Control Engineering Practice最新文献_第8页

Experimental implementation of neural network integration into a dynamic recurrent neurocontroller in PV system application 神经网络集成于动态递归神经控制器的实验实现在光伏系统中的应用

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

Control Engineering Practice

Pub Date : 2026-01-08 DOI: 10.1016/j.conengprac.2025.106753

Mustapha Meraouah , Faiza Kaddari , Said Hassaine , Sandrine Moreau , Youcef Mihoub

This study aims to develop a high–performance Maximum Power Point Tracking (MPPT) strategy for Photovoltaic (PV) systems, with the objective of achieving fast convergence, minimal oscillations, and stable operation under rapidly changing irradiance. The nonlinear behavior of PV systems poses a significant challenge to meeting these goals and maximizing harvested energy. To address this, the paper presents a dynamic MPPT strategy that leverages PV characteristics through an Artificial Neural Network (ANN) trained using graph–based data processing methods to estimate the reference voltage. The neural estimator is integrated with a dynamic recurrent neurocontroller, which is trained via the Real–Time Recurrent Learning (RTRL) algorithm. To ensure system stability, a Lyapunov–based adaptive learning rate is applied to dynamic ANN–RTRL. Simulation studies were first performed to assess the effectiveness and robustness of the proposed ANN under varying meteorological conditions, and the results were subsequently validated experimentally using a Chroma PV emulator and a dSPACE 1202 MicroLabBox. The ANN performance was also compared with conventional, advanced, optimization, and intelligent algorithms under severe environmental fluctuations. The proposed ANN–RTRL was experimentally benchmarked against two reference methods: an ANN using the Least Mean Squares (ANN–LMS) algorithm and a conventional Perturb and Observe (P&O) with a Proportional–Integral (PI) controller. Results under both standard and rapidly varying irradiance conditions show that ANN–RTRL and ANN–LMS achieve tracking efficiencies above 99%, with reduced oscillations and improved precision. Notably, ANN–RTRL exhibits superior robustness and reaches the Maximum Power Point (MPP) 58% faster than ANN–LMS and 5% faster than P&O–PI, confirming its suitability for high–performance, real–time PV MPPT applications.

本研究旨在开发一种高性能的光伏（PV）系统最大功率点跟踪（MPPT）策略，其目标是在快速变化的辐照度下实现快速收敛、最小振荡和稳定运行。光伏系统的非线性行为对实现这些目标和最大限度地获取能量提出了重大挑战。为了解决这个问题，本文提出了一种动态MPPT策略，该策略通过使用基于图的数据处理方法训练的人工神经网络（ANN）来利用光伏特性来估计参考电压。神经估计器与动态递归神经控制器相结合，通过实时递归学习（RTRL）算法进行训练。为了保证系统的稳定性，将基于李雅普诺夫的自适应学习率应用于动态ANN-RTRL。首先进行了模拟研究，以评估所提出的人工神经网络在不同气象条件下的有效性和鲁棒性，随后使用Chroma PV模拟器和dSPACE 1202 MicroLabBox对结果进行了实验验证。并将人工神经网络与传统算法、高级算法、优化算法和智能算法在剧烈环境波动下的性能进行了比较。所提出的ANN - rtrl与两种参考方法进行了实验基准测试：一种是使用最小均方差（ANN - lms）算法的ANN，另一种是使用比例积分（PI）控制器的传统扰动与观察（P&；O）方法。在标准和快速变化辐照度条件下的结果表明，ANN-RTRL和ANN-LMS的跟踪效率都在99%以上，振荡减少，精度提高。值得注意的是，ANN-RTRL表现出卓越的鲁棒性，比ANN-LMS快58%，比P& - O-PI快5%，达到最大功率点（MPP），证实了其适用于高性能、实时光伏MPPT应用。

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引用次数: 0

Resilient safety-critical control for autonomous electric vehicles via disturbance-observer-based adaptive control barrier functions 基于扰动观测器的自适应控制障碍函数的自动驾驶电动汽车弹性安全关键控制

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

Control Engineering Practice

Pub Date : 2026-01-08 DOI: 10.1016/j.conengprac.2025.106690

Yifan Cheng , Yuxiang Zhang , Yifei Wang , Hongqing Chu , Bingzhao Gao , Hong Chen

This paper proposes a resilient safety-critical control design for disturbed systems by integrating a finite-time disturbance observer (DOB) to enhance resilience beyond the standalone adaptability of penalty parameters. Specifically, for systems subject to unstructured disturbances and high-relative-degree safety constraints, a general safety condition of adaptive control barrier function is derived. By incorporating the finite-time DOB, a sufficient condition for resilience-oriented system safety is established, thereby effectively reducing the variation range of penalty parameters and mitigating reliance on their standalone adaptability, and improving system performance under disturbances. The optimization framework incorporating this improved condition is implemented, enhancing the robustness of the original closed-loop system in terms of safety and stability. The proposed controller is applied to the motion control problem of autonomous electric vehicles. The co-simulations and Hardware-in-the-Loop tests under typical disturbances demonstrate its superiority over various baseline methods.

本文通过集成有限时间扰动观测器（DOB），提出了一种受扰动系统的弹性安全关键控制设计，以增强系统的弹性，超越惩罚参数的独立自适应。具体而言，对于受非结构化干扰和高相对安全约束的系统，导出了自适应控制障碍函数的一般安全条件。通过引入有限时间DOB，建立了面向弹性的系统安全的充分条件，从而有效地减小了惩罚参数的变化范围，减轻了对惩罚参数独立自适应的依赖，提高了系统在扰动下的性能。实现了包含改进条件的优化框架，增强了原闭环系统在安全性和稳定性方面的鲁棒性。将该控制器应用于自动驾驶电动汽车的运动控制问题。在典型干扰下的联合仿真和硬件在环测试表明了该方法优于各种基线方法。

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引用次数: 0

Adaptive nonlinear model predictive control of monoclonal antibody glycosylation in CHO cell culture CHO细胞培养单克隆抗体糖基化的自适应非线性模型预测控制

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

Control Engineering Practice

Pub Date : 2026-01-07 DOI: 10.1016/j.conengprac.2025.106731

Yingjie Ma , Jing Guo , Alexis B. Dubs , Krystian Ganko , Richard D. Braatz

N-glycosylation is a critical quality attribute of monoclonal antibodies (mAbs), the dominant class of biopharmaceuticals. Controlling glycosylation remains difficult due to intrinsic pathway complexity, limited online measurements, and a lack of tailored control strategies. This work applies an adaptive nonlinear model predictive control (ANMPC) framework to a fed-batch mAb production process, using a multiscale model that links extracellular conditions to intracellular Golgi reactions to predict glycan profiles. Model parameters are updated online as new measurements arrive, after which a shrinking-horizon optimization computes the control inputs; only the first control move is implemented each cycle. Case studies show that, with a minimal day-1 galactose excitation, ANMPC mitigates model–plant mismatch and achieves up to 130% and 96% higher performance than open-loop optimization and state NMPC, respectively. Under more realistic conditions (partial measurement availability and longer preparation time), ANMPC maintains comparable performance, indicating robustness to practical limitations. Overall, the results demonstrate that ANMPC can actively shape glycan distributions in silico and offers a viable path toward closed-loop control of mAb glycosylation.

n -糖基化是单克隆抗体（mab）的关键质量属性，单克隆抗体是生物制药的主导类别。由于内在途径的复杂性、有限的在线测量和缺乏定制的控制策略，控制糖基化仍然很困难。这项工作将自适应非线性模型预测控制（ANMPC）框架应用于补批单抗生产过程，使用将细胞外条件与细胞内高尔基反应联系起来的多尺度模型来预测聚糖谱。当新的测量值到达时，在线更新模型参数，然后通过缩界优化计算控制输入；每个周期只执行第一个控制动作。案例研究表明，在1天半乳糖激励最小的情况下，ANMPC减轻了模型与工厂的不匹配，比开环优化和状态NMPC的性能分别提高了130%和96%。在更现实的条件下（部分测量可用性和更长的制备时间），ANMPC保持相当的性能，表明对实际限制的鲁棒性。总之，结果表明，ANMPC可以积极地塑造聚糖在硅中的分布，并为单克隆抗体糖基化的闭环控制提供了可行的途径。

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引用次数: 0

Benchmark vehicle suspension data based on ISO 8608 road profiles 基于ISO 8608道路轮廓的基准车辆悬架数据

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

Control Engineering Practice

Pub Date : 2026-01-07 DOI: 10.1016/j.conengprac.2025.106755

Mathias Foo , Ai Hui Tan

Ride comfort is a key factor in passenger safety and is largely determined by the suspension system of a vehicle. Although mechanical active suspension systems have greatly improved ride quality, the move towards electronically controlled active suspensions—driven by rapid advances in vehicle technology—requires more sophisticated engineering solutions. This challenge is made even greater by the increasing variability of modern road infrastructure. To design and validate such solutions, suspension systems need to be tested across a wide variety of road surface profiles. Understanding the system’s limitations under different conditions is crucial before developing and evaluating control strategies. For this reason, standardised road profiles that accurately represent different types of terrain are essential. This paper presents a benchmark dataset for vehicle suspension systems based on ISO-compliant road profiles. The dataset has been developed to support researchers working on suspension parameter identification, control algorithm design, and the study of nonlinear suspension behaviour. To illustrate its value, a benchmark comparison of system identification models using the dataset is provided. The data containing ISO road profiles and measured suspension displacements, along with codes for modelling and visualisation, is freely available at https://doi.org/10.5281/zenodo.17242919

乘坐舒适性是乘客安全的关键因素，在很大程度上取决于车辆的悬架系统。虽然机械主动悬架系统大大提高了乘坐质量，但在汽车技术快速发展的推动下，向电子控制主动悬架的转变需要更复杂的工程解决方案。现代道路基础设施的不断变化使这一挑战更加严峻。为了设计和验证这些解决方案，悬架系统需要在各种路面上进行测试。在制定和评估控制策略之前，了解系统在不同条件下的局限性至关重要。因此，准确表示不同类型地形的标准化道路轮廓是必不可少的。本文提出了一个基于iso标准道路轮廓的汽车悬架系统基准数据集。该数据集的开发是为了支持研究人员对悬架参数识别、控制算法设计和非线性悬架行为的研究。为了说明其价值，提供了使用该数据集的系统识别模型的基准比较。包含ISO道路轮廓和测量的悬架位移的数据，以及建模和可视化代码，可在https://doi.org/10.5281/zenodo.17242919免费获得

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引用次数: 0

A review of noise-aware path planning for urban drone operations 城市无人机作业噪声感知路径规划研究综述

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

Control Engineering Practice

Pub Date : 2026-01-07 DOI: 10.1016/j.conengprac.2025.106750

Yinuo Wang , Nan Zhang , Xun Huang

The accelerating integration of drones into urban air mobility (UAM) has sparked concerns about noise pollution and its consequences on public acceptance and regulatory frameworks. This review examines the role of noise-aware air traffic management strategies in facilitating the harmonious incorporation of drones into the low-altitude economy. First, we examine traditional noise mitigation practices within conventional aviation and assess their suitability for drone applications. Next, we offer an overview of drone noise measurement techniques and psychoacoustic metrics to facilitate a more nuanced understanding for drone noise modeling. To obtain deep insights into noise-aware path planning, we outline general optimization methods before focusing specifically on how noise-related factors can be integrated into the planning process. Key advances in noise-related loss functions, optimization algorithms, and unique application scenarios are evaluated. Finally, we identify the potential challenges of implementing noise-aware UAM and propose several possible future research directions in noise-aware path planning.

无人机与城市空中交通（UAM）的加速融合引发了人们对噪音污染及其对公众接受度和监管框架的影响的担忧。本综述探讨了噪声感知空中交通管理策略在促进无人机和谐融入低空经济中的作用。首先，我们研究了传统航空中的传统降噪做法，并评估了它们对无人机应用的适用性。接下来，我们提供无人机噪声测量技术和心理声学指标的概述，以促进更细致入微的理解无人机噪声建模。为了深入了解噪声感知路径规划，我们概述了一般的优化方法，然后特别关注如何将噪声相关因素集成到规划过程中。评估了噪声相关损失函数、优化算法和独特应用场景的关键进展。最后，我们确定了实现噪声感知UAM的潜在挑战，并提出了噪声感知路径规划的几个可能的未来研究方向。

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引用次数: 0

Trajectory tracking control for underground rubber-tyred shuttle cars via axle-decoupled tire force hybrid observer and delay-Compensating controller 基于轴解耦轮胎力混合观测器和时滞补偿控制器的地下胶轮穿梭车轨迹跟踪控制

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

Control Engineering Practice

Pub Date : 2026-01-07 DOI: 10.1016/j.conengprac.2025.106754

Likun Le, Aixiang Ma, Dongjie Wang, Guohua Li

Addressing the trajectory tracking control problem of underground mining double Ackermann four-wheel steering unmanned transport vehicles in complex underground roadway environments, this paper proposes a comprehensive control strategy integrating tire force hybrid observation and measurement-control delay compensation. By designing an Axle-decoupled Tire Force Hybrid Observer System (ATFHOS), combining neural networks to estimate tire steady-state forces with sliding mode observers to reconstruct dynamic forces, and achieving front-rear axle coupling error compensation based on virtual measurements, a Razumikhin-LMI Robust Controller (RLRC) is further constructed, utilizing higher-order sliding mode control for steering angles and solving stability problems caused by steering system and observer delays through LMI iterative optimization. Simulations and experiments demonstrate that the proposed tire force coupling compensation strategy significantly reduces longitudinal/lateral tire force estimation errors, and the comprehensive performance of the proposed RLRC is superior to three control groups.

针对地下采矿双Ackermann四轮转向无人运输车辆在复杂地下巷道环境下的轨迹跟踪控制问题，提出了一种轮胎力混合观测与测控延迟补偿相结合的综合控制策略。通过设计车轴解耦的轮胎力混合观测器系统（ATFHOS），结合神经网络估计轮胎稳态力和滑模观测器重构动态力，并基于虚拟测量实现前后桥耦合误差补偿，进一步构建了Razumikhin-LMI鲁棒控制器（RLRC）。利用高阶滑模控制转向角，通过LMI迭代优化解决转向系统和观测器延迟引起的稳定性问题。仿真和实验结果表明，所提出的轮胎力耦合补偿策略显著降低了轮胎纵向/横向力估计误差，并且所提出的RLRC的综合性能优于三个对照组。

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引用次数: 0

Robust control of interleaved boost converter with active disturbance compensation 具有自干扰补偿的交错升压变换器鲁棒控制

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

Control Engineering Practice

Pub Date : 2026-01-06 DOI: 10.1016/j.conengprac.2025.106746

Giovanni Garraffa , Daniele Scirè , Francesco Alonge , Filippo D’Ippolito , Giuseppe Lullo , Giuseppe Costantino Giaconia , Alessandro Busacca , Gianpaolo Vitale , Antonino Sferlazza

Nowadays, extensive research is conducted on interleaved DC-DC boost converters due to their ability to minimize current ripple and enhance fault tolerance. In this article, we present a control methodology that achieves effective output voltage regulation while demonstrating robustness against uncertainties in parameters, variations in the source supply voltage, and deviations in load. These objectives are achieved by developing an equivalent circuit model for the interleaved boost converter and formulating a linear representation using the exact linearization technique. Subsequently, trajectory tracking control strategies are employed, incorporating a sliding mode element to manage parameter uncertainties and compensate for disturbance errors. The controller, applied to the equivalent boost converter model, adjusts the duty cycle of each individual MOSFET command signal. This control mechanism utilizes the output voltage and the sum of the currents within the converter’s branches as feedback variables. Experimental results illustrate the effectiveness and feasibility of the presented methodology.

交错DC-DC升压变换器具有减小电流纹波和提高容错能力的优点，目前得到了广泛的研究。在本文中，我们提出了一种控制方法，可以实现有效的输出电压调节，同时展示了对参数不确定性、电源电压变化和负载偏差的鲁棒性。这些目标是通过开发交错升压转换器的等效电路模型和使用精确线性化技术制定线性表示来实现的。然后，采用轨迹跟踪控制策略，结合滑模单元来管理参数不确定性并补偿干扰误差。该控制器应用于等效升压变换器模型，调整每个MOSFET命令信号的占空比。这种控制机制利用输出电压和变换器分支内电流的总和作为反馈变量。实验结果证明了该方法的有效性和可行性。

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引用次数: 0

Exponentially dissipative vehicle path tracking control considering roll safety under network communications 网络通信下考虑滚转安全的指数耗散车辆路径跟踪控制

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

Control Engineering Practice

Pub Date : 2026-01-06 DOI: 10.1016/j.conengprac.2025.106744

Yong Jun Lee , Woo Jin Ahn , Dong Sung Pae , Hyun Duck Choi , Myo Taeg Lim

This paper proposes an exponentially dissipative vehicle path tracking control scheme that considers roll stability in the presence of time-varying delays and stochastic packet dropouts. The proposed controller simultaneously enhances lane-keeping performance and ride comfort by incorporating both lateral and roll dynamics into the system model. To attenuate the effects of disturbances, exponential dissipativity is introduced in the control design process, which covers existing results for the H_∞ and passivity. On the other hand, the use of network communication in path tracking systems introduces challenges such as packet dropouts, transmission delays, and limited resources. A dynamic event-triggered mechanism using auxiliary variables plays an important role in efficiently utilizing communication resources. Moreover, a set of delay-dependent conditions for the desired controller is derived in terms of linear matrix inequalities using the generalized reciprocally convex technique with the canonical Bessel-Legendre inequality. Finally, the effectiveness of the proposed controller is evaluated under several driving scenarios using CarSim/Simulink co-simulation.

提出了一种考虑时变时滞和随机丢包情况下车辆侧倾稳定性的指数耗散路径跟踪控制方案。所提出的控制器通过将横向和侧倾动力学纳入系统模型，同时提高了车道保持性能和乘坐舒适性。为了减弱干扰的影响，在控制设计过程中引入了指数耗散率，这涵盖了H∞和无源性的现有结果。另一方面，在路径跟踪系统中使用网络通信带来了诸如数据包丢失、传输延迟和有限资源等挑战。利用辅助变量的动态事件触发机制对有效利用通信资源起着重要作用。此外，利用正则贝塞尔-勒让德不等式，利用广义互凸技术，导出了一组线性矩阵不等式的时滞相关条件。最后，利用CarSim/Simulink联合仿真对所提控制器在几种驾驶场景下的有效性进行了评估。

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引用次数: 0

Optimal quantum gate design for Bloch-band interferometry 布洛赫带干涉测量的最佳量子门设计

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

Control Engineering Practice

Pub Date : 2026-01-05 DOI: 10.1016/j.conengprac.2025.106714

Ali Sulehria , Jieqiu Shao , Marco M. Nicotra

Recent advancements in quantum sensing have led to a new generation of trapped-atom interferometers that can be “programmed” by performing a sequence of elementary operations, or gates. The objective of each gate is to promote a specific transition between the Bloch states of the free Hamiltonian. This paper details how quantum optimal control was used to generate a library of high-fidelity gates for Bloch-band interferometry. For ease of generalization, the gates featured in this paper are grouped into three broad categories: state-to-state transfer, relative phase unitary, and global phase unitary, each of which is associated with a different quantum optimal control problem formulation. Specific examples of Bloch-band interferometry gates are presented throughout the paper. Sample modifications to account for actuator bandwidth are also provided.

最近在量子传感方面的进展导致了新一代的捕获原子干涉仪，它可以通过执行一系列基本操作或门来“编程”。每个门的目标是促进自由哈密顿量的布洛赫状态之间的特定过渡。本文详细介绍了如何使用量子最优控制来生成用于布洛赫带干涉测量的高保真门库。为了便于推广，本文中的门被分为三大类：状态到状态转移、相对相位酉和全局相位酉，每一类都与不同的量子最优控制问题公式相关联。文中给出了布洛赫带干涉测量门的具体实例。还提供了考虑致动器带宽的修改样例。

引用次数: 0

Desired compensation adaptive robust control of remotely operated vehicles through integrated attitude and depth design 基于姿态与深度集成设计的遥控车辆期望补偿自适应鲁棒控制

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

Control Engineering Practice

Pub Date : 2026-01-05 DOI: 10.1016/j.conengprac.2025.106748

Xin Hu , Yiyang Zheng , Deqing Mei , Zheng Chen

The increasing reliance on remotely operated vehicles (ROVs) for critical subsea operations necessitates accurate attitude and position control in complex underwater environments. In addition to seabed operations, ROVs frequently perform hovering tasks that require control of different attitude angles. However, the hovering operation pattern and movements of mounted tools such as manipulators generate additional disturbances, resulting in unsatisfactory performance of conventional dynamic positioning controllers. As a solution, this paper proposes a desired compensation adaptive robust control scheme. The controller is based on a partially decoupled dynamic model of ROVs which retains the coupling effects among depth and attitude angles. A parameter adaptation law is proposed to cope with parameter uncertainties. Coupling and nonlinearity are compensated through model compensation. Additionally, robust feedback is designed to address underwater disturbances, model uncertainties, and residual coupling due to model decomposition. The feasibility and capability of the proposed control scheme are experimentally validated. Experiments are conducted including simultaneous depth and attitude control with tilted postures and control during horizontal movements. The results show substantial improvement in tracking error and response time.

在关键的水下作业中，越来越多地依赖远程操作车辆（rov），这就需要在复杂的水下环境中进行精确的姿态和位置控制。除了海底作业，rov还经常执行需要控制不同姿态角的悬停任务。然而，悬停操作方式和安装工具（如机械手）的运动产生额外的干扰，导致传统的动态定位控制器性能不理想。为此，本文提出了一种理想补偿自适应鲁棒控制方案。该控制器基于rov部分解耦的动力学模型，该模型保留了深度和姿态角之间的耦合效应。提出了一种参数自适应律来处理参数的不确定性。通过模型补偿对耦合和非线性进行补偿。此外，鲁棒反馈设计用于解决水下干扰、模型不确定性和模型分解引起的剩余耦合。实验验证了所提控制方案的可行性和性能。实验包括倾斜姿态下的深度和姿态同步控制以及水平运动时的控制。结果表明，在跟踪误差和响应时间方面有了很大的改进。

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引用次数: 0