Data Driven Vibration Control:A Review

With the ongoing advancements in sensor networks and data acquisition technologies across various systems like manufacturing,aviation,and healthcare,the data driven vibration control(DDVC)has attracted broad interests from both the industrial and academic communities.Input shaping(IS),as a simple and effective feedforward method,is greatly demanded in DDVC methods.It convolves the desired input command with impulse sequence without requiring parametric dynamics and the closed-loop system structure,thereby suppressing the residual vibration separately.Based on a thorough investigation into the state-of-the-art DDVC methods,this survey has made the following efforts:1)Introducing the IS theory and typical input shapers;2)Categorizing recent progress of DDVC methods;3)Summarizing commonly adopted metrics for DDVC;and 4)Discussing the engineering applications and future trends of DDVC.By doing so,this study provides a systematic and comprehensive overview of existing DDVC methods from designing to optimizing perspectives,aiming at promoting future research regarding this emerging and vital issue.

An Improvement on Data-Driven Pole Placement for State Feedback Control and Model Identification

The recently proposed data-driven pole placement method is able to make use of measurement data to simultaneously identify a state space model and derive pole placement state feedback gain. It can achieve this precisely for systems that are linear time-invariant and for which noiseless measurement datasets are available. However, for nonlinear systems, and/or when the only noisy measurement datasets available contain noise, this approach is unable to yield satisfactory results. In this study, we investigated the effect on data-driven pole placement performance of introducing a prefilter to reduce the noise present in datasets. Using numerical simulations of a self-balancing robot, we demonstrated the important role that prefiltering can play in reducing the interference caused by noise.

A Data-Driven Adaptive Method for Attitude Control of Fixed-Wing Unmanned Aerial Vehicles

In this paper, a real-time online data-driven adaptive method is developed to deal with uncertainties such as high nonlinearity, strong coupling, parameter perturbation and external disturbances in attitude control of fixed-wing unmanned aerial vehicles (UAVs). Firstly, a model-free adaptive control (MFAC) method requiring only input/output (I/O) data and no model information is adopted for control scheme design of angular velocity subsystem which contains all model information and up-mentioned uncertainties. Secondly, the internal model control (IMC) method featured with less tuning parameters and convenient tuning process is adopted for control scheme design of the certain Euler angle subsystem. Simulation results show that, the method developed is obviously superior to the cascade PID (CPID) method and the nonlinear dynamic inversion (NDI) method.

Data-Driven Model Identification and Control of the Inertial Systems

In the synthesis of the control algorithm for complex systems, we are often faced with imprecise or unknown mathematical models of the dynamical systems, or even with problems in finding a mathematical model of the system in the open loop. To tackle these difficulties, an approach of data-driven model identification and control algorithm design based on the maximum stability degree criterion is proposed in this paper. The data-driven model identification procedure supposes the finding of the mathematical model of the system based on the undamped transient response of the closed-loop system. The system is approximated with the inertial model, where the coefficients are calculated based on the values of the critical transfer coefficient, oscillation amplitude and period of the underdamped response of the closed-loop system. The data driven control design supposes that the tuning parameters of the controller are calculated based on the parameters obtained from the previous step of system identification and there are presented the expressions for the calculation of the tuning parameters. The obtained results of data-driven model identification and algorithm for synthesis the controller were verified by computer simulation.

DATA DRIVEN控制方式图象理解系统的结构性能及改进

本文是以图象理解系统实例分析入手,较详尽地论述了采用DATADRIVEN控制方式的线画解释图象理解系统的硬软件结构,并在评估了系统的可靠性基础上,提出了采用数据驱动和模型驱动双向控制的新观点.

Data-Driven Anomaly Diagnosis for Machining Processes

To achieve zero-defect production during computer numerical control(CNC)machining processes,it is imperative to develop effective diagnosis systems to detect anomalies efficiently.However,due to the dynamic conditions of the machine and tooling during machining processes,the relevant diagnosis systems currently adopted in industries are incompetent.To address this issue,this paper presents a novel data-driven diagnosis system for anomalies.In this system,power data for condition monitoring are continuously collected during dynamic machining processes to support online diagnosis analysis.To facilitate the analysis,preprocessing mechanisms have been designed to de-noise,normalize,and align the monitored data.Important features are extracted from the monitored data and thresholds are defined to identify anomalies.Considering the dynamic conditions of the machine and tooling during machining processes,the thresholds used to identify anomalies can vary.Based on historical data,the values of thresholds are optimized using a fruit fly optimization(FFO)algorithm to achieve more accurate detection.Practical case studies were used to validate the system,thereby demonstrating the potential and effectiveness of the system for industrial applications.

Self-Tuning Control Techniques for Wind Turbine and Hydroelectric Plant Systems

The interest on the use of renewable energy resources is increasing, especially towards wind and hydro powers, which should be efficiently converted into electric energy via suitable technology tools. To this aim, self-tuning control techniques represent viable strategies that can be employed for this purpose, due to the features of these nonlinear dynamic processes working over a wide range of operating conditions, driven by stochastic inputs, excitations and disturbances. Some of the considered methods were already verified on wind turbine systems, and important advantages may thus derive from the appropriate implementation of the same control schemes for hydroelectric plants. This represents the key point of the work, which provides some guidelines on the design and the application of these control strategies to these energy conversion systems. In fact, it seems that investigations related with both wind and hydraulic energies present a reduced number of common aspects, thus leading to little exchange and share of possible common points. This consideration is particularly valid with reference to the more established wind area when compared to hydroelectric systems. In this way, this work recalls the models of wind turbine and hydroelectric system, and investigates the application of different control solutions. Another important point of this investigation regards the analysis of the exploited benchmark models, their control objectives, and the development of the control solutions. The working conditions of these energy conversion systems will also be taken into account in order to highlight the reliability and robustness characteristics of the developed control strategies, especially interesting for remote and relatively inaccessible location of many installations.

基于LSTM-MPC的PEMFC运行状态建模与容错控制

质子交换膜燃料电池(proton exchange membrane fuel cell,PEMFC)具有多物理场耦合特性易产生不同故障且难以控制。为了能在故障状态下快速有效控制,提出基于模型预测控制(modelpredictivecontrol,MPC)的容错控制方案。首先,以长短时记忆神经网络(long short-term memory,LSTM)的预测误差为遗传算法的适应度函数,寻优获取LSTM的最优超参数组合,基于数据驱动构建PEMFC系统在4种不同运行状态下的LSTM预测模型作为预测模型模块。然后,建立基于神经网络的控制器作为优化控制器模块,根据上述模块制定以PEMFC系统阴阳极输入气体压强为控制量、电堆电压为输出量的容错控制方案。最后,仿真验证LSTM预测模型与容错控制方案得到,LSTM预测模型在训练集和测试集的评估指标均方根误差(root mean square error,RMSE)指标值分别为0.0489和0.0558,具有较好的拟合效果。在不同故障状态下,MPC相较于传统PID容错控制方案电压恢复时间缩短50%及以上,并在氢气泄露故障状态下,最大压降降低22.2%,证明了所提控制策略的有效性和正确性。

基于异质数据的复杂网络数据驱动控制

针对复杂网络难以控制,模型难以构建的问题,论文提出基于异质数据的复杂网络数据驱动控制策略。通过对不同时间、任意初始状态、不同最终状态输入输出和状态的异质数据进行重构,建立了基于异质数据集的采样轨迹表示方法,并依据该方法推导出适用于多种复杂网络模型的最小控制能量输入的两种计算方法,对计算方法需要的最少数据量进行了讨论,实现多种复杂网络的数据驱动控制,为复杂网络控制研究提供了新的方法。

基于自联想核回归算法的数据驱动电机控制

在电机控制领域,基于模型驱动的控制方法已经广为研究,随着计算机运算能力的提升,给大量数据计算带来了可能性。为进一步提升电机控制性能,研究了一种基于自联想核回归法算法的控制补偿策略,基于运行过程中的大量数据来校正补偿电机控制过程,以提升电机控制性能,平抑运行过程中的波动。通过理论以及仿真分析,该方法能较好完成电机运行控制,加上补偿后的控制策略能减小电流波动以及转矩脉动,有一定的控制补偿效果。此控制算法同样适用于其他电机控制策略的优化过程,有一定的参考价值。

船舶自抗扰无模型自适应航迹控制

[目的]旨在研究船舶在真实海况下航行面临复杂环境干扰的影响,控制船舶克服干扰,实现航迹跟随、智能航行。[方法]首先,将无模型自适应控制(MFAC)算法引入自抗扰控制器(ADRC)中,设计改进自抗扰无模型自适应控制(ADRC-MFAC)算法,通过ADRC跟踪系统的实时状态、识别系统所受未知扰动,根据MFAC建立输入数据(舵角)与输出数据(航向角、角速度)之间的非线性关系,实现稳定的航向控制。然后,结合自适应视线(LOS)制导策略,通过动态航向控制实现对航迹的精准控制。[结果]仿真结果表明,所设计的控制器能够控制船舶快速航行至预设轨迹并沿轨迹行进,在复杂环境扰动下仍能够实现理想的航迹控制。[结论]研究成果不依赖船舶具体模型,可为船舶航迹控制提供参考。

基于数据驱动的配电网无功优化

传统无功电压控制由于分布式电源、储能以及柔性负荷的接入面临计算速度和精度上的挑战。该文提出了一种基于数据驱动的配电网无功电压优化方法,通过跟踪实际系统的运行参数,实现无功电压的主动控制。在极限学习机中引入自动编码器构建深度学习机制,利用自动编码器建立极限学习机输入-输出的直接耦合关系,实现无监督学习和有监督学习有机结合,缩短训练模型的迭代过程;利用蒙特卡洛法基于分布式电源、负荷预测信息构建配电网运行场景,利用深度极限学习机挖掘运行场景优化运行与无功调压设备状态间的内在联系,建立电网运行场景与系统无功调压策略的映射关系。该文提出的基于数据驱动的无功优化方法不依赖实际系统潮流计算,能够实现配电网运行状态的跟踪和无功调节设备的优化调度,为配电网无功电压的主动控制打下基础。

面向安全高效运行的重载列车智能操控关键技术

面向我国重载铁路自动化和智能化发展以及重载铁路运输需求持续增长对既有列车运行控制带来的严峻挑战,文章围绕重载列车智能操控关键技术展开了研究现状介绍,并分析了目前亟待解决的难题与未来发展趋势。首先,针对列车纵向动力学建模,已有研究多考虑单一环境、固定工况下机理建模及分析,难以精确表征列车运行过程中的内、外部参数时空渐变特性,因此,在已有机理模型基础上考虑未建模动态,并进行降阶等效是未来一个重要研究方向。其次,长编组列车车辆间非线性强耦合特性、空气制动特性对列车运行过程中纵向冲动的影响依旧为重载列车操纵优化带来了巨大的挑战,借鉴高速列车多车协同控制技术,研究考虑主动抑制纵向冲动的重载列车多机车分布式协同控制具有重要意义。最后,结合数据驱动与人工智能算法、多源信息融合等新兴技术分析了列车控制领域一些新的机遇,而耦合仿真平台与评估体系的搭建也将成为重载列车安全运行分析、评估与优化的一种有效途径。

数据驱动的污水处理曝气过程低碳优化控制方法

针对现有的污水处理过程存在碳排放机理不清且难以评估,无法通过有效的调控方式降低碳排放总量的问题,设计了一种数据驱动的污水处理曝气过程低碳优化控制方法。首先,通过深入分析碳排放影响因素及其与水质指标的相互关系,获得了曝气过程各水质指标和碳排放之间的关联关系;其次,采用数据驱动的方法,设计了曝气过程能耗与碳排放的优化模型,以获取曝气过程最优化的控制策略;最后,将获取的曝气过程低碳优化控制方法应用于基准仿真模型。测试结果说明该方法能够有效地跟踪控制曝气过程,降低能耗与碳排放量总量。

紧格式无模型自适应控制在四旋翼飞行器中的应用

为解决难以建立自适应离散变结构控制的受控系统数学模型问题,提出一类改进的基于紧格式离散时间非线性系统的无模型自适应控制(ICF-MFAC)方法。首先,在仅有一项时变积分项的CF-MFAC方法中加入误差反馈输出控制项,使得控制器具有在线整定内部参数的优势,提高系统的动态性能。然后,通过严格的数学推导对ICF-MFAC方法进行收敛性证明,理论分析表明,ICF-MFAC方法提高了伪偏导数估计值的利用率,加快了系统的响应速度。最后,在四旋翼飞行器中进行仿真实验,与CF-MFAC方法相比,改进的控制方法控制误差减小6.12%,且无超调、跟踪误差小,始终保持良好的控制效果和鲁棒性。

高速动车组数据驱动无模型自适应积分滑模预测控制

同许多复杂系统一样,动车组(Electric multiple unit,EMU)运行过程也具有多变量、强耦合以及非线性等特性,这严重影响着列控系统的性能.针对包含外部扰动的动车组自动驾驶系统,提出一种新型的多输入多输出(Multi-input-multi-output,MIMO)数据驱动积分滑模预测控制(Integral sliding mode predictive control,ISMPC)算法.首先,该算法基于与动车组运行过程等效的全格式动态线性化(Full format dynamic linearization,FFDL)数据模型,设计一种离散积分滑模控制(Integral sliding mode control,ISMC)律.为了使系统能够获得更高的输出跟踪误差精度,利用模型预测控制(Model predictive control,MPC)代替ISMC的切换控制,进一步推导出ISMPC算法.同时,通过对FFDL数据模型的未知扰动、参数误差等不确定项进行延时估计,提升了算法的控制性能和对系统的等价描述程度.在提供两种算法的稳定性证明分析之后,以实验室配备的CRH380A型动车组仿真实验台对提出的ISMC和ISMPC算法进行仿真测试,并与其他方法进行对比,仿真结果表明ISMPC算法控制性能较好,动车组各动力单元速度跟踪误差均在±0.132 km/h以内,满足列车的跟踪精度需求;控制力和加速度分别在[-52 kN,42 kN]和±0.9249 m/s2以内且变化平稳.

异构多智能体的数据驱动输出一致性跟踪控制

针对一类异构多智能体编队一致性跟踪控制问题,考虑到实际系统中控制输入和输出均基于离散采样数据,且部分状态信息无法通过传感器获取,提出一种数据驱动的异构多智能体一致性输出跟踪控制方案。该方案利用数字采样控制方法和离散时间输入-输出采样数据,设计了包含无人机和无人车的异构多智能体一致性跟踪控制策略。通过状态观测器,利用降维输出信息实现了对全维状态信息的一致性跟踪控制。采用基于收缩映射原理的稳定性分析,验证了所提出的一致性跟踪控制方案对于每个闭环信号的有界性。同时,通过数值仿真验证了理论分析结果以及无人机和无人车的一致性跟踪控制性能。

数据驱动的结构振动鲁棒控制策略研究

振动控制工程中实际结构模型复杂,难以通过机理建模构建振动控制系统。提出基于数据驱动的子空间辨识和H_(∞)鲁棒控制构建结构振动抑制策略。根据实际被控系统所遭受的外扰以及作动器、传感器的布置情况,首先,确立出被控系统的扰动、控制输入和量测输出等参数;接着,分别对系统施加扰动力和主动作动力并采集此时系统的输入和响应数据;然后,根据采集到的输入输出的数据,运用子空间辨识法得到扰动、输入与输出间的传递函数;最后,通过H_(∞)鲁棒控制理论将得到的传递函数进行组装构建出系统的广义控制对象,并求解出了系统的反馈控制器。并以一多自由度箭载载荷隔振模型为例,建立系统的动力学方程,并通过该系统的仿真数据重构系统的传递函数模型,结果表明通过将重构的广义模型所求解出的反馈控制器代入原系统后,扰动到控制输出间的传递率在谐振峰区间相比原系统得到极大的降低;时域上,在外界扰动下反馈控制系统相比无控系统其稳态响应的幅值大大降低,系统在外扰下的动态指标都得到极大改善。