Control Engineering Practice最新文献_第7页

From individual to group: Double-layer optimization for industrial equipment scheduling via multi-mechanism-driven intelligence 从个体到群体：基于多机制驱动智能的工业设备调度双层优化

IF 4.6 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2026-01-04 DOI: 10.1016/j.conengprac.2025.106743

Lin Guan , Yalin Wang , Bin Lin , Xujie Tan , Yongfei Xue , Chenliang Liu

Reasonable scheduling of industrial equipment is essential for enhancing enterprise production efficiency. However, as the scale and complexity of equipment increase, scheduling optimization becomes more challenging. Traditional manual operations and scheduling methods often fail to comprehensively perceive equipment status or accurately prioritize tasks, leading to delays, resource conflicts, and diminished overall operational efficiency. To address these issues, this paper proposes a novel double-layer scheduling optimization method for the intelligent coordination of industrial equipment. At the upper layer, a multi-mechanism-driven whale optimization algorithm (MMD-WOA) is designed to enable precise and efficient task allocation, incorporating dynamic inertia weight coefficient, Lévy mutation mechanism and boundary solution repair mechanism. At the lower layer, equipment path planning is optimized through the incorporation of a dynamic priority adjustment strategy and an adaptive time-interval mechanism, thereby improving the maneuverability and responsiveness of industrial systems. Finally, the effectiveness of the proposed method is validated through extensive experiments on 23 benchmark functions and three real-world multi-crane scheduling cases of different operational scales. In the real-world industrial case, the proposed method achieves the best makespan among all comparison algorithms and further reduces the passive movement distance by 6.01% and the total computational time by 67.95% compared with the traditional passive avoidance strategy.

工业设备的合理调度是提高企业生产效率的关键。然而，随着设备规模和复杂性的增加，调度优化变得更加具有挑战性。传统的人工操作和调度方法往往不能全面地感知设备状态或准确地确定任务的优先级，从而导致延迟和资源冲突，降低整体运营效率。针对这些问题，本文提出了一种新的工业设备智能协调的双层调度优化方法。在上层，设计了一种多机制驱动的鲸鱼优化算法（MMD-WOA），结合动态惯性权重系数、lsamvy突变机制和边界解修复机制，实现了任务的精确高效分配。在下层，通过引入动态优先级调整策略和自适应时间间隔机制来优化设备路径规划，从而提高工业系统的可操作性和响应性。最后，通过23个基准函数和3个不同操作规模的实际多起重机调度案例的大量实验，验证了所提方法的有效性。在实际工业案例中，该方法在所有比较算法中获得了最佳的makespan，与传统的被动回避策略相比，该方法进一步减少了6.01%的被动移动距离和67.95%的总计算时间。

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引用次数: 0

An interpretability and accuracy trade-off method in belief rule base for safety assessment 基于信念规则库的安全评价可解释性与准确性权衡方法

IF 4.6 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2026-01-03 DOI: 10.1016/j.conengprac.2025.106741

Zongjun Zhang , Zhiying Fan , Wei He, Shuwen Wang, Yutong Xia, Wenkuan Wu

Safety assessment plays a vital role in complex systems. Incomplete data, ambiguous information, and tightly coupled factors often cause severe challenges in the assessment process. Belief rule base (BRB) is used to combine expert knowledge and data-driven inference. It can handle uncertainty and fuzziness effectively. BRB has become an important tool for safety assessment in complex systems. Existing BRB methods improve accuracy at the cost of model simplicity and transparency. The assessment results are difficult for engineers to understand and trust. This limitation reduces practical applicability. Therefore, a flexible trade-off between interpretability and accuracy is a core challenge. To address this issue, this paper proposes a BRB-based method for a trade-off between interpretability and accuracy in safety assessment (IAT-BRB). To enhance the capability of BRB in modeling complex systems for safety assessment, this paper establishes dual-objective modeling guidelines that considers the trade-off between interpretability and accuracy. Based on this, a bi-objective whale optimization algorithm considering the trade-off between interpretability and accuracy (IAT-BOWOA) is proposed based on the bi-objective whale optimization algorithm (BOWOA). This algorithm achieves a flexible trade-off between interpretability and accuracy. It meets diverse performance requirements across application scenarios. In a case study, a WD615 diesel engine and flywheel system are selected as typical complex systems. Safety assessment experiments are conducted. The effectiveness and practicality of the proposed method are demonstrated through a systematic analysis.

安全评价在复杂系统中起着至关重要的作用。不完整的数据、模糊的信息和紧密耦合的因素往往在评估过程中造成严重的挑战。采用信念规则库（BRB）将专家知识与数据驱动推理相结合。它能有效地处理不确定性和模糊性。BRB已成为复杂系统安全评价的重要工具。现有的BRB方法以模型的简单性和透明度为代价提高了精度。评估结果难以被工程师理解和信任。这种限制降低了实际的适用性。因此，在可解释性和准确性之间的灵活权衡是一个核心挑战。为了解决这一问题，本文提出了一种基于brb的方法来权衡安全评估的可解释性和准确性（IAT-BRB）。为了提高BRB在复杂系统安全评估建模中的能力，本文建立了考虑可解释性和准确性之间权衡的双目标建模准则。在此基础上，在双目标鲸鱼优化算法（BOWOA）的基础上，提出了一种兼顾可解释性与准确性的双目标鲸鱼优化算法（IAT-BOWOA）。该算法在可解释性和准确性之间实现了灵活的权衡。它可以满足不同应用场景的性能需求。以WD615型柴油机及其飞轮系统为典型的复杂系统。进行了安全性评价实验。通过系统分析，验证了该方法的有效性和实用性。

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引用次数: 0

A dynamic feature block ensemble soft sensor for cement clinker production using spatiotemporal similarity spectral clustering 基于时空相似光谱聚类的水泥熟料生产动态特征块集成软传感器

IF 4.6 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2026-01-03 DOI: 10.1016/j.conengprac.2025.106740

Hanqi Ren , Hui Liu , Ran Xu , Heng Li , Xiaojun Xue

Accurate prediction of free calcium oxide (F-CaO) content is essential for cement clinker quality control. The cement clinker manufacturing process shows high complexity, with continuously evolving operating parameters, deeply coupled process data correlations, and complex temporal evolution patterns across multiple time series. Current prediction algorithms face two main challenges: insufficient use of temporal similarity features in time-series data, and inadequate modeling of time-varying characteristics, which leads to poor capture of dynamic process evolution patterns. This study presents a Spatiotemporal Similarity Spectral Clustering Dynamic Feature Block Ensemble (STSSC-DFBE) soft sensor for F-CaO concentration estimation. A spectral clustering algorithm incorporating JS divergence and temporal weighting mechanisms is developed. This approach effectively addresses similarity measurement difficulties in high-dimensional cement process data while better utilizing temporal information. The method also introduces a Comprehensive Metric (CM) that balances feature relevance and uniqueness, constructed from normalized Mutual Information (MI) and a practical, residual-based approximation of Conditional Information Gain (CIG). This enables dynamic feature ranking and adaptive ensemble strategies, particularly effective for small-sample industrial datasets. Experimental validation using real industrial data from cement manufacturing facilities shows that STSSC-DFBE achieves R² of 0.7953, with Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) reduced to 0.2045 and 0.1202, respectively. These results represent significant improvements over existing approaches. The research contributes to both theoretical understanding of spatiotemporal data modeling in industrial soft sensing and provides practical solutions for intelligent cement manufacturing.

准确预测游离氧化钙（F-CaO）含量对水泥熟料质量控制至关重要。水泥熟料生产过程具有高度的复杂性，操作参数不断变化，过程数据关联深度耦合，并且在多个时间序列中具有复杂的时间演化模式。当前的预测算法面临两个主要挑战：对时间序列数据时间相似性特征的利用不足，以及对时变特征的建模不足，导致动态过程演化模式的捕捉不足。本研究提出一种时空相似光谱聚类动态特征块集成（STSSC-DFBE）软传感器，用于F-CaO浓度估计。提出了一种结合JS散度和时间加权机制的谱聚类算法。该方法有效地解决了高维水泥工艺数据的相似性测量难题，同时更好地利用了时间信息。该方法还引入了平衡特征相关性和唯一性的综合度量（CM），该度量由归一化互信息（MI）和基于残差的条件信息增益（CIG）逼近构造而成。这使得动态特征排序和自适应集成策略成为可能，对小样本工业数据集尤其有效。使用水泥生产设施的实际工业数据进行实验验证表明，STSSC-DFBE的R²为0.7953，均方根误差（RMSE）和平均绝对误差（MAE）分别降至0.2045和0.1202。这些结果代表了对现有方法的重大改进。该研究不仅有助于对工业软测量中时空数据建模的理论认识，也为水泥智能制造提供了实际解决方案。

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引用次数: 0

Nonlinear control of a tilt augmented multi-rotor UAV with a fixed maximum convergence rate in presence of Inertia uncertainties 存在惯性不确定性时具有固定最大收敛速率的倾斜增强多旋翼无人机非线性控制

IF 4.6 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2026-01-03 DOI: 10.1016/j.conengprac.2025.106747

Sathyanarayanan Seshasayanan , Soumya Ranjan Sahoo

Multi-rotor unmanned aerial vehicles (UAVs) are capable of vertical take off and landing (VTOL) along with forward and lateral flights. Typically, such UAVs use linear controllers designed around the linearized model of the UAV obtained about its hover flight. Thus, wide range of attitude changes cannot be achieved with such controllers. To overcome this, we look into the design of nonlinear controllers, specifically for a tilt augmented multi-rotor UAV. The state-of-the-art nonlinear controllers designed for UAVs guarantee stability but do not provide a method to select the controller gains that not only ensure stability but also account for the bandwidth of the actuator. If the convergence rate associated with the loop is more than the operating frequency of the loop, the vehicle may undergo oscillations and even become unstable. Hence, a nonlinear controller with a fixed maximum convergence rate is proposed for a tilt augmented multi-rotor’s position and attitude control which accounts for this frequency. Given the bandwidth of the UAV’s rotor, we give a method to choose the appropriate gains for our controller so that the desired convergence rate restrictions are not violated. The proposed controller guarantees that the position and attitude errors converge to a desired bounded region in finite time with the presence of bounded external disturbances and parameter uncertainties. To account for the parameter uncertainties, we introduced an adaptive law along with the proposed controller. The stability and fixed maximum rate of convergence are established using the Lyapunov approach. Various experiments are performed outdoors by varying the mass and moment-of-inertia of the tilt augmented multi-rotor UAV to validate the performance of the proposed controller. The corresponding experimental videos can be found at https://www.youtube.com/watch?v=TiBILF6g2q0.

多旋翼无人机（uav）能够垂直起降（VTOL）以及向前和横向飞行。典型地，这类无人机使用线性控制器，其设计围绕得到的无人机关于其悬停飞行的线性化模型。因此，这种控制器无法实现大范围的姿态变化。为了克服这个问题，我们研究了非线性控制器的设计，特别是针对倾斜增强多旋翼无人机。为无人机设计的最先进的非线性控制器保证了稳定性，但没有提供一种方法来选择既保证稳定性又考虑执行器带宽的控制器增益。如果与回路相关的收敛速率大于回路的工作频率，则车辆可能发生振荡，甚至变得不稳定。在此基础上，提出了一种具有固定最大收敛速率的倾角增强多旋翼位置姿态控制的非线性控制器。在给定无人机旋翼带宽的情况下，给出了一种选择适当增益的方法，使控制器不违反期望的收敛速率限制。该控制器在存在有界外部干扰和参数不确定性的情况下，保证了位置和姿态误差在有限时间内收敛到期望的有界区域。为了考虑参数的不确定性，我们在提出的控制器中引入了自适应律。利用李亚普诺夫方法建立了该算法的稳定性和固定的最大收敛速率。通过改变倾斜增强多旋翼无人机的质量和转动惯量，在室外进行了各种实验，以验证所提出的控制器的性能。相应的实验视频可以在https://www.youtube.com/watch?v=TiBILF6g2q0上找到。

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引用次数: 0

Temperature estimation in PMSMs via combined direct and indirect methods 直接和间接相结合的pmsm温度估计方法

IF 4.6 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2026-01-02 DOI: 10.1016/j.conengprac.2025.106738

Martin Stefan Baumann , Andreas Steinboeck , Andreas Kugi , Wolfgang Kemmetmüller

Accurate temperature monitoring is essential for the safe and efficient operation of permanent magnet synchronous machines (PMSMs), especially in automotive applications. However, due to cost and integration challenges, placing temperature sensors on critical components like permanent magnets is impractical. This paper proposes an observer-based approach that leverages available measurements to estimate temperatures throughout the machine without relying on full sensor coverage. Two observers are developed: one based on an electrical machine model, which estimates the permanent magnet temperature indirectly via the permanent magnet flux linkage, and another one based on a thermal model, which incorporates measured temperatures from the end winding and the estimate from the electrical observer. The approach combines data-driven calibration with physical modeling to achieve high estimation accuracy and robustness under varying cooling conditions. The proposed method is validated both experimentally and through dedicated simulation studies, that assess the observer’s robustness to model uncertainties, parameter variations, and measurement noise. The results demonstrate that fusing electrical and thermal observations enables more precise and responsive temperature estimation than using either model alone. The method provides a practical alternative to dense sensor placement while preserving reliability and safety.

准确的温度监测对于永磁同步电机（pmms）的安全高效运行至关重要，特别是在汽车应用中。然而，由于成本和集成方面的挑战，将温度传感器放置在永久磁铁等关键部件上是不切实际的。本文提出了一种基于观测器的方法，该方法利用可用的测量值来估计整个机器的温度，而不依赖于完全的传感器覆盖。开发了两种观测器：一种基于电机模型，通过永磁链间接估计永磁体温度；另一种基于热模型，结合了末端绕组的测量温度和电观测器的估计。该方法将数据驱动校准与物理建模相结合，在不同的冷却条件下实现了较高的估计精度和鲁棒性。所提出的方法通过实验和专门的仿真研究进行了验证，这些研究评估了观测器对模型不确定性、参数变化和测量噪声的鲁棒性。结果表明，与单独使用任何一种模型相比，融合电和热观测可以更精确和响应地估计温度。该方法在保证可靠性和安全性的同时，为密集传感器的放置提供了一种实用的替代方案。

{"title":"Temperature estimation in PMSMs via combined direct and indirect methods","authors":"Martin Stefan Baumann , Andreas Steinboeck , Andreas Kugi , Wolfgang Kemmetmüller","doi":"10.1016/j.conengprac.2025.106738","DOIUrl":"10.1016/j.conengprac.2025.106738","url":null,"abstract":"<div><div>Accurate temperature monitoring is essential for the safe and efficient operation of permanent magnet synchronous machines (PMSMs), especially in automotive applications. However, due to cost and integration challenges, placing temperature sensors on critical components like permanent magnets is impractical. This paper proposes an observer-based approach that leverages available measurements to estimate temperatures throughout the machine without relying on full sensor coverage. Two observers are developed: one based on an electrical machine model, which estimates the permanent magnet temperature indirectly via the permanent magnet flux linkage, and another one based on a thermal model, which incorporates measured temperatures from the end winding and the estimate from the electrical observer. The approach combines data-driven calibration with physical modeling to achieve high estimation accuracy and robustness under varying cooling conditions. The proposed method is validated both experimentally and through dedicated simulation studies, that assess the observer’s robustness to model uncertainties, parameter variations, and measurement noise. The results demonstrate that fusing electrical and thermal observations enables more precise and responsive temperature estimation than using either model alone. The method provides a practical alternative to dense sensor placement while preserving reliability and safety.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"168 ","pages":"Article 106738"},"PeriodicalIF":4.6,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing pH control in microalgae raceway photobioreactors using 3DoF-KF model-on-demand model predictive control 利用3DoF-KF模型-按需模型预测控制加强微藻回旋式光生物反应器pH控制

IF 4.6 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2026-01-02 DOI: 10.1016/j.conengprac.2025.106742

Pablo Otálora , Sarasij Banerjee , Mohamed El Mistiri , Owais Khan , Daniel E. Rivera , José Luis Guzmán

Microalgae have gained increasing recognition as a sustainable resource with diverse applications ranging from biofuel production to wastewater treatment. Raceway reactors, the most widespread microalgae production system, are vulnerable to environmental fluctuations and external contamination, which can negatively impact microalgal growth and increase the emphasis on culture conditions, especially pH. In this study, a three-degree-of-freedom model predictive controller is presented, which is designed to regulate pH in raceway reactors. The controller employs a Model-on-Demand (MoD) approach for real-time data-driven estimation by using a database generated by exciting the system with control-relevant multisine signals. The estimated models demonstrate accurate goodness of fit across a wide range of prediction horizons, outperforming similar linear models. The controller formulation offers significant flexibility, enabling users to independently tune the speeds of setpoint tracking, measured disturbance rejection, and unmeasured disturbance rejection. The experimental results achieved in a pilot facility demonstrate that the proposed methodology is intuitive, straightforward and highly effective in controlling microalgae production systems.

微藻作为一种可持续资源已得到越来越多的认可，其应用范围从生物燃料生产到废水处理。回旋式反应器是最广泛的微藻生产系统，它容易受到环境波动和外界污染的影响，这会对微藻的生长产生负面影响，并增加对培养条件特别是pH的重视。本研究提出了一种三自由度模型预测控制器，用于调节回旋式反应器中的pH。控制器采用按需模型（MoD）方法，通过使用与控制相关的多正弦信号激励系统产生的数据库进行实时数据驱动估计。估计模型在广泛的预测范围内显示出准确的拟合优度，优于类似的线性模型。控制器配方提供了显著的灵活性，使用户能够独立调整设定值跟踪，测量干扰抑制和未测量干扰抑制的速度。在一个试点设施中取得的实验结果表明，所提出的方法直观、直接，在控制微藻生产系统方面非常有效。

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引用次数: 0

Fully actuated system approach-based control design for three benchmark nonlinear underactuated systems 基于全驱动系统方法的三基准非线性欠驱动系统控制设计

IF 4.6 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2025-12-31 DOI: 10.1016/j.conengprac.2025.106728

Guangren Duan , Lin Liu , Haowen Liu

In this paper, fully actuated system (FAS) approach-based control design for three benchmark underactuated systems (UASs), namely, the inertia wheel pendulum (IWP), ball-and-beam system (BBS), and translational oscillator with rotational actuator (TORA), is proposed. We first convert the three dynamic systems into a simple unified form, which belongs to the Type III UAS defined in the previous work. Then, two FAS approach-based controllers with their corresponding feasible sets are proposed on two distinct subsets. The effectiveness of the proposed controllers is validated through simulation and experimental results for both stabilization of the BBS and IWP. Furthermore, output tracking and regulation problems are addressed with corresponding experimental demonstrations.

针对惯性轮摆（IWP）、球梁系统（BBS）和带旋转作动器的平移振荡器（TORA）三种基准欠驱动系统（UASs），提出了基于全驱动系统（FAS）方法的控制设计。我们首先将三个动态系统转换成一个简单的统一形式，属于前面工作中定义的Type III UAS。然后，在两个不同的子集上提出了两种基于FAS方法的控制器及其相应的可行集。通过仿真和实验结果验证了所提控制器对BBS和IWP的稳定性。在此基础上，对输出跟踪和调节问题进行了相应的实验论证。

引用次数: 0

Reduced-order asymptotic observer of nonlinear friction for precise motion control 精确运动控制非线性摩擦的降阶渐近观测器

IF 4.6 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2025-12-31 DOI: 10.1016/j.conengprac.2025.106733

Michael Ruderman

Nonlinear friction has long been, and continues to be, one of the major challenges for precision motion control systems. A linear asymptotic observer of the motion state variables with nonlinear friction uses a dedicated state-space representation of the dynamic friction force (including pre-sliding) (Ruderman, 2023b), which is robust to the noisy input signals. The observer implements the reduced-order Luenberger observation law, while assuming the output displacement is the only available measurement. The latter is relevant for multiple motion control applications with use of encoder-type sensors. The uniform asymptotic stability and convergence analysis of the proposed observer are elaborated in the present work by using the Lyapunov function-based stability criterion by Ignatyev and imposing parametric constraints on the time-dependent eigenvalues of the system matrix to be always negative real. A design procedure for assigning a dominant, and thus slowest, real pole of the observer is proposed. Explanative numerical examples accompany the developed analysis. In addition, a thorough experimental evaluation is given for the proposed observer-based friction compensation which is performed for positioning and tracking tasks. The observer-based compensation, which can serve as a plug-in to a standard feedback controller, extends a PID feedback control that is optimally tuned for disturbance suppression. The experimental results are compared with and without the plugged-in observer-based compensator.

非线性摩擦一直是并将继续是精密运动控制系统的主要挑战之一。具有非线性摩擦的运动状态变量的线性渐近观测器使用动态摩擦力（包括预滑动）的专用状态空间表示（Ruderman, 2023b），该观测器对噪声输入信号具有鲁棒性。该观测器实现了降阶Luenberger观测律，同时假设输出位移是唯一可用的测量值。后者与使用编码器类型传感器的多种运动控制应用相关。本文通过使用由Ignatyev提出的基于Lyapunov函数的稳定性判据，并对系统矩阵的时变特征值施加参数约束使其始终为负实数，阐述了该观测器的一致渐近稳定性和收敛性分析。提出了一种分配观测器的主导、最慢的实际极点的设计方法。解释性的数值例子伴随着发展的分析。此外，对提出的基于观测器的摩擦补偿方法在定位和跟踪任务中的应用进行了全面的实验评估。基于观测器的补偿可以作为标准反馈控制器的插件，扩展了对干扰抑制进行最佳调谐的PID反馈控制。对插入观测器补偿器和不插入观测器补偿器的实验结果进行了比较。

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引用次数: 0

Energy-aware optimization of electric vehicles’ dual-motor coupled powertrain based on heterogeneous synchronous reinforcement learning 基于异构同步强化学习的电动汽车双电机耦合动力系统能量感知优化

IF 4.6 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2025-12-31 DOI: 10.1016/j.conengprac.2025.106745

Ying Zhang , Haoran Qi , Jinchao Chen , Chenglie Du , Shuaishuai Ge , Yongquan Xie

Dual-motor coupled powertrain (DMCP) is a promising drive type that can improve the energy utilization efficiency and driving range of electric vehicles (EVs). In this paper, a heterogeneous synchronous reinforcement learning (HSRL) approach is proposed to optimize the energy utilization efficiency of the DMCP in EVs. First, the models of the DMCP and vehicle dynamics are established. Then, the HSRL framework is constructed to select the drive mode and determine the torque allocation coefficient of the DMCP simultaneously. Within the HSRL framework, an actor-critic network is adopted, and a cross-domain learning strategy is proposed. The cross-domain learning strategy incorporates discrete domain learning (DDL) and continuous domain learning (CDL) to learn both the discrete and continuous decision-making tasks concurrently. Additionally, gradient strategies for DDL and CDL are designed. Based on the proposed HSRL, the energy-aware drive mode and torque allocation coefficient of the DMCP are selected and determined. To demonstrate the superiority of the proposed method, two state-of-the-art (SOTA) methods are chosen as benchmarks. The simulation and experimental results show that the proposed method outperforms these benchmarks in optimizing the energy utilization efficiency of the EVs’ DMCP.

双电机耦合动力系统（DMCP）是一种很有前途的驱动类型，可以提高电动汽车的能源利用效率和续驶里程。本文提出了一种异构同步强化学习（HSRL）方法来优化电动汽车DMCP的能量利用效率。首先，建立了DMCP模型和车辆动力学模型。然后，构建HSRL框架，同时选择驱动模式和确定DMCP的转矩分配系数。在HSRL框架内，采用了行动者-评论网络，并提出了跨领域学习策略。跨领域学习策略将离散领域学习（DDL）和连续领域学习（CDL）相结合，可以同时学习离散和连续的决策任务。此外，还设计了DDL和CDL的梯度策略。在此基础上，选择并确定了能量感知驱动方式和转矩分配系数。为了证明所提出方法的优越性，选择了两种最先进的（SOTA）方法作为基准。仿真和实验结果表明，该方法在优化电动汽车DMCP能量利用效率方面优于这些基准。

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引用次数: 0

Composite safety control for non-coaxial two-wheeled robot with geometry-enhanced multi-step control barrier function 具有几何增强多步控制屏障功能的非同轴两轮机器人复合安全控制

IF 4.6 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice

Pub Date : 2025-12-31 DOI: 10.1016/j.conengprac.2025.106732

Mengfei Zhang , Zhao Feng , Xiaohui Zhang , Xingyu Chen , Deng Li , Xiaohui Xiao

Navigating non-coaxial two-wheeled robots through narrow environments presents dual challenges: ensuring collision-free motion and maintaining lateral safety during aggressive steering maneuvers required for obstacle avoidance. This paper proposes a novel approach which integrates two complementary safety mechanisms within a model predictive control (MPC) architecture. The geometry-enhanced navigation safety control barrier function (GNSCBF) accurately captures the robot’s varying spatial occupancy during steering through a novel articulated geometry representation. The multi-step roll safety control barrier function (MRSCBF) predicts future roll dynamics over a finite horizon and optimizes velocity profiles through quadratic programming (QP) to ensure lateral safety. Experimental validation on real-world platform demonstrates the effectiveness of the proposed approach.

在狭窄的环境中导航非同轴两轮机器人面临双重挑战：确保无碰撞运动，并在避障所需的积极转向机动期间保持横向安全。本文提出了一种在模型预测控制（MPC）体系结构中集成两种互补安全机制的新方法。几何增强的导航安全控制屏障功能（GNSCBF）通过一种新颖的铰接几何表示精确捕捉机器人在转向过程中的空间占用变化。多级滚转安全控制屏障函数（MRSCBF）预测有限范围内未来的滚转动力学，并通过二次规划（QP）优化速度剖面，以确保横向安全。在实际平台上的实验验证表明了该方法的有效性。

引用次数: 0