛尔体育场智能化建造方法及应用

为探索以BIM为主的智能化建造技术在大跨空间结构建设全生命期中的应用模式,提出一套系统的基于BIM的智能化建造方法,以2022年世界杯主体育场即卢赛尔体育场为背景,建立了基于BIM的智能建造方法在大跨空间结构建设全过程中的应用流程。通过对该项目建设重难点进行梳理,采用国际标准和全生命期一体化的理念,从设计、施工、运维三个阶段入手详述BIM技术在卢赛尔体育场建设中的综合创新应用。研究成果完善了基于BIM的智能化建造方法,解决了国际工程建设过程中全球多点异地协同和施工管理难度大等问题。

Unified stabilizing controller synthesis approach for discrete-time intelligent systems with time delays by dynamic output feedback

A novel model, termed the standard neural network model （SNNM）, is advanced to describe some delayed （or non-delayed） discrete-time intelligent systems composed of neural networks and Takagi and Sugeno （T-S） fuzzy models. The SNNM is composed of a discrete-time linear dynamic system and a bounded static nonlinear operator. Based on the global asymptotic stability analysis of the SNNMs, linear and nonlinear dynamic output feedback controllers are designed for the SNNMs to stabilize the closed-loop systems, respectively. The control design equations are shown to be a set of linear matrix inequalities （LMIs） which can be easily solved by various convex optimization algorithms to determine the control signals. Most neural-network-based （or fuzzy） discrete-time intelligent systems with time delays or without time delays can be transformed into the SNNMs for controller synthesis in a unified way. Three application examples show that the SNNMs not only make controller synthesis of neural-network-based （or fuzzy） discrete-time intelligent systems much easier, but also provide a new approach to the synthesis of the controllers for the other type of nonlinear systems.