Control Engineering Practice最新文献_第10页

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 : 2026-03-01 Epub 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

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 : 2026-03-01 Epub 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。

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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 : 2026-03-01 Epub 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

Large-scale EV charging coordination: a detailed exploration of mean-field and reinforcement learning approaches 大规模电动汽车充电协调：均值场和强化学习方法的详细探索

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

Control Engineering Practice

Pub Date : 2026-03-01 Epub Date: 2025-11-29 DOI: 10.1016/j.conengprac.2025.106669

Josep Caballé , Pablo Segovia , Carlos Ocampo-Martinez , Nicanor Quijano

The rapid proliferation of electric vehicles (EVs) presents significant challenges to power-grid stability, especially when thousands of cars charge simultaneously without coordination. This paper investigates two complementary families of scalable control schemes-(i) Mean-Field Control (Mean-Field Games and Mean-Field-Type Games), and (ii) model-free Reinforcement Learning (classical Reinforcement Learning and Deep Reinforcement Learning)-that capture stochastic arrivals, nodal capacity limits, ambient-temperature effects and battery degradation. Analytical mean-field-based formulations yield decentralized charging policies that depend only on population statistics and enjoy ϵ-Nash optimality as fleet size grows, while reinforcement-learning-based agents learn directly from interaction histories and cope naturally with partial observability and non-stationary price signals. A common set of nine key performance indicators is applied to two benchmark scenarios: a one-night, three-node grid and a seven-day, heterogeneous 1 000-EV testbed built on real distribution-network data. Results show that Mean-Field-Type Games minimizes unmet energy ( ≥ 1 %) and queueing delays, Deep Reinforcement Learning maximizes average final State-of-Charge ( ≈ 81 %) under volatile tariffs, and classical Reinforcement Learning provides the most interpretable albeit least efficient baseline. These quantified trade-offs clarify when model-based equilibrium methods suffice and when adaptive, data-driven controllers become indispensable, providing actionable guidance for large-scale, battery-health-aware EV-charging deployments.

电动汽车的快速发展对电网的稳定性提出了重大挑战，特别是在成千上万辆汽车同时充电而不协调的情况下。本文研究了两个互补的可扩展控制方案家族——(i)平均场控制（平均场博弈和平均场类型博弈）和（ii）无模型强化学习（经典强化学习和深度强化学习）——捕获随机到达、节点容量限制、环境温度效应和电池退化。基于分析平均场的公式产生分散的收费策略，仅依赖于人口统计，并且随着车队规模的增长享有ϵ-Nash最优性，而基于强化学习的代理直接从交互历史中学习，并自然地处理部分可观察性和非平稳价格信号。一套常见的9个关键性能指标应用于两个基准场景：一个是一个晚上的三节点电网，一个是一个7天的、基于真实配电网数据的1000 - ev异构试验台。结果表明，平均场型博弈最小化了未满足的能量（ ≥ 1%）和排队延迟，深度强化学习最大化了波动电价下的平均最终充电状态（ ≈ 81%），经典强化学习提供了最可解释但效率最低的基线。这些量化的权衡明确了什么时候基于模型的平衡方法足够了，什么时候自适应的、数据驱动的控制器变得必不可少，为大规模的、电池健康意识的电动汽车充电部署提供了可操作的指导。

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引用次数: 0

Rule-based approach incorporating MPC, integral-type LQR, and six-step operation for current control in PMSM 基于规则的PMSM电流控制方法，结合MPC、积分型LQR和六步操作

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

Control Engineering Practice

Pub Date : 2026-03-01 Epub Date: 2025-12-18 DOI: 10.1016/j.conengprac.2025.106694

Kenta Koiwa , Tomoya Takahashi , Tadanao Zanma , Kang-Zhi Liu

Permanent magnet synchronous motors (PMSMs) have been widely used for many applications. The current controller in voltage source inverters (VSIs) has to achieve a fast current response with zero steady-state errors in the wide operating range of the PMSM. In particular, it is challenging for the VSI to control the current precisely when the PMSM operates in the over-modulation region, including the six-step operation. In this region, the modulation index exceeds the linear range of pulse-width modulation, and during the six-step operation, the inverter output voltage becomes approximately a square wave. This article proposes a novel rule-based approach for VSIs driving the PMSM to achieve a fast and accurate current response in both the transient and steady states in the entire region from linear to six-step operations. The advantage of the proposed method is verified by a comparative analysis with the conventional finite-control-set model predictive control and continuous-control-set model predictive control through simulations and experiments. Consequently,the proposed method reduces the offset by approximately 23–36% in the over-modulation region and by approximately 41–50% during the six-step operation, compared with conventional methods, while maintaining a fast current (torque) response.

永磁同步电机（PMSMs）有着广泛的应用。电压源逆变器（vsi）中的电流控制器必须在PMSM的宽工作范围内实现零稳态误差的快速电流响应。特别是，当PMSM在过调制区域工作时，包括六步操作，VSI精确控制电流是具有挑战性的。在该区域，调制指数超过脉宽调制的线性范围，在六步工作期间，逆变器输出电压近似为方波。本文提出了一种新的基于规则的vsi驱动永磁同步电机的方法，以实现从线性到六步操作的整个区域的瞬态和稳态快速准确的电流响应。通过仿真和实验，将该方法与传统的有限控制集模型预测控制和连续控制集模型预测控制进行了对比分析，验证了该方法的优越性。因此，与传统方法相比，该方法在过调制区域减少了约23-36%的偏置，在六步操作期间减少了约41-50%的偏置，同时保持了快速的电流（转矩）响应。

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引用次数: 0

Constant-frequency control strategy and semi-active implementation of a two-stage leaf spring suspension 两级钢板弹簧悬架的恒频控制策略及半主动实现

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

Control Engineering Practice

Pub Date : 2026-03-01 Epub Date: 2025-12-19 DOI: 10.1016/j.conengprac.2025.106718

Xiaoliang Zhang , Tianqi Guo , Chaofu Bai , Jiamei Nie

Improving ride comfort in multi-stage leaf spring suspensions is critical for vehicle safety, driver health, and overall driving performance, yet a major challenge remains the abrupt changes in natural frequency due to sudden stiffness variations, which severely degrade ride quality. To address this challenge, this study employs the harmonic superposition method to extend the applicability of the memory element ω² criterion from simple harmonic vibrations to random vibrations, consequently proposing an extended ω² criterion. To this end, a skyhook inertance constant-frequency control rule is proposed, using the extended ω² criterion as the judgment basis to mitigate the abrupt natural frequency changes caused by two-stage stiffness leaf springs with memory characteristics. Furthermore, based on a semi-active device combining an adjustable inerter and damper, the skyhook inertance semi-active constant-frequency control(SH-IFC) strategy for two-stage stiffness leaf spring suspension systems is developed. By utilizing the simulated-mass property of the grounded inerter to add virtual mass to the sprung mass during abrupt stiffness variation, this strategy maintains the natural frequency of the suspension system and mitigates the impact of its natural frequency on suspension performance. Experimental validation conducted on hardware-in-the-loop (HiL) systems and a D2P platform confirmed the control strategy’s effectiveness, achieving RMS body acceleration reductions of 13.86 % (1/4 load), 18.57 % (1/2 load), and 15.32 % (3/4 load) compared to passive suspensions. These results indicate that the proposed strategy effectively stabilizes the natural frequency and mitigates ride comfort degradation caused by its abrupt variations.

提高多级钢板弹簧悬架的乘坐舒适性对车辆安全、驾驶员健康和整体驾驶性能至关重要，但主要挑战仍然是由于刚度突然变化导致的固有频率突然变化，这严重降低了乘坐质量。为了解决这一问题，本研究采用谐波叠加方法将记忆元件ω2判据的适用性从简单谐波振动扩展到随机振动，从而提出了扩展的ω2判据。为此，提出了一种以扩展ω2准则为判断依据的天钩惯性恒频控制规则，以缓解具有记忆特性的两级刚度钢板弹簧引起的固有频率突变。在此基础上，提出了一种结合可调惯性器和阻尼器的半主动控制策略，用于两级刚度钢板弹簧悬架系统的天钩惯性半主动恒频控制。该策略利用接地干涉器的模拟质量特性，在刚度突变时为簧载质量增加虚质量，保持了悬架系统的固有频率，减轻了其固有频率对悬架性能的影响。在硬件在环（HiL）系统和D2P平台上进行的实验验证证实了该控制策略的有效性，与被动悬架相比，RMS车身加速度降低了13.86%（1/4负载）、18.57%（1/2负载）和15.32%（3/4负载）。结果表明，该策略有效地稳定了车辆固有频率，减轻了因固有频率突变引起的乘坐舒适性下降。

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引用次数: 0

Combined feedback stabilization and iterative pulse shaping for regenerative optical amplifiers 再生光放大器的联合反馈稳定与迭代脉冲整形

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

Control Engineering Practice

Pub Date : 2026-03-01 Epub Date: 2025-12-18 DOI: 10.1016/j.conengprac.2025.106711

Nikolaus Würkner , Lukas Tarra , Andreas Deutschmann-Olek , Andreas Kugi

Regenerative amplifiers (RAs) are a common tool to generate high-intensity laser pulses by circulating them through an optical gain medium inside a cavity until the stored energy is depleted. This paper presents a control strategy that simultaneously suppresses dynamical instabilities while generating output pulses with a desired pulse shape. To enable model-based design, we derive a reduced nonlinear discrete-time model of the amplifier by combining spectral small-gain approximation with spatially averaged population dynamics in the gain medium. The resulting model captures both the dynamical and spectral behavior of the amplifier. Stabilization of a desired operating point is achieved through a linear–quadratic regulator (LQR) combined with an Extended Kalman Filter (EKF) for state estimation based on output energy measurements. This subordinate feedback scheme suppresses bifurcations and mitigates excitations induced by changes in the spectral pulse shape. On top of this control loop, we apply model-based iterative learning control (ILC) to asymptotically track desired output spectra. Projected ILC laws are used to adapt both the input filter and the provided pump-light intensity. The proposed method is validated in simulation on a distributed-parameter model calibrated to measurements of a Ho:YAG-based RA. Results demonstrate robust convergence to desired pulse shapes and highlights its feasibility for real-time implementation.

再生放大器（RAs）是一种常见的工具，产生高强度的激光脉冲，通过循环他们通过一个光学增益介质在一个腔内，直到储存的能量耗尽。本文提出了一种控制策略，在产生具有期望脉冲形状的输出脉冲的同时抑制动态不稳定性。为了实现基于模型的设计，我们通过将频谱小增益近似与增益介质中的空间平均种群动态相结合，推导出放大器的简化非线性离散时间模型。所得到的模型捕获了放大器的动态和频谱行为。通过线性二次型调节器（LQR）与基于输出能量测量的扩展卡尔曼滤波器（EKF）相结合来实现所需工作点的稳定。这种从属反馈方案抑制了分岔，减轻了谱脉冲形状变化引起的激励。在此控制回路之上，我们应用基于模型的迭代学习控制（ILC）来渐近跟踪期望的输出谱。投影ILC律用于调整输入滤波器和提供的泵浦光强度。在一个基于Ho: yag的RA测量校准的分布参数模型上进行了仿真验证。结果表明，该算法对期望的脉冲形状具有鲁棒性收敛，并强调了实时实现的可行性。

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引用次数: 0

Saturated output feedback control for quadrotor trajectory tracking via fixed-time observers 基于固定时间观测器的四旋翼飞行器轨迹跟踪饱和输出反馈控制

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

Control Engineering Practice

Pub Date : 2026-03-01 Epub Date: 2025-12-18 DOI: 10.1016/j.conengprac.2025.106713

Yuanqing Xia, Min Gong, Dailiang Ma, Ganghui Shen

In this paper, a saturated output feedback trajectory tracking scheme for a quadrotor is developed by using the fixed-time observer (FxTO) technique. The translational controller is examined based on a saturated nonlinear control law, where the unmeasurable velocity states are estimated by the FxTO. The proposed FxTO theoretically guarantees the convergence of the estimation errors in a fixed time, and the stability of the closed-loop system is proved. Next, a cascaded attitude controller is explored, and a feedforward compensation is introduced via the differential flatness approach. Additionally, a fixed-time disturbance observer (FxTDO) is incorporated to improve robustness against disturbances. Finally, the tracking accuracy and robustness of the proposed method are verified through simulations and experiments.

利用固定时间观测器（FxTO）技术，提出了一种四旋翼飞行器饱和输出反馈轨迹跟踪方案。基于饱和非线性控制律对平移控制器进行了检验，其中不可测速度状态由FxTO估计。该方法从理论上保证了估计误差在固定时间内的收敛性，并证明了闭环系统的稳定性。其次，研究了级联姿态控制器，并通过微分平坦度方法引入前馈补偿。此外，还加入了固定时间干扰观测器（FxTDO）来提高对干扰的鲁棒性。最后，通过仿真和实验验证了该方法的跟踪精度和鲁棒性。

引用次数: 0

Lane following with obstacle avoidance for unmanned tracked vehicles using monocular vision and active disturbance rejection control 基于单目视觉和自抗扰控制的无人履带车辆避障车道跟踪

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

Control Engineering Practice

Pub Date : 2026-03-01 Epub Date: 2025-12-27 DOI: 10.1016/j.conengprac.2025.106723

Salem-Bilal Amokrane , Momir Stanković , Rafal Madonski , Benyahia Ahmed Taki-Eddine

This paper presents an integrated system for autonomous lane following with obstacle avoidance in unmanned tracked vehicles (UTVs), combining monocular vision and Active Disturbance Rejection Control (ADRC). A vision-based guidance system is developed using deep learning models: YOLOPv2 for lane segmentation and YOLOv8 for obstacle detection within a dynamic region of interest. Novel lane processing algorithms address partial detections and generate aligned lane boundaries, while a computationally efficient virtual lane generation mechanism enables path planning around obstacles without requiring dedicated depth sensors. To follow the path defined by this guidance system, an ADRC controller is designed for the UTV’s lateral control channel based on a kinematic model, incorporating disturbance estimation via an extended state observer, ensuring robust regulation of lateral path error. The system’s effectiveness is demonstrated through comprehensive experimental validation on a physical UTV platform in two distinct environments: an indoor track with static obstacles and an outdoor setting with both static and dynamic obstacles. Outdoor trials confirm the system’s robustness against real-world challenges, including sloped terrain, varying natural lighting, and multi-colored lane markings. Furthermore, the system successfully navigated around obstacles and critically validated its fail-safe stop logic when the path was fully blocked. Comparative tests against a conventional PID controller quantitatively demonstrate the ADRC’s superior tracking accuracy and disturbance rejection capabilities, highlighting its enhanced robustness in both controlled indoor and unstructured outdoor environments. These results confirm the feasibility of achieving robust lane following and effective obstacle avoidance in UTVs using cost-efficient monocular vision. Supplementary material: https://youtu.be/9aKGugeYmfw?si=qiBCTzi7hYUvwUW6

提出了一种将单目视觉与自抗扰控制相结合的无人履带车辆自动车道跟随避障集成系统。使用深度学习模型开发了基于视觉的制导系统：yolov2用于车道分割，YOLOv8用于感兴趣动态区域内的障碍物检测。新的车道处理算法解决了部分检测并生成对齐的车道边界，而计算效率高的虚拟车道生成机制可以在不需要专用深度传感器的情况下实现绕过障碍物的路径规划。为了遵循该制导系统定义的路径，针对UTV的横向控制通道设计了基于运动学模型的自抗扰控制器，通过扩展状态观测器进行干扰估计，保证了横向路径误差的鲁棒调节。通过在物理UTV平台上的两种不同环境下的综合实验验证，证明了该系统的有效性：室内轨道静态障碍物和室外设置静态和动态障碍物。室外试验证实了该系统对现实世界挑战的稳健性，包括斜坡地形、不同的自然采光和多色车道标记。此外，该系统成功绕过障碍物，并在路径完全堵塞时严格验证了其故障安全停止逻辑。与传统PID控制器的对比测试定量地证明了自抗扰控制器优越的跟踪精度和抗干扰能力，突出了其在受控室内和非结构化室外环境中增强的鲁棒性。这些结果证实了利用低成本的单目视觉在无人驾驶汽车中实现稳健车道跟踪和有效避障的可行性。补充资料：https://youtu.be/9aKGugeYmfw?si=qiBCTzi7hYUvwUW6

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引用次数: 0

A novel discrete sliding mode control based adaptive differentiator for five phase synchronous machines 一种基于离散滑模控制的五相同步电机自适应微分器

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

Control Engineering Practice

Pub Date : 2026-03-01 Epub Date: 2025-12-30 DOI: 10.1016/j.conengprac.2025.106705

Daniel Igbokwe , Malek Ghanes , Marc Bodson , Mohamed Hamida , Amir Messali

This paper introduces a novel discrete sliding-mode control (DSM) strategy for multiphase synchronous machines, supported by an adaptive discrete hybrid filtering differentiator (DHFD). The proposed reaching law significantly enhances transient performance while reducing computational complexity, enabling real-time implementation on low-cost processors. Focusing on five-phase wound-rotor synchronous machines (WRSMs)—a rarely studied configuration in existing literature—the method demonstrates remarkable efficacy in handling transient dynamics and parameter uncertainties. Notably, the control scheme is directly applicable to permanent magnet synchronous machines (PMSMs) and extensible to *n*-phase systems beyond five phases. Experimental hardware-in-the-loop (HIL) and simulation results validate the performance of the approach, showcasing rapid convergence, chattering suppression, and robustness under dynamic loads. By bridging the gap between advanced discrete-time sliding-mode theory and practical implementation for multiphase machines, this work offers a versatile solution for high-performance motor drives in aerospace, automotive, and industrial applications.

提出了一种基于自适应离散混合滤波微分器（DHFD）的多相同步电机离散滑模控制策略。提出的趋近律显著提高了瞬态性能，同时降低了计算复杂度，使低成本处理器上的实时实现成为可能。以五相绕线转子同步电机（wrsm）为研究对象，该方法在处理瞬态动力学和参数不确定性方面表现出显著的有效性。值得注意的是，该控制方案可直接适用于永磁同步电机（pmms），并可扩展到五相以上的*n*相系统。实验结果和仿真结果验证了该方法在动态负载下的快速收敛、抖振抑制和鲁棒性。通过弥合先进的离散时间滑模理论与多相电机的实际实现之间的差距，这项工作为航空航天，汽车和工业应用中的高性能电机驱动提供了一种通用解决方案。

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引用次数: 0