Control Engineering Practice最新文献_第3页

Electrically actuated control system for the stabilization of synchrotron X-ray beams 同步加速器x射线光束稳定的电动控制系统

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

Control Engineering Practice

Pub Date : 2026-01-31 DOI: 10.1016/j.conengprac.2026.106805

Niccolò La Rosa , Samuele Moscato , Luigi Fortuna , Maide Bucolo , Massimo Camarda

Maintaining sub-micrometer stability of synchrotron X-ray beams is essential for the accuracy and repeatability of cutting-edge scientific and medical experiments. Traditional beamline stabilization systems, based on mechanical actuation of optical elements, are inherently limited in speed due to physical constraints like friction and inertia. This study introduces an innovative control strategy based on electrical actuation, directly influencing the bending magnet responsible for steering the beam into the beamline. This approach unlocks the potential for significantly higher control frequencies, comparable to those used for the electron beam stabilization. A laboratory-scale replica was developed to validate the feasibility and robustness of this method. A Proportional-Integral (PI) controller has been implemented to stabilize the electron beam and compensate for disturbances. Experimental results demonstrate that this strategy enables precise, high-frequency beam stabilization, even in the presence of typical disturbances such as position drift occurring during X-Ray Absorption Spectroscopy (XAS) experiments. This work lays the groundwork for next-generation control systems in synchrotron facilities, aiming to enhance performance and open the door to more advanced experimental capabilities.

保持同步加速器x射线光束的亚微米稳定性对于尖端科学和医学实验的准确性和可重复性至关重要。传统的光束稳定系统基于光学元件的机械驱动，由于摩擦和惯性等物理约束，其速度本身就受到限制。本研究介绍了一种基于电驱动的创新控制策略，直接影响负责将光束转向光束线的弯曲磁铁。这种方法开启了显著提高控制频率的潜力，可与用于电子束稳定的频率相媲美。为了验证该方法的可行性和鲁棒性，开发了实验室规模的副本。采用比例积分（PI）控制器来稳定电子束并补偿干扰。实验结果表明，即使在x射线吸收光谱（XAS）实验中出现典型的干扰（如位置漂移），该策略也能实现精确的高频光束稳定。这项工作为同步加速器设施中的下一代控制系统奠定了基础，旨在提高性能并为更先进的实验能力打开大门。

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引用次数: 0

Disturbance-rejection pressure control for integrated brake system based on improved non-singular fast terminal sliding mode 基于改进非奇异快速终端滑模的综合制动系统抗扰压力控制

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

Control Engineering Practice

Pub Date : 2026-01-30 DOI: 10.1016/j.conengprac.2026.106811

Jian Zhao, Ruijie Dang, Bing Zhu, Zhicheng Chen, Jiayi Han, Peixing Zhang, Dongjian Song, Shizheng Jia

Integrated brake system (IBS) is a critical component of intelligent electric vehicle electronics. However, the pressure control of IBS is usually affected by lumped disturbance such as friction uncertainties, time-varying hydraulic characteristics and unmodeled dynamics, which present significant challenges to the pressure tracking. In order to achieve high-precision, fast-response, and robust pressure tracking performance, this article proposes a disturbance-rejection pressure control strategy. First, to improve the response rate, a non-singular fast terminal sliding mode control (NFTSMC) with finite-time convergence is applied in the basic pressure regulator. Subsequently, a super-twisting algorithm is used to reduce the control chattering in NFTSMC and enhance pressure tracking accuracy. On this basis, we design a finite-time extended state observer to estimate the lumped disturbance, which is then integrated into the NFTSMC to maintain the robustness with small control gains. This integration also reconciles the contradiction between control chattering and robustness in the NFTSMC. The finite-time convergence of the proposed strategy is rigorously validated during both the reaching and sliding phases of sliding mode control. Finally, hardware-in-the-loop experiments are performed. The experimental results demonstrate that compared to the baseline, the proposed strategy achieves at least a 28% improvement in pressure-tracking root mean square error and maximum error.

集成制动系统（IBS）是智能电动汽车电子系统的重要组成部分。然而，IBS的压力控制通常受到摩擦不确定性、时变水力特性和未建模动力学等集总扰动的影响，这给压力跟踪带来了重大挑战。为了实现高精度、快速响应和鲁棒的压力跟踪性能，本文提出了一种抗扰压力控制策略。首先，为了提高响应速率，在基本压力调节器中采用有限时间收敛的非奇异快速终端滑模控制（NFTSMC）。在此基础上，采用超扭转算法降低了NFTSMC的控制抖振，提高了压力跟踪精度。在此基础上，我们设计了一个有限时间扩展状态观测器来估计集总扰动，然后将其集成到NFTSMC中以保持小控制增益的鲁棒性。这种集成也调和了NFTSMC中控制抖振与鲁棒性之间的矛盾。在滑模控制的到达和滑动阶段严格验证了所提策略的有限时间收敛性。最后，进行了硬件在环实验。实验结果表明，与基线相比，该策略在压力跟踪均方根误差和最大误差方面至少提高了28%。

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引用次数: 0

Dual-stage recognition framework for open-set fault diagnosis in rotating machinery considering varying inter-class similarity 考虑类间相似度变化的旋转机械开集故障诊断双阶段识别框架

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

Control Engineering Practice

Pub Date : 2026-01-30 DOI: 10.1016/j.conengprac.2026.106808

Penglong Lian , Penghui Shang , Jianxiao Zou , Shicai Fan

Open-set fault diagnosis in rotating machinery is critically hindered by substantial inter-class similarity between unknown and known fault classes, leading to unreliable recognition. Although significant advances have been made using various adaptation and classification techniques, current open-set methods still struggle to resolve fine-grained distinctions and class ambiguities in open-set environments, often resulting in misclassifications and higher maintenance costs. To address these challenges, we propose an adaptive dual-stage framework that integrates a novel tri-branch network and dynamic contrastive learning (Ds-TBN). Specifically, the tri-branch network integrates a base feature branch, a similarity-sensitive branch, and a global feature enhancement branch to collaboratively extract complementary and discriminative representations. Dynamic contrastive learning is then applied to enforce intra-class compactness and explicitly enhance inter-class separability, significantly improving feature discriminability. Building on these enhanced representations, the dual-stage recognition framework first utilizes an adaptive Weibull distribution to detect boundary outliers for accurate identification of unknown fault classes. Subsequently, the second stage further refines classification probabilities using a meta-recognition module, adaptively resolving ambiguities between highly similar known and unknown faults. Extensive experiments across diverse similarity-based open-set diagnostic tasks on the CWRU, Gearbox, and our self-developed Drivetrain Prognostics Simulator (DPS) test bench show that the proposed method Ds-TBN achieves average H-scores of 96.65%, 90.43%, and 93.58%, respectively. These results significantly surpass existing approaches and highlight the framework’s robustness and practical applicability for real-world industrial fault diagnosis.

旋转机械的开集故障诊断受到未知和已知故障类间大量相似性的严重阻碍，导致识别不可靠。尽管使用各种适应和分类技术取得了重大进展，但目前的开放集方法仍然难以解决开放集环境中的细粒度差异和类歧义，这往往导致错误分类和更高的维护成本。为了应对这些挑战，我们提出了一种自适应双阶段框架，该框架集成了一种新的三分支网络和动态对比学习（Ds-TBN）。具体来说，三分支网络集成了一个基本特征分支、一个相似敏感分支和一个全局特征增强分支，以协同提取互补和区分表示。然后应用动态对比学习来增强类内紧密性和显式增强类间可分离性，显著提高特征可判别性。在这些增强表征的基础上，双阶段识别框架首先利用自适应威布尔分布来检测边界异常值，以准确识别未知故障类别。随后，第二阶段使用元识别模块进一步细化分类概率，自适应地解决高度相似的已知和未知故障之间的歧义。在CWRU、Gearbox和我们自主开发的动力传动系统预测模拟器（DPS）测试台上进行的各种基于相似性的开放集诊断任务的大量实验表明，所提出的方法Ds-TBN的平均h分数分别为96.65%、90.43%和93.58%。这些结果大大超越了现有的方法，突出了该框架在实际工业故障诊断中的鲁棒性和实用性。

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引用次数: 0

A deep reinforcement learning exploration method based on motion cost rewards 一种基于运动代价奖励的深度强化学习探索方法

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

Control Engineering Practice

Pub Date : 2026-01-29 DOI: 10.1016/j.conengprac.2026.106793

Chuang Chen , Weifeng Liu , Meng Zhou , Lei Cai

In large-scale unknown water surface environment exploration, hierarchical exploration methods are an effective way to reduce computational overhead. However, existing hierarchical exploration methods suffer from low trajectory quality and poor feasibility, leading to low autonomous exploration efficiency of USVs (Unmanned Surface Vehicles). Therefore, this paper proposes a deep reinforcement learning exploration method based on motion cost rewards. This method jointly optimizes the decision-making process and motion planning. The motion cost of each trajectory segment of the USV is calculated using an analytical method, enabling the policy network to take into account both exploration efficiency and trajectory feasibility during the decision-making process. Finally, nonlinear model predictive control (NMPC) is used for trajectory tracking control. Simulation and real-world experimental results show that the proposed method achieves better performance in terms of exploration efficiency and path cost.

在大规模未知水面环境勘探中，分层勘探方法是减少计算量的有效方法。然而，现有分层探测方法存在轨迹质量低、可行性差的问题，导致无人水面车辆自主探测效率不高。因此，本文提出了一种基于运动代价奖励的深度强化学习探索方法。该方法对决策过程和运动规划进行了联合优化。利用解析法计算USV各轨迹段的运动代价，使策略网络在决策过程中兼顾勘探效率和轨迹可行性。最后，采用非线性模型预测控制（NMPC）进行轨迹跟踪控制。仿真和实际实验结果表明，该方法在勘探效率和路径成本方面取得了较好的效果。

引用次数: 0

Nash bargaining for power-fatigue co-optimization in wake-affected wind farms: A learning-aided approach 受尾流影响的风电场电力疲劳协同优化的纳什议价：一种学习辅助方法

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

Control Engineering Practice

Pub Date : 2026-01-29 DOI: 10.1016/j.conengprac.2026.106809

Yiming Liu , Zhaojian Wang , Ruanming Huang , Bo Yang

This paper proposes a multi-objective control framework for wake-affected wind farms to manage the trade-off between power maximization and fatigue load minimization. The conflicting objectives are formulated using Nash bargaining theory, providing a fair, Pareto-efficient solution without heuristic weight tuning. A Warm-started Proximal Alternating Direction Method of Multipliers (W-PADMM) algorithm is proposed to efficiently solve the bargaining problem, which embeds a learning-aided mechanism using a Long Short-Term Memory (LSTM) network to proactively guide the optimization. Case studies on both an illustrative 9-turbine system and a real offshore wind farm under seasonally varying wind conditions demonstrate that the proposed W-PADMM approach achieves an improved power-fatigue trade-off together with substantial computational acceleration.

本文提出了一个尾迹风电场的多目标控制框架，以实现功率最大化和疲劳负荷最小化之间的平衡。相互冲突的目标是使用纳什议价理论制定的，提供了一个公平的，帕累托有效的解决方案，没有启发式的权重调整。为了有效地解决议价问题，提出了一种暖启动近邻交替方向乘数法（W-PADMM）算法，该算法嵌入了一种学习辅助机制，利用长短期记忆（LSTM）网络主动引导优化。在季节性变化的风力条件下，对一个示范性的9涡轮机系统和一个实际的海上风电场的案例研究表明，所提出的W-PADMM方法实现了改进的功率疲劳权衡以及可观的计算加速。

引用次数: 0

Intelligent compensation for uncertain time delay in vehicle magnetorheological suspension control using predictive experience 基于预测经验的汽车磁流变悬架不确定时滞智能补偿

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

Control Engineering Practice

Pub Date : 2026-01-28 DOI: 10.1016/j.conengprac.2026.106796

Liu Zhan , Xiaowei Xu , Zian Bai , Xiaofeng Guo , Mingxing Deng , Yingxue Zou

Aiming at the deterioration of ride comfort caused by uncertain time delay of magnetorheological (MR) damper, a feedforward-feedback collaborative mode is proposed by integrating Long Short-Term Memory (LSTM) and Deep Reinforcement Learning (DRL) to alleviate time delay and optimize damping effect. Firstly, fuzzy Linear Quadratic Regulator algorithm is employed to simulate and control an active suspension to obtain the ideal control state information without time delay, and the LSTM is developed and trained using the ideal state information to establish the prediction model based on ideal experience; Secondly, within the Soft Actor-Critic (SAC), the prediction model is utilized to predict real-time observations, yielding predicted values for next state. Relevant experience is added to replay buffer of DRL, and the reward item of prediction error is introduced to obtain a SAC algorithm with Predictive Experience Guidance (SAC-PEG). Finally, the results of passive suspension, Proximal Policy Optimization, SAC, Twin Delayed Deep Deterministic Policy Gradient and SAC-PEG are compared by simulations and bench experiments. The simulations demonstrate that body acceleration controlled by SAC-PEG is 25.52 % lower than that of passive suspension, and suspension working space and tire dynamic load are increased by 90.59 % and 66.35 %; Compared with SAC, when suspension working space and tire dynamic load are only deteriorated by 7.956 % and 5.440 %, body acceleration is optimized by 4.143 %. Bench experiment also achieved satisfactory results. The results validated that SAC-PEG has better mitigation effect on uncertain time delay than other comparative methods, and can improve the smoothness problem caused by uncertain time delay.

针对磁流变阻尼器时滞不确定导致的平顺性恶化问题，提出了一种将长短期记忆（LSTM）和深度强化学习（DRL）相结合的前馈-反馈协同模式，以缓解时滞，优化阻尼效果。首先，采用模糊线性二次型调节器算法对主动悬架进行仿真控制，获得无时滞的理想控制状态信息，利用理想状态信息开发和训练LSTM，建立基于理想经验的预测模型；其次，在软Actor-Critic （SAC）中，利用预测模型对实时观测值进行预测，得到下一状态的预测值。在DRL的重播缓冲区中加入相关经验，并引入预测误差奖励项，得到带有预测经验指导（SAC- peg）的SAC算法。最后，通过仿真和台架实验对被动悬架、近端策略优化、SAC、双延迟深度确定性策略梯度和SAC- peg的结果进行了比较。仿真结果表明，与被动悬架相比，SAC-PEG控制的车身加速度降低了25.52%，悬架工作空间和轮胎动载荷分别提高了90.59%和66.35%；与SAC相比，悬架工作空间和轮胎动载荷分别恶化7.956%和5.440%时，车身加速度优化了4.143%。台架实验也取得了满意的结果。结果验证了SAC-PEG对不确定时延的缓解效果优于其他比较方法，可以改善不确定时延带来的平滑问题。

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引用次数: 0

Robust electro-hydraulic control for aircraft anti-skid systems with full validation from test bench to flight 稳健的电液控制飞机防滑系统，从试验台到飞行的全面验证

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

Control Engineering Practice

Pub Date : 2026-01-28 DOI: 10.1016/j.conengprac.2026.106814

José Joaquín Mendoza Lopetegui, Mara Tanelli

In modern aviation, anti-skid systems are fundamental in preventing wheel-locking conditions and maximizing braking performance. To achieve airworthiness, these systems must be robust, fault-tolerant, and comply with existing standards and regulations. Existing solutions fall short in addressing important aspects for a successful practical implementation, as testified by the lack of flight testing verification in the literature. This paper proposes a novel aircraft anti-skid system that leverages robust control techniques to enhance safety and performance. The proposed architecture integrates a fault-tolerant design that accounts for measurement noise, hydraulic system asymmetries, and pressure transducer faults, while maintaining stability despite uncertainties in the electro-hydraulic brake dynamics. A cascaded control structure combining robust pressure regulation with wheel deceleration control and supervisory logic enables resilient performance under varying operating conditions. The pressure controller’s stability is verified by a Kharitonov-type stability check, whereas the proposed gain-scheduled deceleration controller is analyzed under a Linear Parameter-Varying system formulation, checked for stability by a collection of Linear Matrix Inequalities under assumptions of rate-bounded variability of the involved parameters. The approach is validated on a hydraulic test bench, an aeronautic dynamometer, and flight test experiments, demonstrating practical applicability and alignment with the demands of modern hydraulic control systems.

在现代航空中，防滑系统是防止轮锁状况和最大化制动性能的基础。为了实现适航性，这些系统必须具有鲁棒性、容错性，并符合现有的标准和法规。现有的解决方案在解决成功的实际实施的重要方面不足，正如文献中缺乏飞行测试验证所证明的那样。本文提出了一种新的飞机防滑系统，利用鲁棒控制技术来提高安全性和性能。该架构集成了容错设计，考虑了测量噪声、液压系统不对称和压力传感器故障，同时在电液制动动力学不确定的情况下保持稳定性。级联控制结构将鲁棒压力调节与车轮减速控制和监督逻辑相结合，使其在不同的操作条件下具有弹性性能。通过kharitonov稳定性检验验证了压力控制器的稳定性，而在线性变参数系统公式下分析了所提出的增益调度减速控制器，并在相关参数的速率有界可变性假设下通过线性矩阵不等式的集合检查了稳定性。该方法在液压试验台、航空测功机和飞行试验中得到了验证，证明了该方法的实用性和符合现代液压控制系统的要求。

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引用次数: 0

An enhanced quality prediction method incorporating operator interventions via dual-branch feature extraction and its application to a hot strip rolling mill process 基于双分支特征提取的质量预测方法及其在热连轧过程中的应用

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

Control Engineering Practice

Pub Date : 2026-01-28 DOI: 10.1016/j.conengprac.2026.106807

Kai Zhang , Yali Wang , Kaixiang Peng

In the hot strip rolling mill (HSRM) process, the steel quality indices that serve as important performance indicators should be precisely predicted. Most existing methods are developed either based on process mechanisms or solely considering ordinary process variables (OPVs), which may perform poorly for cases with operator interventions. To address this problem, this paper proposes an enhanced quality prediction method by incorporating various operator interventions’ information. Firstly, the inter-variable and temporal features of OPVs are captured using a gated recurrent unit (GRU) combined with the attention mechanism. Secondly, the abrupt change features and trend features of operator intervention variables (OIVs) are extracted via a dual-branch convolution neural network (CNN) and the gating mechanism, which are guided by operator intervention types. Then, the features extracted from OIVs further induce the output of the GRU in the OPV feature extraction part through an inductive mechanism, and both features extracted from both OPVs and OIVs are finally fused to construct the quality prediction model. The proposed method is trained, validated, and tested using actual HSRM data that cover different operator intervention cases and various strip steels. It is shown from the experiment results that compared with those without considering OIVs and transformer-based methods, this method can decrease the prediction error of the steel crown by 19.48%, and the prediction-hit rate can reach 94% when operator interventions occur. The applicability is further examined using a cloud-edge-end prototype system with real-time HSRM process data, which shows that the real-time performance can be achieved.

在热连轧过程中，作为重要性能指标的钢材质量指标需要进行准确的预测。大多数现有方法要么是基于过程机制开发的，要么仅仅考虑了普通过程变量（opv），这可能在操作员干预的情况下表现不佳。为了解决这一问题，本文提出了一种结合各种作业者干预信息的增强质量预测方法。首先，利用门控循环单元（GRU）结合注意机制捕获opv的变量间和时间特征；其次，以算子干预类型为导向，通过双分支卷积神经网络（CNN）和门控机制提取算子干预变量（OIVs）的突变特征和趋势特征；然后，从OIVs中提取的特征通过归纳机制进一步诱导出OPV特征提取部分的GRU输出，最后将从OPV和OIVs中提取的特征融合构建质量预测模型。所提出的方法经过了训练、验证，并使用实际的HSRM数据进行了测试，这些数据涵盖了不同的操作人员干预案例和各种带钢。实验结果表明，与不考虑OIVs和基于变压器的方法相比，该方法可将钢冠的预测误差降低19.48%，当有操作员干预时，预测命中率可达94%。利用云边缘原型系统实时HSRM过程数据进一步验证了该方法的适用性，结果表明该方法能够实现实时性。

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引用次数: 0

Fault detection for time-variant avionics systems based on a new data-driven time-varying approach 基于数据驱动时变方法的时变航空电子系统故障检测

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

Control Engineering Practice

Pub Date : 2026-01-27 DOI: 10.1016/j.conengprac.2026.106802

Lidong Lu , Jialiang Wang , Bing Wang , Wenxin Sun , Yiyang Chen , Kangkang Sun , Zhichao Feng , Hongtian Chen

Efficient fault detection and diagnosis are crucial aspects for avionics systems of large aircraft. Traditional model-based approaches encounter significant challenges due to the complexity of system modeling and the presence of time-varying factors in avionics systems. To address the problem, this paper proposes a fault detection (FD) approach suitable for time-varying avionics systems. The proposed approach constructs an optimal observer using a time-varying state space model. It establishes the relationship between time-varying factors and model parameters using neural networks. The expectation-maximization (EM) algorithm is then employed to optimize these parameters, including those of neural networks. The proposed method is a data-driven fault detection algorithm because its implementations rely on system input and output data. Furthermore, both the sufficient and necessary conditions for this design are provided. Validations on real flight data show that this approach demonstrates excellent efficacy in the timely detection of faults, providing valuable support for predictive maintenance management.

高效的故障检测与诊断是大型飞机航电系统的关键环节。由于航空电子系统建模的复杂性和时变因素的存在，传统的基于模型的方法面临着巨大的挑战。针对这一问题，本文提出了一种适用于时变航空电子系统的故障检测方法。该方法利用时变状态空间模型构造最优观测器。利用神经网络建立时变因素与模型参数之间的关系。然后采用期望最大化算法对这些参数进行优化，包括神经网络的参数。该方法的实现依赖于系统输入和输出数据，是一种数据驱动的故障检测算法。并给出了设计的充分条件和必要条件。对实际飞行数据的验证表明，该方法在及时发现故障方面具有优异的效果，为预测性维修管理提供了有价值的支持。

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引用次数: 0

Adaptive speed control strategy for the high-pressure pump in a ground-based hydraulic wind turbine-driven reverse osmosis seawater desalination system 陆基风力发电反渗透海水淡化系统高压泵自适应调速策略研究

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

Control Engineering Practice

Pub Date : 2026-01-27 DOI: 10.1016/j.conengprac.2026.106801

Guisheng Yang , Wei Gao , Lijuan Chen , Weiyi Li , Chao Ai , Xiangdong Kong

Maintaining a constant speed for the high-pressure pump in wind-driven reverse osmosis (RO) desalination systems is critically challenging due to the stochastic nature of wind energy and inherent system nonlinearities. This instability hinders the efficient and reliable operation of RO membranes. While Hydraulic Wind Turbines (HWTs) offer a flexible transmission solution, their coupling with RO systems demands a robust control strategy that can handle parametric uncertainties and external disturbances. This paper proposes an Adaptive Backstepping Sliding Mode Control (ABSMC) strategy to achieve precise and robust speed regulation. The key innovation of ABSMC lies in its synergistic combination of the recursive design structure of backstepping for guaranteed transient performance, the inherent robustness of sliding mode control against disturbances, and online adaptive laws to estimate and compensate for system uncertainties without requiring prior knowledge of their bounds. The primary design challenge was to formulate the control law and Lyapunov function to ensure global stability while mitigating the chattering phenomenon commonly associated with sliding mode control. The closed-loop system’s asymptotic stability is rigorously proven using Lyapunov theory. Simulation results demonstrate the ABSMC strategy’s superiority over conventional PID control, reducing response time by 18.8 s and exhibiting significantly superior performance in robustness, dynamic response, and steady-state accuracy, while experimental validation on a 30-kW HWT-RO platform confirms its practical efficacy. The findings confirm the feasibility of the proposed method in maintaining stable RO system operation under wind speed fluctuations, providing an effective and intelligent control solution for hydraulic wind turbine-driven desalination systems.

由于风能的随机性和固有的系统非线性，在风力驱动的反渗透（RO）海水淡化系统中，保持高压泵的恒定转速是一项极具挑战性的工作。这种不稳定性阻碍了反渗透膜的高效可靠运行。虽然水力风力涡轮机（hwt）提供了灵活的传输解决方案，但它们与RO系统的耦合需要一个强大的控制策略，可以处理参数不确定性和外部干扰。本文提出了一种自适应反步滑模控制（ABSMC）策略，以实现精确和鲁棒的调速。ABSMC的关键创新在于它将保证暂态性能的递推设计结构、滑模控制对干扰的固有鲁棒性和在线自适应律相结合，以估计和补偿系统的不确定性，而不需要事先知道它们的边界。主要的设计挑战是制定控制律和Lyapunov函数，以确保全局稳定性，同时减轻滑模控制中常见的抖振现象。利用李雅普诺夫理论严格证明了闭环系统的渐近稳定性。仿真结果表明，ABSMC策略优于传统PID控制，响应时间缩短18.8 s，在鲁棒性、动态响应和稳态精度方面表现出明显的优势，在30kw HWT-RO平台上的实验验证证实了其实际有效性。研究结果证实了该方法在风速波动下保持RO系统稳定运行的可行性，为水力风力涡轮机驱动的海水淡化系统提供了一种有效的智能控制解决方案。

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引用次数: 0