Control Engineering Practice最新文献_第6页

Fuzzy adaptive impedance control for the two-layered vertical cable-driven parallel robot 双层垂直缆索驱动并联机器人的模糊自适应阻抗控制

IF 5.4 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2024-10-01 DOI: 10.1016/j.conengprac.2024.106110

Thanh-Hai Nguyen , Kwan-Woong Gwak

This study unveils a novel two-layered vertical octahedron cable-driven parallel robot (TLVO CDPR), distinctively engineered for effective force interactions with vertical surfaces while preventing collision with cables. It pioneers an innovative control strategy integrating a position-based fuzzy adaptive impedance controller with a fuzzy Proportional – Integral – Derivative (PID) controller, adeptly managing both the pose and contact force of the robot. While dual control application is often found in rigid-link robots, it remains a largely unexplored frontier in the realm of CDPRs, despite its critical importance in sectors like manufacturing and assembly. The fuzzy adaptive mechanism significantly boosts impedance control efficacy in the face of unpredictable, non-uniform working surfaces, ensuring algorithmic stability and convergence. Concurrently, fuzzy logic is harnessed to optimize PID controller performance. The forward kinematics challenge is efficiently tackled using a least squares method coupled with an Inertial Measurement Unit (IMU), ensuring swift and precise solutions. The robustness and adaptability of the robot and its control systems are thoroughly validated through extensive experimental trials, involving diverse trajectories and varying uncertainties on vertical working surfaces.

本研究揭示了一种新型双层垂直八面体缆索驱动并联机器人（TLVO CDPR），其独特的设计可与垂直表面进行有效的力交互，同时防止与缆索发生碰撞。它首创了一种创新控制策略，将基于位置的模糊自适应阻抗控制器与模糊比例-积分-微分（PID）控制器集成在一起，对机器人的姿势和接触力进行了有效管理。虽然双控制应用通常出现在刚性连杆机器人中，但在 CDPR 领域，尽管它在制造和装配等行业中至关重要，但在很大程度上仍是一个尚未开发的前沿领域。面对不可预测、不均匀的工作表面，模糊自适应机制大大提高了阻抗控制的功效，确保了算法的稳定性和收敛性。同时，还利用模糊逻辑优化了 PID 控制器的性能。采用最小二乘法与惯性测量单元（IMU）相结合，有效地解决了前向运动学难题，确保了快速、精确的解决方案。机器人及其控制系统的鲁棒性和适应性通过广泛的实验测试得到了充分验证，包括垂直工作表面上的各种轨迹和不同的不确定性。

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引用次数: 0

Selection of alarm deadbands and delay timers with their connections based on risk indicators for removing nuisance alarms 根据风险指标选择警报死区和延迟计时器及其连接，以消除骚扰警报

IF 5.4 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2024-10-01 DOI: 10.1016/j.conengprac.2024.106113

Zheng Zhang, Jiandong Wang, Yan Qi

Alarm systems are crucial to the safety and efficiency of industrial processes, but they may be contaminated by massive nuisance alarms. Alarm deadbands and delay timers with their connections are often used to remove nuisance alarms. However, different process variables are with different characteristics of alarm events, so that it is necessary to determine which one of these alarm systems is the most appropriate for a given process variable. This paper proposes a method to select the most suitable alarm system for a given process variable, by formulating an indicator to evaluate the risk of missed abnormality detections. The technical challenge is about how to calculate the uncertainty of the risk indicator. The Bayesian estimation approach is utilized to yield confidence intervals of the risk indicator for addressing the technical challenge. The alarm system with the lowest risk indicator is chosen as the most appropriate one. Numerical and industrial examples are presented to support the proposed method.

报警系统对工业流程的安全和效率至关重要，但可能会受到大量骚扰报警的污染。报警死区和延迟定时器及其连接通常用于消除骚扰报警。然而，不同的过程变量具有不同的报警事件特征，因此有必要确定这些报警系统中哪一个最适合给定的过程变量。本文提出了一种为给定过程变量选择最合适报警系统的方法，通过制定一个指标来评估异常检测遗漏的风险。技术难题在于如何计算风险指标的不确定性。为解决这一技术难题，我们采用了贝叶斯估计方法来得出风险指标的置信区间。风险指标最低的报警系统被选为最合适的报警系统。为支持所提出的方法，介绍了数值和工业实例。

引用次数: 0

Data-driven adaptive and stable feature selection method for large-scale industrial systems 大规模工业系统的数据驱动自适应稳定特征选择方法

IF 5.4 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2024-09-27 DOI: 10.1016/j.conengprac.2024.106097

Xiuli Zhu , Yan Song , Peng Wang , Ling Li , Zixuan Fu

Data-driven modeling is a crucial technology for the real-time monitoring of large-scale industrial systems. However, it often suffers from the redundancy of input variables, resulting in low prediction and modeling accuracy. To address this issue, a novel feature selection method, namely adaptive and stable feature selection based on a reference vector-guided evolutionary multi-objective optimization algorithm (ASFS-RVEA), is proposed in this paper. The proposed ASFS-RVEA comprehensively considers four important objectives: the number of features, prediction accuracy, the dissimilarity of selected features, and the mitigation of feature redundancy.Considering the interaction and conflict among these four objectives, a multi-objective optimization problem with an unknown Pareto front is formulated to find an optimal balance among them, thereby obtaining promising and convincing results. Furthermore, Jensen–shannon divergence (JSD) is introduced to the RreliefF algorithm to account for the data distribution information between various input features and key output variables, guiding population crossover and mutation. This greatly enhances the robustness of the algorithm when handling data with different distributions. Next, a reference vector adapting strategy is proposed to update the generation based on dynamically changing distributions, which helps accelerate convergence in the optimization process. Finally, experiments conducted on datasets collected from the Dow process and the polyester polymerization process demonstrate the effectiveness of the proposed ASFS-RVEA.

数据驱动建模是实时监控大规模工业系统的一项重要技术。然而，它往往受到输入变量冗余的影响，导致预测和建模精度较低。针对这一问题，本文提出了一种新颖的特征选择方法，即基于参考向量引导的进化多目标优化算法（ASFS-RVEA）的自适应稳定特征选择。考虑到这四个目标之间的相互作用和冲突，本文提出了一个具有未知帕累托前沿的多目标优化问题，以寻求它们之间的最佳平衡，从而获得了令人信服的结果。此外，在 RreliefF 算法中引入了 Jensen-shannon divergence (JSD)，以考虑各种输入特征和关键输出变量之间的数据分布信息，指导种群交叉和突变。这大大增强了算法在处理不同分布数据时的鲁棒性。接下来，我们提出了一种参考向量自适应策略，根据动态变化的分布更新生成量，这有助于加快优化过程的收敛速度。最后，对从陶氏化学过程和聚酯聚合过程中收集的数据集进行的实验证明了所提出的 ASFS-RVEA 的有效性。

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引用次数: 0

Design of an automated street crossing management module for a delivery robot 为送货机器人设计自动过街管理模块

IF 5.4 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2024-09-25 DOI: 10.1016/j.conengprac.2024.106095

Riccardo Pieroni, Matteo Corno, Filippo Parravicini, Sergio M. Savaresi

Autonomous navigation of mobile robots in urban environments is a complex problem, that can be decomposed in several tasks. Among them, autonomous street crossing is particularly difficult because it requires the robot to estimate the position and speed of surrounding vehicles and to decide which is the best action to perform based on such information. This paper develops the entire pipeline that implements autonomous street crossing; the approach is composed of an extended target tracking algorithm that estimates the position and velocity of obstacles and a crossing algorithm that determines the best crossing strategy to negotiate an unregulated intersection (i.e. without traffic lights) based on the other vehicles’ behavior. The method is first validated in an ad hoc simulation environment, and then experimentally tested using a prototype parcel delivery robot operating in a real urban environment. The results show that the robot is capable of tracking incoming vehicles and managing the crossing with good performance, in terms of the time taken to cross the road and of actions performed by the robot during the interaction with vehicles.

移动机器人在城市环境中的自主导航是一个复杂的问题，可以分解为多项任务。其中，自主过街尤其困难，因为它要求机器人估计周围车辆的位置和速度，并根据这些信息决定执行哪项行动最好。本文开发了实现自主过街的整个流水线；该方法由一个扩展的目标跟踪算法和一个过街算法组成，前者可估算障碍物的位置和速度，后者可根据其他车辆的行为确定最佳过街策略，以便在不受管制的交叉路口（即没有交通信号灯的路口）进行交涉。该方法首先在临时模拟环境中进行了验证，然后使用在真实城市环境中运行的包裹递送机器人原型进行了实验测试。结果表明，从过马路所需的时间以及机器人在与车辆互动过程中执行的操作来看，机器人能够跟踪来往车辆，并以良好的性能管理过马路。

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引用次数: 0

A multi-objective hierarchical deep reinforcement learning algorithm for connected and automated HEVs energy management 用于互联和自动混合动力汽车能源管理的多目标分层深度强化学习算法

IF 5.4 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2024-09-25 DOI: 10.1016/j.conengprac.2024.106104

Serdar Coskun , Ozan Yazar , Fengqi Zhang , Lin Li , Cong Huang , Hamid Reza Karimi

Connected and autonomous vehicles have offered unprecedented opportunities to improve fuel economy and reduce emissions of hybrid electric vehicle (HEV) in vehicular platoons. In this context, a hierarchical control strategy is put forward for connected HEVs. Firstly, we consider a deep deterministic policy gradient (DDPG) algorithm to compute the optimized vehicle speed using a trained optimal policy via vehicle-to-vehicle communication in the upper level. A multi-objective reward function is introduced, integrating vehicle fuel consumption, battery state-of-the-charge, emissions, and vehicle car-following objectives. Secondly, an adaptive equivalent consumption minimization strategy is devised to implement vehicle-level torque allocation in the platoon. Two drive cycles, HWFET and human-in-the-loop simulator driving cycles are utilized for realistic testing of the considered platoon energy management. It is shown that DDPG runs the engine more efficiently than the widely-implemented Q-learning and deep Q-network, thus showing enhanced fuel savings. Further, the contribution of this paper is to speed up the higher-level vehicular control application of deep learning algorithms in the connected and automated HEV platoon energy management applications.

互联和自动驾驶汽车为提高混合动力电动汽车（HEV）的燃油经济性和减少排放提供了前所未有的机遇。在此背景下，我们提出了一种针对联网 HEV 的分层控制策略。首先，我们考虑采用深度确定性策略梯度（DDPG）算法，通过上层的车对车通信，使用训练有素的最优策略计算优化车速。引入了多目标奖励函数，综合了车辆油耗、电池充电状态、排放和车辆跟车目标。其次，设计了一种自适应等效消耗最小化策略，以实现排中的车辆级扭矩分配。利用两种驾驶循环，即 HWFET 和人类在环模拟器驾驶循环，对所考虑的车队能源管理进行了实际测试。结果表明，DDPG 比广泛实施的 Q-learning 和深度 Q 网络更有效地运行发动机，从而提高了节油效果。此外，本文的贡献还在于加速了深度学习算法在互联和自动混合动力汽车车队能源管理应用中更高层次的车辆控制应用。

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引用次数: 0

Station-keeping strategy for wave gliders considering obstacle area 考虑到障碍物面积的波浪滑翔机站位保持策略

IF 5.4 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2024-09-24 DOI: 10.1016/j.conengprac.2024.106093

Peiyuan Yu , Ying Zhou , Xiujun Sun , Hongqiang Sang , Shuai Zhang

An obstacle mode station-keeping strategy that considers obstacles in the station-keeping center area is proposed for wave gliders (WGs) to cope with special applications such as oil spill monitoring on drilling platforms and observation around the island. Different from the traditional station-keeping strategy which requires closing in the preset position as much as possible, this strategy uses the adaptive integral line of sight (AILOS) algorithm to make the WG sail around the preset obstacle area. A partitioning control strategy based on distance error is introduced to divide three areas according to the risk level: warning area, escape area and obstacle area. A tan-type barrier Lyapunov function (BLF) is introduced into the warning area control method to determine the boundary. To avoid the potential risk of collision, the escape area control strategy is to make the WG move away from the obstacle area as quickly as possible. Simulation and sea trial results verified the capability of the proposed station-keeping strategy in a stable ocean environment and the station-keeping safety of the WG using this strategy under extreme situations.

为波浪滑翔机（WGs）提出了一种障碍物模式定点策略，该策略考虑了定点中心区域的障碍物，以应对钻井平台溢油监测和环岛观测等特殊应用。传统的定点保持策略要求尽可能地靠近预设位置，与之不同的是，该策略采用自适应整体视线（AILOS）算法，使波浪滑翔机绕着预设障碍物区域航行。引入基于距离误差的分区控制策略，根据风险等级划分三个区域：警告区、逃逸区和障碍区。警告区控制方法中引入了 tan 型障碍物 Lyapunov 函数（BLF）来确定边界。为避免潜在的碰撞风险，逃逸区控制策略是使 WG 尽快远离障碍区。仿真和海试结果验证了所提出的驻留策略在稳定海洋环境中的能力，以及在极端情况下使用该策略的 WG 的驻留安全性。

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引用次数: 0

Finite frequency domain H∞ hybrid control design of drag-free spacecraft with model-based generalized extended state observer 利用基于模型的广义扩展状态观测器进行无阻力航天器的有限频域 H∞ 混合控制设计

IF 5.4 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2024-09-24 DOI: 10.1016/j.conengprac.2024.106096

Qianjiao Xu , Bing Cui , Pengcheng Wang , Yuanqing Xia , Yonghe Zhang

For the drag-free spacecraft on the space-borne gravitational wave detection mission, the drag-free control scheme is considered one of the core technologies to achieve the ultra-quiet-stable control requirements in the measurement bandwidth (MBW). This high-precision control performance is constrained by actuation noises, measurement noises, environmental disturbances, and the limited control bandwidth. In order to address these difficulties, a finite frequency domain double closed-loop control (DCC) framework with the parameter design method is proposed in this paper. First, a model-based generalized extended state observer (MGESO) framework is proposed. This framework integrates plant estimation and disturbance estimation components to accurately estimate those disturbances and noises with lower orders. Then, based on the MGESO framework, the DCC framework is proposed for drag-free control. Within the control structure, the performance specifications can be directly divided into the inner and outer loop performances, which reduces the complexity of the parameter tuning. Subsequently, a finite frequency domain parameter tuning method for the DCC framework is provided, leveraging the generalized Kalman-Yakubovich-Popov (GKYP) lemma. The introduction of the sensitive frequency domain as a design constraint can result in a reduction of control expenditures. Finally, the effectiveness and superiority of the DCC structure are verified in the drag-free spacecraft hardware-in-loop experiment platform.

对于执行天基引力波探测任务的无阻力航天器来说，无阻力控制方案被认为是实现测量带宽（MBW）内超静音稳定控制要求的核心技术之一。这种高精度控制性能受到执行噪声、测量噪声、环境干扰和有限控制带宽的限制。为了解决这些难题，本文提出了一种有限频域双闭环控制（DCC）框架和参数设计方法。首先，本文提出了基于模型的广义扩展状态观测器（MGESO）框架。该框架集成了工厂估计和干扰估计组件，可精确估计低阶干扰和噪声。然后，基于 MGESO 框架，提出了用于无阻力控制的 DCC 框架。在控制结构中，性能指标可直接分为内环和外环性能，从而降低了参数调整的复杂性。随后，利用广义卡尔曼-雅库博维奇-波波夫（GKYP）定理，为 DCC 框架提供了一种有限频域参数调整方法。引入敏感频域作为设计约束，可以减少控制支出。最后，在无阻力航天器硬件在环实验平台上验证了 DCC 结构的有效性和优越性。

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引用次数: 0

Signal-Interpreted Coloured Petri Nets: A modelling tool for rapid prototyping in feedback-based control of discrete event systems 信号解释彩色 Petri 网：离散事件系统基于反馈控制的快速原型建模工具

IF 5.4 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2024-09-23 DOI: 10.1016/j.conengprac.2024.106099

Matheus Ungaretti Borges , Alessandro Pilloni , Gustavo Ribeiro Pontes , Carla Seatzu , Eduardo José Lima II

Petri nets (PNs) are typically used for design and verification rather than direct control implementation. In this paper, aligning with the Industry 4.0 paradigm’s focus on flexible and reconfigurable control systems, we propose a modelling tool for rapidly prototyping feedback-based discrete-event control algorithms on programmable controllers such as PLCs or microcontroller boards. This modelling tool, named Signal Interpreted Coloured Petri Nets (SICPNs), aims to combine the formal modelling expressiveness of Coloured PNs with the capabilities of Signal Interpreted PNs, which are specialised in processing plant measurements and determining actuator commands. This contribution involves: (a) the formal definition of SICPN; (b) the presentation in the IEC61131-3 compliant SCL language of the so-called Token Player, a software entity designed to support feedback-based decision-making within the SICPN; (c) the validation of the effectiveness of the proposed formalism in controlling an extended configuration of the FESTO Modular Processing Station (MPS) using an Arduino microcontroller via two-way UART serial communications; (d) the modelling of a Digital Twin of the FESTO MPS testbed. The tests demonstrate that, during transitions, the colour and signal interpretation conditions enable the microcontroller to accurately schedule and dynamically reconfigure control actions while keeping the size of the PN-based controller small relative to the control problem’s complexity.

Petri 网 (PN) 通常用于设计和验证，而不是直接实现控制。本文结合工业 4.0 范式对灵活和可重构控制系统的关注，提出了一种建模工具，用于在 PLC 或微控制器板等可编程控制器上快速原型化基于反馈的离散事件控制算法。该建模工具被命名为信号解释彩色 Petri 网（SICPNs），旨在将彩色 PNs 的形式建模表达能力与信号解释 PNs 的功能相结合，后者专门用于处理工厂测量和确定执行器指令。这一贡献包括：(a) SICPN 的形式定义；(b) 用符合 IEC61131-3 标准的 SCL 语言介绍所谓的令牌播放器，这是一个软件实体，旨在支持 SICPN 内基于反馈的决策；(c) 验证所提议的形式主义在使用 Arduino 微控制器通过双向 UART 串行通信控制 FESTO 模块化处理站（MPS）的扩展配置方面的有效性；(d) FESTO MPS 试验台的数字孪生体建模。测试表明，在过渡期间，颜色和信号解释条件使微控制器能够准确地安排和动态地重新配置控制操作，同时保持基于 PN 的控制器的体积相对于控制问题的复杂性较小。

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引用次数: 0

Output consensus for interconnected heterogeneous systems via a combined model predictive control and integral sliding mode control with application to CSTRs 通过模型预测控制和积分滑模控制相结合的方法实现互联异构系统的输出共识，并将其应用于中央反应器

IF 5.4 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2024-09-20 DOI: 10.1016/j.conengprac.2024.106100

Ye Zhang , Fei Li , Shouli Gao , Dongya Zhao , Xing-Gang Yan , Sarah K. Spurgeon

Interconnected structures are commonly found in process networks. In this paper, an output consensus framework is proposed for a class of continuous interconnected linear heterogeneous systems subject to disturbances and constraints. The distributed output consensus control strategy is developed by combining integral sliding mode control with model predictive control. The integral sliding mode control is designed to eliminate a class of matched disturbances. The model predictive control plays two main roles: On the one hand, it drives the system states to track the steady state values so as to achieve output consensus; on the other hand, it helps to deal with interconnections and constraints existing in systems. In the meantime, a distributed iterative algorithm is designed to acquire the system steady states. A simulation example and an experiment relating to control of systems of interconnected CSTRs are presented to validate the effectiveness and superiority of the proposed method.

流程网络中通常存在互连结构。本文针对一类受干扰和约束的连续互联线性异构系统提出了一个输出共识框架。分布式输出共识控制策略是通过将积分滑模控制与模型预测控制相结合而开发的。积分滑模控制旨在消除一类匹配干扰。模型预测控制起两个主要作用：一方面，它驱动系统状态跟踪稳态值，以实现输出共识；另一方面，它有助于处理系统中存在的互连和约束。同时，设计了一种分布式迭代算法来获取系统稳态。为了验证所提方法的有效性和优越性，介绍了一个仿真实例和一个与相互连接的中央反应器系统控制有关的实验。

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引用次数: 0

HFTL-KD: A new heterogeneous federated transfer learning approach for degradation trajectory prediction in large-scale decentralized systems HFTL-KD：用于大规模分散系统退化轨迹预测的新型异构联合迁移学习方法

IF 5.4 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2024-09-19 DOI: 10.1016/j.conengprac.2024.106098

Shixiang Lu, Zhi-Wei Gao, Yuanhong Liu

Restrictions arising from the limited training data and privacy preservation make large-scale lithium-ion battery degradation trajectory prediction challenging. In this study, a novel heterogeneous federated transfer learning with knowledge distillation approach is proposed for lithium-ion battery lifetime prediction with scarce training data and privacy concerns. The approach enables each device in large-scale decentralized system to not only own its private data, but also a unique network designed based on its resource constraints. Specifically, the central server first designs its unique network according to the resource constraints of each device, and trains the network on publicly available data with entire degradation cycles, thus avoiding the high cost of collecting abundant degradation cycles. Then, the trained model is transferred to each device for collaborative training, in which the knowledge of heterogeneous models extracted by knowledge distillation is used for communication between the isolated devices, rather than the parameters in conventional federated learning. Extensive real-world datasets are leveraged to verify the effectiveness of the proposed approach. The comparison results demonstrate that the proposed method outperforms seven benchmarks. An ablation study indicates that the approach can achieve satisfactory battery residual life prediction while preserving privacy.

有限的训练数据和隐私保护所带来的限制使得大规模锂离子电池退化轨迹预测具有挑战性。本研究针对训练数据稀缺和隐私保护问题，提出了一种新颖的异构联合迁移学习与知识提炼方法，用于锂离子电池寿命预测。该方法使大规模分散系统中的每个设备不仅拥有自己的隐私数据，还能根据其资源限制设计出独特的网络。具体来说，中央服务器首先根据每个设备的资源限制设计其独特的网络，并在公开的具有完整降解周期的数据上训练该网络，从而避免了收集大量降解周期的高成本。然后，将训练好的模型传输到每个设备上进行协作训练，在协作训练中，通过知识提炼提取的异构模型知识被用于孤立设备之间的通信，而不是传统联合学习中的参数。我们利用广泛的真实数据集来验证所提方法的有效性。比较结果表明，所提出的方法优于七个基准。一项消融研究表明，该方法可以在保护隐私的同时实现令人满意的电池剩余寿命预测。

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引用次数: 0