In this paper, the stabilization is studied for a complex dynamic model which involves nonlinearities, uncertainty, and Levy noises. This paper also discusses the controller discretization and presents a new algorithm to obtain the upper bound for the sample interval through which the exponential stability of the discrete system can still be guaranteed. Firstly, an integral sliding surface is designed to obtain the sliding mode dynamics for the considered stochastic Levy process. By using Lyapunov theory, generalized Ito formula and some inequality techniques, the exponential stability is proved in the sense of mean square for sliding mode dynamics. The reachability of the sliding mode surface is also ensured by designing a sliding mode control law. Secondly, the continuous-time controller is discretized from the point of control cost, and the squared difference is analyzed for the states before and after the discretization. Different from those classical stochastic differential equations driven by Brownian motions, the noise is supposed to be Levy type and the squared difference is analyzed in different cases. Furthermore, we obtain the largest sampling interval through which the discretized controller can still stabilize the Levy process driven stochastic system. Finally, a simulation for a drill bit system is given to demonstrate the results under the algorithms. & COPY; 2023 The Franklin Institute. Published by Elsevier Inc. All rights reserved.
This paper addresses the tracking control problem of intelligent vehicle steering systems with actuator faults and model uncertainty. An adaptive fuzzy logic system is employed to online approximate the unmodeled nonlinear dynamics of the steering system, which is combined with dynamic gain technology to design an adaptive sliding mode controller. This controller effectively compensates for the influences of unknown control gain and actuator faults on control performance. To mitigate the chattering phenomenon inherent in sliding mode control, first-order filtering technology is integrated into the controller design, ensuring smooth system operation. Finally, the designed controller can realize the asymptotic stability of the intelligent vehicle steering system by combining Lyapunov stability theorem analysis. The rationality of the designed control method is verified by numerical simulation and vehicle experiments.
A data-driven model-based microwave heating adaptive dynamic planning temperature tracking algorithm is proposed for the microwave heating process with time-varying nonlinearity and strong coupling. The microwave heating process is modeled using a single hidden-layer feedforward network and the discrimination capability of an extreme learning machine. Based on this, the performance index functions and control laws are approximated using the approximation capability of the single hidden layer feedforward network and the extreme learning machine. Experimental and simulation results show that the proposed algorithm successfully tracks the temperature of high titanium slag during the microwave heating process.
Early fault diagnosis, as an important means to avoid the hidden danger of industrial system, has always been a research hotspot in the field of industry. And the algorithm based on deep learning (DL) with its efficient feature extraction ability has gradually become the mainstream method to solve the problem of fault diagnosis. However, when the fault type is complex and the sample data is insufficient, the existing DL model has the problem of insufficient diagnosis accuracy or the model is too complex to train. Therefore, in order to effectively solve the existing problems, this paper proposes a fault diagnosis method based on support vector machine (SVM) optimized by improved stacked autoencoder (SAE) and differential evolution (DE). Firstly, the back propagation (BP) neural network with a softmax classifier is used to improve SAE, and then the improved SAE is used to extract the features of fault diagnosis data set. The extracted features are input into SVM for fault classification, and the key parameters of SVM are automatically optimized by the DE algorithm. By comparing with the known fault diagnosis methods, the experimental results show that this method can ensure the efficiency of fault diagnosis and improve the diagnosis accuracy when there are too many fault types and few sample data.
In the Internet of Vehicles scenario, the in-vehicle terminal cannot meet the requirements of computing tasks in terms of delay and energy consumption; the introduction of cloud computing and MEC is an effective way to solve the above problem. The in-vehicle terminal requires a high task processing delay, and due to the high delay of cloud computing to upload computing tasks to the cloud, the MEC server has limited computing resources, which will increase the task processing delay when there are more tasks. To solve the above problems, a vehicle computing network based on cloud-edge-end collaborative computing is proposed, in which cloud servers, edge servers, service vehicles, and task vehicles themselves can provide computing services. A model of the cloud-edge-end collaborative computing system for the Internet of Vehicles is constructed, and a computational offloading strategy problem is given. Then, a computational offloading strategy based on the M-TSA algorithm and combined with task prioritization and computational offloading node prediction is proposed. Finally, comparative experiments are conducted under task instances simulating real road vehicle conditions to demonstrate the superiority of our network, where our offloading strategy significantly improves the utility of task offloading and reduces offloading delay and energy consumption.
Since the underground transportation of coal mainly relies on the mine conveyor belt to complete, the mine conveyor belt with large pieces of coal will affect transportation safety. Therefore, to address the problem of real-time monitoring of lump coal, the method Ghost-ECA-Bi FPN (GEB) YOLOv5 for lump coal in the process of mining conveyor belt transportation is proposed based on a lightweight neural network and multisource information fusion. First, the image preprocessing is performed by adaptive histogram equalization, which reduces the influence of coal dust, dust, and uneven lighting on target monitoring. Second, the redundancy of the convolution process is exploited, and a lightweight neural network GhostNet is introduced to optimize the feature extraction process. In addition, combined with the efficient channel attention mechanism, the 1D convolution enables local cross-channel information interaction, which can solve the problem of imbalance between model complexity and performance. Finally, the feature information of the three stages is fused using a weighted bidirectional feature pyramid network to enhance the generalization ability of the model. The experimental results show that the improved GEB YOLOv5 algorithm has obvious advantages. In terms of model structure, the number of network layers reduces by 36.97%, and the number of model structure parameters and floating-point operations reduce by 64.53% and 69.14%, respectively. Moreover, the model volume reduces from 92.7 M to 33.0 M. Regarding the monitoring performance, the precision and recall rates improve by 1.19% and 1.11%, respectively. Furthermore, the real-time performance improves from 68.34 FPS to 110.70 FPS. It can be seen that the problem of the model performance against the model complexity is effectively solved in this experiment and the real-time monitoring of lump coal is realized.
Aiming at the control difficulties of variable air volume (VAV) air conditioning system, such as large lag and multivariable coupling, a parameter tuning method of multivariable VAV air conditioning system generalized predictive controller (GPC) based on population attenuation beetle swarm optimization (BSO) algorithm is proposed. Firstly, the weight parameters and learning factors of BSO are dynamically selected, and the population attenuation function and event trigger (ET) conditions are reasonably selected to obtain better optimization performance. Secondly, a novel GPC parameter tuning method based on population attenuation BSO algorithm is proposed, which can improve the time-domain performance of the system. Finally, simulation and hardware in the loop experiments are carried out. The results show that the method which proposed by this paper is effective.
针对现代控制理论课程传统教学模式中课堂教学形式局限、教学效果评价单一、与实际工程需求脱节等问题,基于成果导向教育模式的特点,结合交叉学科特点,进行现代控制理论课程教学改革方案的探索.以学生为中心,以研究成果为导向,结合每个学生的需求,从教学目标、改革内容、改革方法以及实施方案等方面进行研究和改革.通过善用示范例程、课堂实时评价与反馈以及建设性介入等策略,来引导、协助学生达成预期成果,并能有效提高现代控制理论课程的教学质量.实践反馈表明,这种成果导向教育模式对于多学科工程背景下的现代控制理论教学探索具有理论以及应用价值.
针对机械臂逆解求取过程中存在大量矩阵变换、计算成本高的问题,采用位姿分离法对逆运动学求解过程进行改进,并提出基于自适应步长的 RRT-connect 路径规划算法.首先建立六自由度机械臂连杆坐标系模型,采用Standard Denavit-Hartenberg(D-H)方法对机械臂进行正运动学分析,得到机械臂末端执行器位姿相对于基座的齐次变换矩阵.然后引入位姿分离法改进了机械臂的逆运动学求解方法,将机械臂运动学逆解分为位置逆解和姿态逆解两部分,分别用几何法和解析法进行求解,减少了整体计算量.再者提出基于自适应步长的改进RRT-connect路径规划算法,解决了扩展速度慢的问题.最后通过仿真验证所提出方法的正确性和有效性.
冷却剂丧失事故(loss of coolant accident,LOCA)试验是模拟核电设备在运行中突然出现冷却剂丧失而导致仓内温度骤然升高的场景,检验核电站用电缆、传感器等设备性能的一种标准化测试流程.为解决流入LOCA试验仓内高温蒸汽的温度控制具有非线性、迟滞大以及时变性的技术难题,使用 Fluent 软件模拟 LOCA试验仓中的温度变化和蒸汽流场,并用MATLAB仿真对比模糊专家PID与经典PID的差异.以设计基准事故(design basis accident,DBA)鉴定曲线CPR1000 和AP1000 为标准,在现场进行了瞬态热冲击试验、喷淋降温试验和温度平衡试验.通过仿真和现场试验结果表明,采用模糊专家PID联合控制的温控系统比经典PID控制响应速度更快、鲁棒性更高、超调量更小,误差控制在±5℃以内,符合DBA曲线的温控要求,能够满足LOCA温控系统的自动化测试需要.
对带三维装载约束的多车场车辆路径问题,以最小化车辆行驶总里程为优化目标,建立问题模型,并提出一种三阶段优化算法进行求解.第一阶段设计带循环平衡的K-medoids聚类算法,将原问题分解成多个带三维装载约束限制的车辆路径子问题.第二阶段提出一种双层结构的超启发式蚁群算法用于求解各子问题,以确定各车辆的配送路径.在该算法中,低层设计9种启发式操作,并将其所构成的排列作为高层个体;同时,高层采用蚁群算法更新高层个体,以引导算法搜索方向.第三阶段以第二阶段所得阶段解作为初始解,设计组合启发式装箱算法对带容积约束的装箱过程进行优化,进而将第二、三阶段确定的解合并为原问题的解.最后,仿真实验和算法比较验证了所提算法的有效性.
鉴于传统自适应阈值分割方法在检测中无法根据镜框粗细、颜色深浅以及拍摄背景复杂程度自适应双阈值门限.现采用深度学习中的 CenterNet 模型,通过 MobileNetV3中的bneck结构堆叠,组成CenterNet模型的主干特征提取部分,从用户图片中获得镜框和眼睛的类别信息、位置信息,最后通过相对位置关系计算出测量要求的相应尺寸.该过程无需对图像进行阈值分割,实现了非结构化场景中镜框尺寸的快速测量.实验结果表明,该方法不受镜框粗细以及拍摄背景的影响,检测精度高达 98.01%,每秒处理图片的数量(frames per second,FPS)达到 15 帧/秒,训练后的权重文件从 152 MB减小到 47 MB,并有较强的泛化能力.
大范围变工况的燃煤机组脱硝系统因具有大惯性特性、强非线性以及多源扰动等特点,使其控制具有一定的挑战性.为提高脱硝系统的跟踪和抗干扰控制性能,并节约喷氨量,提出了基于控制性能指标和喷氨量指标的多目标优化串级改进自抗扰控制策略.在改进自抗扰控制原理的基础上,通过单一变量法分析改进自抗扰控制参数对控制效果的影响.采用多目标遗传算法优化脱硝系统改进自抗扰控制参数.通过仿真验证所提改进自抗扰控制在设定值跟踪、扰动抑制方面的优势,蒙特卡洛实验验证了所提改进自抗扰控制在应对系统不确定性时具有很强的鲁棒性.仿真结果表明所提改进自抗扰控制具有很好的实际应用潜力.
高速公路交通量预测中原始数据存在大量缺失值,为了挖掘高速公路交通量时间序列中的更多信息,提高交通量预测的精度,构建了缺失值修复方法、Dropout 以及长短时记忆网络(long short term memory,LSTM)相结合的高速公路流量混合预测模型.通过缺失值修复方法对高速公路流量数据进行数据修复;在 LSTM 网络中非循环的部分加入Dropout 机制来减少过拟合情况;通过实测交通量数据进行实验,实验结果表明考虑缺失值修复的 Dropout-LSTM 的高速公路流量预测模型相较于 LSTM 及常用高速公路预测模型,预测精度更高,验证了该模型在短时高速公路交通量预测中的有效性.
针对具有持续有界扰动的线性变参数系统,设计一种基于 Tube 不变集的鲁棒模型预测控制算法.离线算法结合系统多胞体模型参数变化的影响,构建系统的 Tube 不变集.在对应标称模型状态变量的多面体不变集算法基础上,得到系统的多面体状态允许不变集序列.在线算法通过强控制优化得到标称模型系统的控制量,以得到符合实际控制过程的系统控制量,给出本算法的详细步骤和系统稳定性证明.仿真结果验证了本算法的有效性,表明本算法将持续有界扰动对系统的影响限制在 Tube 不变集中,实现了系统的快速稳定控制.
为解决普通深度学习方法提取特征能力较差和传统软测量模型都是单输出模型的问题,提出了基于多重注意力卷积门控循环单元(multiple attention-based convolutional gated recurrent unit,MA-CGRU)的燃煤电厂污染气体浓度并行软测量模型.首先使用 T 分布随机近邻嵌入对原始数据做非线性降维,接着采用一维卷积层提取数据的特征,然后将特征送入门控循环单元层.同时,采用多重注意力机制来提升并行软测量模型的特征提取效率.此外,所提出的模型可以并行输出NOx和SO2两种污染气体在下一时刻的浓度,有着较高的准确性,并且优于其他对比模型,可以为实际工业现场其他参数的并行软测量提供一定的参考.
永磁同步电机的负载转矩、粘滞摩擦系数和转动惯量等机械参数的在线辨识,对实现高性能伺服控制具有重要的意义.为了减小误差、提高辨识精度,充分考虑不同机械参数间的相互关联与影响,将扩展滑模观测器、模型参考自适应系统以及所设计的新型自适应系统相结合,并构成一种互联观测网络.然后,基于此网络,对永磁同步电机的3个机械参数进行实时的在线精确辨识.相比于传统辨识方法,所提出的辨识方法可以有效抑制滑模抖振,提高系统的动态响应能力和鲁棒性.
考虑到传统模型的约束条件过于理想化,而且实际电-气综合能源系统中互联耦合关系较复杂,以可再生能源利用量最大为目标函数,建立两种网络在新型约束条件下的潮流计算模型.首先,建立天然气管网、配电网以及涵盖电转气装置的耦合设备的数学模型;然后,在传统约束条件的基础上,考虑互联模型耦合关系的加深,引入新型约束条件,以可再生能源利用量最大为目标函数,对模型进行混合网络的潮流计算;最后,对不同节点数量构成的3种电-气综合能源系统算例,用MATLAB进行仿真验证.仿真结果表明,引入电转气技术的电-气综合能源系统能有效提高能源的利用效率.电-气综合能源系统考虑了耦合关系加深的约束条件更符合实际情况,为系统的建模及优化提供了理论指导.
为了增强永磁同步电机调速系统电流预测控制的鲁棒性,提高调速性能,提出了一种永磁同步电机非线性电流预测鲁棒控制策略.首先,将含扰动误差项的永磁同步电机非线性数学模型利用前向欧拉公式转换为离散数学模型,在此基础上利用复合积分终端滑模观测器估计非线性模型的未知扰动项,并通过最优控制理论完成预测电流最优控制作用转化,构建模型预测电流控制环路;再结合预估器及自适应控制思想,对环路输入输出辨识校正,提高延迟补偿的精确性,从而达到增强系统鲁棒性和快速跟踪性的目的;最后,通过仿真与半实物实验验证了控制策略的有效性.
为保证出水水质,降低运行成本,污水处理过程的优化需要动态更新污水处理过程操作变量的最优设定值.因此,提出使用进化算法对溶解氧的设定值进行优化,并结合案例推理(case-based reasoning,CBR),提出一种污水处理的曝气过程智能控制方法.首先,建立入水数据与出水指标的神经网络模型,针对不同工况,使用优化算法获取操作变量的优化设定值,建立动态案例库,使用最近相邻法于案例匹配过程中,将案例重用后取得操作变量的优化设定值应用于基准仿真模型 1 号(benchmark simulation model No.1,BSM1)中,并得到性能评价指标.根据性能评价指标,更新操作变量的优化设定值和神经网络模型.使用BSM1 对优化系统进行仿真,优化系统较原系统曝气能耗减少了18.5%,同时出水水质(effluent quality,EQ)指标得到了改善.
