IFAC Journal of Systems and Control最新文献_第7页

A comparative analysis of PPO and SAC algorithms for energy optimization with country-level energy consumption insights 能源优化的PPO和SAC算法与国家级能源消耗洞察的比较分析

IF 1.8 Q3 AUTOMATION & CONTROL SYSTEMS

IFAC Journal of Systems and Control

Pub Date : 2025-12-01 Epub Date: 2025-10-03 DOI: 10.1016/j.ifacsc.2025.100344

Enes Bajrami, Andrea Kulakov, Eftim Zdravevski, Petre Lameski

Background:

This study addresses national-scale energy optimization using deep reinforcement learning. Unlike prior works that rely on simulated environments or synthetic datasets, this research integrates real-world energy indicators, including electricity generation, greenhouse gas emissions, renewable energy share, fossil fuel dependency, and oil consumption. These indicators, sourced from the World Energy Consumption dataset, capture both developed and developing energy systems, enabling the evaluation of intelligent control policies across diverse contexts.

Methodology:

Two advanced algorithms, Proximal Policy Optimization (PPO) and Soft Actor–Critic (SAC), were implemented and trained using PyTorch across multi-phase evaluation runs (300–3000 episodes). Comparative performance analysis was conducted on key metrics: execution speed, action consistency, and reward optimization. A secondary regional analysis focused on contrasting the Balkan and Nordic countries to evaluate algorithm adaptability between highly developed and developing energy infrastructures.

Significant findings:

SAC demonstrated superior computational throughput and policy stability, making it suitable for real-time and resource-constrained environments. PPO exhibited stronger action magnitudes, enabling more assertive control signals for high-impact interventions. Both agents significantly outperformed a rule-based baseline in responsiveness and adaptability. The proposed framework represents a novel contribution by combining deep reinforcement learning with interpretable, country-level energy indicators. Future work will extend the evaluation to additional continents, including Asia, Africa, and South America, to assess global scalability and applicability.

背景：本研究利用深度强化学习解决了全国范围的能源优化问题。与以往依赖于模拟环境或合成数据集的工作不同，本研究整合了现实世界的能源指标，包括发电量、温室气体排放、可再生能源份额、化石燃料依赖和石油消耗。这些指标来自世界能源消费数据集，涵盖了发达和发展中国家的能源系统，从而能够在不同背景下评估智能控制政策。方法：两种先进的算法，近端策略优化（PPO）和软行为者批评家（SAC），在多阶段评估运行（300-3000集）中使用PyTorch实现和训练。在执行速度、行动一致性和奖励优化等关键指标上进行了比较绩效分析。第二项区域分析侧重于对比巴尔干和北欧国家，以评估高度发达和发展中国家能源基础设施之间的算法适应性。重大发现：SAC展示了卓越的计算吞吐量和策略稳定性，使其适用于实时和资源受限的环境。PPO表现出更强的行动幅度，为高影响干预提供了更自信的控制信号。两种代理在响应性和适应性方面都明显优于基于规则的基线。提出的框架通过将深度强化学习与可解释的国家级能量指标相结合，代表了一种新的贡献。未来的工作将把评估扩展到其他大陆，包括亚洲、非洲和南美洲，以评估全球可扩展性和适用性。

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引用次数: 0

Application of LiDAR and neuromorphic vision in Ambient Assisted Living environments 激光雷达和神经形态视觉在环境辅助生活环境中的应用

IF 1.8 Q3 AUTOMATION & CONTROL SYSTEMS

IFAC Journal of Systems and Control

Pub Date : 2025-12-01 Epub Date: 2025-09-30 DOI: 10.1016/j.ifacsc.2025.100347

Niklas Huhs, Niloofar Kalashtari , Jens Kraitl, Christoph Hornberger, Olaf Simanski

Continuous and non-invasive patient monitoring is essential in healthcare, particularly within Ambient Assisted Living (AAL) environments, to enhance safety and acceptance while preserving privacy. This work investigates two complementary approaches for patient monitoring. In the first approach, a Light Detection and Ranging (LiDAR)-based system was developed to detect and track human subjects in a room using a fine-tuned You Only Look Once, version 5 (YOLOv5) deep learning model. Thanks to LiDAR’s precision and depth sensing capabilities, the system enables live tracking of multiple individuals under varying lighting conditions while safeguarding patient privacy. When the position of the patients in the room is known, the second approach is relevant. A neuromorphic camera, which has a more limited field of view in the room, was employed to measure vital signs such as respiration rate and heart rate by capturing subtle chest movements and micro-vibrations induced by blood circulation. A study involving 26 participants was conducted, with measurements taken at distances ranging from 0.5 metres to 2 metres as well as before and after exercise tasks, consisting of light jogging on a treadmill. Reference data were collected using a Powerlab 15T system equipped with a three-point ECG and a respiration belt. The neuromorphic camera-based measurements demonstrated promising accuracy, validating the feasibility of the approach. Overall, these combined systems offer a contact-free, privacy-preserving solution for continuous patient monitoring, addressing challenges such as limited healthcare staffing, infection control, and the need for vital parameter online tracking in AAL environments.

在医疗保健中，特别是在环境辅助生活（AAL）环境中，持续和非侵入性的患者监测对于提高安全性和接受度，同时保护隐私至关重要。这项工作调查了两种互补的病人监测方法。在第一种方法中，开发了基于光探测和测距（LiDAR）的系统，使用经过微调的You Only Look Once， version 5 （YOLOv5）深度学习模型来检测和跟踪房间中的人类受试者。由于激光雷达的精度和深度传感能力，该系统可以在不同的照明条件下实时跟踪多个个体，同时保护患者的隐私。当病人在房间里的位置是已知的，第二种方法是相关的。神经形态相机在房间内的视野更有限，通过捕捉微妙的胸部运动和血液循环引起的微振动来测量呼吸率和心率等生命体征。研究人员对26名参与者进行了研究，测量了他们在0.5米到2米之间的距离，以及在锻炼任务（包括在跑步机上慢跑）之前和之后的运动量。参考数据的收集使用配备有三点心电图和呼吸带的Powerlab 15T系统。基于神经形态相机的测量显示出良好的准确性，验证了该方法的可行性。总的来说，这些组合系统为患者的持续监测提供了一种无接触、保护隐私的解决方案，解决了诸如有限的医疗人员、感染控制以及AAL环境中对重要参数在线跟踪的需求等挑战。

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引用次数: 0

A physics-informed LSTM framework with lag compensation for coupled vibration signal modeling 耦合振动信号建模的时滞补偿LSTM框架

IF 1.8 Q3 AUTOMATION & CONTROL SYSTEMS

IFAC Journal of Systems and Control

Pub Date : 2025-12-01 Epub Date: 2025-11-10 DOI: 10.1016/j.ifacsc.2025.100348

Xinwei Sun , Lei Zhang

Investigating vibration signals in complex electromechanical systems is essential for improving system stability and control performance. This study proposes a data–physics dual-driven framework to model the dynamic coupling between suspension current and levitation gap in maglev systems. A joint time–frequency analysis is first conducted using Fourier transform, ripple coefficient evaluation, and hysteresis correlation to quantify nonlinear coupling strength and identify a positively lagged relationship between current and gap. To capture this effect, we develop a physics-informed neural network (PINN) that integrates a lag compensation module, embeds electromagnetic equations as physical constraints, and employs an LSTM architecture for end-to-end vibration signal prediction. Unlike conventional approaches that design neural controllers from a control perspective, our method focuses on learning intrinsic coupling patterns directly from real-world operational data. This data-informed modeling approach, enhanced with time-delay compensation and physical consistency, enables accurate prediction of dynamic responses under realistic disturbances. Experiments on data from the Changsha medium-low-speed maglev train show that our model achieves the lowest MAE and RMSE compared to standard PINNs and purely data-driven baselines. It also responds rapidly to gap changes, with a response time of 0.167 ms, making it suitable for real-time maglev control applications. The implementation code is available at: https://github.com/sunning2024/RPinn.

研究复杂机电系统中的振动信号对提高系统稳定性和控制性能至关重要。本文提出了一个数据物理双驱动框架来模拟磁悬浮系统中悬浮电流和悬浮间隙之间的动态耦合。首先使用傅里叶变换、纹波系数评估和滞后相关性进行联合时频分析，以量化非线性耦合强度，并确定电流和间隙之间的正滞后关系。为了捕捉这种效应，我们开发了一种物理信息神经网络（PINN），该网络集成了滞后补偿模块，将电磁方程嵌入为物理约束，并采用LSTM架构进行端到端振动信号预测。与从控制角度设计神经控制器的传统方法不同，我们的方法侧重于直接从现实世界的操作数据中学习内在耦合模式。这种基于数据的建模方法，增强了时延补偿和物理一致性，能够准确预测现实干扰下的动态响应。长沙中低速磁悬浮列车数据实验表明，与标准pinn和纯数据驱动基线相比，我们的模型获得了最低的MAE和RMSE。它对间隙变化的响应也很快，响应时间为0.167 ms，适用于实时磁悬浮控制应用。实现代码可从https://github.com/sunning2024/RPinn获得。

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引用次数: 0

3D-Scan hole detection for robot-assisted laparoscopic surgery 机器人辅助腹腔镜手术的3d扫描孔检测

IF 1.8 Q3 AUTOMATION & CONTROL SYSTEMS

IFAC Journal of Systems and Control

Pub Date : 2025-12-01 Epub Date: 2025-09-09 DOI: 10.1016/j.ifacsc.2025.100336

Birthe Göbel , Alexander Richter , Stefan J. Rupitsch , Alexander Reiterer , Knut Möller

Minimally invasive laparoscopic surgery, where endoscopes and instruments are inserted through small incisions, has advanced to the next stage of development: robot-assisted minimally invasive surgery. These systems use a camera and robotic manipulators operated by human surgeons through human-in-the-loop control. To further improve surgical precision and autonomy, data-driven assistance must be expanded. One promising approach is 3D reconstruction based on endoscopic images. A 30°endoscope tip enhances the field of view by enabling rotational motion around the instrument’s axis. However, when performing a 3D scan with such an endoscope, a blind spot inherently forms along the shaft axis, creating a region that cannot be captured during rotation. Additional missing data may arise due to occlusions from anatomical geometry and the specific endoscope pose during a scan. These limitations result in incomplete 3D reconstructions, which can negatively impact surgical navigation and decision-making. This paper presents a method tailored to medical applications for detecting and characterizing holes in laparoscopic 3D scans. The proposed method uses geometric analysis of the point cloud to identify regions of sparse or missing data and correlates these gaps with endoscope positioning and anatomical visibility. It is designed to operate robustly on high-density point clouds generated by advanced laparoscopic 3D reconstruction systems. By integrating robotic control, our method provides a foundation for adaptive endoscope repositioning to recover missing views and improve reconstruction completeness. The proposed method paves the way towards fast (

\sim

5 s) feedback for optimized 3D scanning in laparoscopic environments.

微创腹腔镜手术，内窥镜和器械通过小切口插入，已经进入下一个发展阶段：机器人辅助微创手术。这些系统使用一个摄像头和由人类外科医生通过人在环控制操作的机器人操纵器。为了进一步提高手术的精确性和自主性，必须扩大数据驱动的辅助。一种很有前途的方法是基于内窥镜图像的3D重建。30°内窥镜尖端通过围绕仪器轴的旋转运动来增强视野。然而，当使用这种内窥镜进行3D扫描时，固有的盲点沿着轴轴形成，在旋转过程中产生无法捕获的区域。由于解剖几何和扫描期间特定内窥镜姿势的闭塞，可能会出现额外的丢失数据。这些限制导致三维重建不完整，对手术导航和决策产生负面影响。本文提出了一种适合于医学应用的方法，用于检测和表征腹腔镜3D扫描中的孔。该方法利用点云的几何分析来识别数据稀疏或缺失的区域，并将这些空白与内窥镜定位和解剖可见性相关联。它被设计成在由先进的腹腔镜三维重建系统产生的高密度点云上稳健地运行。该方法结合机器人控制，为内窥镜自适应定位提供了基础，以恢复缺失视图，提高重建的完整性。所提出的方法为在腹腔镜环境中优化3D扫描的快速（~ 5秒）反馈铺平了道路。

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引用次数: 0

Model-based and Large Language Model Meta Artificial Intelligence techniques for intelligent permanent magnet synchronous motor drive control 智能永磁同步电机驱动控制的基于模型和大语言模型元人工智能技术

IF 1.8 Q3 AUTOMATION & CONTROL SYSTEMS

IFAC Journal of Systems and Control

Pub Date : 2025-12-01 Epub Date: 2025-10-20 DOI: 10.1016/j.ifacsc.2025.100341

Javier Urquizo, Patricia Pasmay, Luis Muñoz, Luis Galarza

Permanent magnet synchronous motors play a critical role in modern applications, particularly in the electrification of transportation. Their high energy efficiency and ability to maintain constant power over a wide speed range make them ideal for high-speed trains and electric vehicles. This research explores advanced control strategies, including Field oriented control (FOC), voltage droop control (Vdroop), and dispatchable virtual oscillator control (dVOC), implemented using the Texas Instruments microcontroller development kit, the Boost inverter, and the conventional platform. Furthermore, supervised machine learning algorithms such as support vector machine and reinforcement learning to learn the optimal action-selection policy for an agent interacting with an environment, such as Q-Learning. Large Language Model Meta Artificial Intelligence instruct (LLAMA3) is employed to dynamically optimize control strategies. Laboratory tests validate the implementation, focusing on system efficiency, adaptability, and stability under varying operating conditions. Our findings highlight the potential of artificial intelligence (AI) selected control methods over traditional strategies to deliver optimal performance for modern Permanent magnet synchronous motor.

永磁同步电机在现代应用中起着至关重要的作用，特别是在交通电气化方面。它们的高能效和在宽速度范围内保持恒定功率的能力使它们成为高速列车和电动汽车的理想选择。本研究探索了先进的控制策略，包括场定向控制（FOC），电压下降控制（Vdroop）和可调度虚拟振荡器控制（dVOC），使用德州仪器微控制器开发套件，Boost逆变器和传统平台实现。此外，有监督的机器学习算法，如支持向量机和强化学习，用于学习智能体与环境交互的最佳动作选择策略，如Q-Learning。采用大语言模型元人工智能指令（LLAMA3）对控制策略进行动态优化。实验室测试验证了系统的实施，重点关注系统在不同操作条件下的效率、适应性和稳定性。我们的研究结果突出了人工智能（AI）选择控制方法的潜力，而不是传统策略，为现代永磁同步电机提供最佳性能。

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引用次数: 0

Robust control and state of charge estimation for off-grid solar power systems using ANN-based reference voltage generation 基于人工神经网络的离网太阳能发电系统鲁棒控制与电量状态估计

IF 1.8 Q3 AUTOMATION & CONTROL SYSTEMS

IFAC Journal of Systems and Control

Pub Date : 2025-12-01 Epub Date: 2025-10-06 DOI: 10.1016/j.ifacsc.2025.100346

Hassan Ouabi, Rachid Lajouad, Mohammed Kissaoui, Abdelmounime El Magri

This study proposes an advanced multi-mode control strategy for a stand-alone photovoltaic (PV) system equipped with a Li-ion battery. The system is designed to cope with weather fluctuations and varying load demands, which can affect battery lifespan and charging efficiency. The proposed multimode control strategy dynamically switches between three modes: Maximum Power Point Tracking (MPPT) maximizes energy extraction under low PV generation, Constant Current (CC) ensuring fast battery charging, and Constant Voltage (CV) to preserve battery health during saturation. An Artificial Neural Network (ANN) is implemented to adaptively generate the PV reference voltage, enhancing system responsiveness to environmental changes. Furthermore, a state observer is designed to deliver accurate values of all battery states like battery’s state of charge (SoC), ensuring optimized performance, longevity, and safety. The effectiveness of the proposed control strategy and observer is validated through MATLAB/Simulink simulations. Finally, a semi-experimental study based on Processor-in-the-Loop (PIL) testing with the eZdsp TMS320F28335 board confirms the robustness and reliability of the system under real operating conditions.

针对锂离子电池独立式光伏发电系统，提出了一种先进的多模式控制策略。该系统旨在应对天气波动和负载需求变化，这可能会影响电池寿命和充电效率。所提出的多模式控制策略在三种模式之间动态切换：最大功率点跟踪（MPPT）在低光伏发电下最大限度地提取能量，恒流（CC）确保电池快速充电，恒压（CV）在饱和状态下保持电池健康。采用人工神经网络（ANN）自适应生成光伏基准电压，增强了系统对环境变化的响应能力。此外，状态观测器旨在提供所有电池状态的准确值，如电池的充电状态（SoC），确保优化的性能，寿命和安全性。通过MATLAB/Simulink仿真验证了所提控制策略和观测器的有效性。最后，利用eZdsp TMS320F28335板进行了半实验研究，验证了系统在实际工作条件下的鲁棒性和可靠性。

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引用次数: 0

Exploring the potential of standardized behaviour competencies in automated driving systems 探索自动驾驶系统中标准化行为能力的潜力

IF 1.8 Q3 AUTOMATION & CONTROL SYSTEMS

IFAC Journal of Systems and Control

Pub Date : 2025-09-01 Epub Date: 2025-06-16 DOI: 10.1016/j.ifacsc.2025.100320

Georg Stettinger , Patrick Weissensteiner , Nayel Fabian Salem , Marcus Nolte , Siddartha Khastgir

This paper presents a comprehensive impact assessment to explore the potential benefits of harmonized behaviour competencies (BC) for automated driving systems (ADS). Typically, ADS-equipped vehicles operate within certain boundaries specified by an operational design domain (ODD), utilizing the relevant implemented BCs. Nonetheless, many regulatory and standardization-relevant documents employ BC attributes in a non-harmonized manner. The study delves into BC-related activities and applications throughout the entire ADS life cycle, affecting all aspects of the ADS value chain, to gain a deeper understanding of the diverse needs of various stakeholders. BCs are linked to one of the four primary requirement sources at the system level. ADS-related BCs are defined through a multidisciplinary approach driven by their underlying core operating principle: the well-known sense-plan-act cycle. The crucial element within the BC specification is the identified manoeuvre pool, which forms the basis for implementing any route from point A to point B. The individual manoeuvres within the manoeuvre pool are defined by considering the needs of multiple stakeholders. They are based on three essential components: the initial condition, the expected manoeuvre, and the final condition. Furthermore, trustworthy behaviour competencies are specified, encompassing three pillars: robustness, ethics, and lawfulness. Following a detailed stakeholder analysis, several related applications are discussed to highlight the concrete advantages of implementing standardized BCs. The study concludes with a summary of the impact analysis, emphasizing key findings and action points. Lastly, a roadmap is proposed to integrate trustworthy BCs into future ADS. Concretely, the authors developed the following innovations within the scope of this article: (1) Concept for trustworthy behaviour competencies driven by law, ethics, and robustness. (2) Robustness is defined as passenger & ODD awareness and plannable & executable manoeuvre. (3) Manoeuvre pool necessary to implement an arbitrary route from point A to point B. (4) Manoeuvre specification via initial condition, expected behaviour, and final condition. (5) The potential benefits of harmonized behaviour competencies drive impact assessment.

本文提出了一个全面的影响评估，以探讨协调行为能力（BC）对自动驾驶系统（ADS）的潜在好处。通常，配备ads系统的车辆在操作设计域（ODD）规定的特定范围内运行，利用相关的实现bc。尽管如此，许多规范性和标准化相关文档以不协调的方式使用BC属性。该研究深入研究了整个ADS生命周期中与bc相关的活动和应用，影响了ADS价值链的各个方面，以更深入地了解不同利益相关者的不同需求。bc与系统级的四个主要需求源之一相关联。与ads相关的bc是通过多学科方法定义的，其基本核心操作原则是：众所周知的感知-计划-行动周期。BC规范中的关键要素是确定的机动池，它构成了实现从A点到b点的任何路线的基础。机动池中的单个机动是通过考虑多个利益相关者的需求来定义的。它们基于三个基本组成部分：初始条件、预期操作和最终条件。此外，值得信赖的行为能力被指定，包括三个支柱：稳健性，道德和合法性。在详细的利益相关者分析之后，讨论了几个相关的应用程序，以突出实现标准化bc的具体优势。该研究总结了影响分析，强调了主要发现和行动要点。最后，提出了将可信赖的bc整合到未来的ADS中的路线图。具体而言，作者在本文的范围内开发了以下创新：(1)由法律、道德和鲁棒性驱动的可信赖行为能力概念。(2)鲁棒性定义为乘客&；ODD意识和计划性；可执行的策略。(3)实现从A点到b点的任意路线所需的机动池。(4)通过初始条件、预期行为和最终条件确定的机动规范。(5)协调行为能力的潜在效益推动了影响评估。

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引用次数: 0

Optimized droop control strategy for efficiency improvement in islanded AC microgrid 孤岛交流微电网效率提升的优化下垂控制策略

IF 1.8 Q3 AUTOMATION & CONTROL SYSTEMS

IFAC Journal of Systems and Control

Pub Date : 2025-09-01 Epub Date: 2025-06-10 DOI: 10.1016/j.ifacsc.2025.100319

Md Akib Hasan , Md Showkot Hossain , Mohd Azrik Roslan , Azralmukmin Azmi , Leong Jenn Hwai , Ahmad Afif Nazib , Noor Syafawati Ahmad

The increasing integration of renewable energy sources has accelerated the adoption of microgrids, necessitating efficient power-sharing and control techniques for reliable operation. This study proposes an optimized droop control technique for parallel inverters in islanded AC microgrids, focusing on improving system efficiency. Conventional droop methods often encounter challenges in power-sharing accuracy under varying load conditions due to mismatched feeder impedances and differing power loss characteristics of distributed generators (DGs). To address these issues, the proposed method dynamically adjusts droop coefficients using Particle Swarm Optimization (PSO) to optimize power distribution, reduce circulating currents, and improve energy conversion efficiency while maintaining system modularity. A system-level microgrid efficiency model is designed to identify optimal operating points under diverse load profiles. Comparative analysis demonstrates that the proposed PSO-based controller consistently outperforms conventional droop methods, achieving system efficiency improvements ranging from 0.11% to 0.52% across various load conditions and power factors. Simulation results from PSIM and MATLAB/Simulink further highlight reduced circulating currents, enhanced energy conversion efficiency, and improved system stability. These findings underscore the potential of PSO-driven control as a scalable and communication-free solution for efficiency optimization in decentralized microgrids.

可再生能源的日益一体化加速了微电网的采用，需要有效的电力分享和可靠运行的控制技术。本文提出了一种孤岛交流微电网并联逆变器下垂优化控制技术，以提高系统效率为目标。由于馈线阻抗不匹配和分布式发电机的功率损耗特性不同，传统的下垂方法在不同负载条件下的功率共享精度面临挑战。为了解决这些问题，该方法利用粒子群优化（PSO）动态调整下垂系数，优化功率分配，减少循环电流，提高能量转换效率，同时保持系统的模块化。设计了系统级微电网效率模型，以确定不同负荷情况下的最佳工作点。对比分析表明，所提出的基于pso的控制器始终优于传统的下垂方法，在各种负载条件和功率因数下，系统效率提高了0.11%至0.52%。PSIM和MATLAB/Simulink的仿真结果进一步表明，循环电流减少，能量转换效率提高，系统稳定性提高。这些发现强调了pso驱动控制作为分散微电网效率优化的可扩展和无通信解决方案的潜力。

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引用次数: 0

In-silico evaluation of three control methodologies with model adaptation to minimize risk of overdosing in anesthesia 三种控制方法的计算机评价与模型适应，以尽量减少麻醉过量的风险

IF 1.8 Q3 AUTOMATION & CONTROL SYSTEMS

IFAC Journal of Systems and Control

Pub Date : 2025-09-01 Epub Date: 2025-06-27 DOI: 10.1016/j.ifacsc.2025.100324

Clara M. Ionescu , Bora Ayvaz , Robin De Keyser, Erhan Yumuk, Dana Copot

The ideal conditions for extracting good models for control are not attainable in clinical settings, due to patient safety and further enforced by ethical and regulatory frameworks. From prior observations, the patient model defined by the pharmacokinetic part is piecewise linear and mostly invariant among the patients, while the drug–dose effect relationship exhibits large variability, resulting in significant large gain variations in patient’s model. In this paper, we propose a model for the gain adaptation as a two-input (Propofol and Remifentanil) one output (hypnotic state BIS variable) linear area of the nonlinear surface of the dose–effect for general anesthesia. The new patient model is used for tuning controllers without over-dosing, i.e. no BIS-nadir values below 50 and avoid negative values of median prediction error indicative of over-dosing. A comparison of target controlled infusion (this is manual control with anesthesiologist closing the loop) against two control strategies is performed. A model based predictive control and a PID control scheme with model adaptation and co-administration in ratio control mode are compared before and after the patient model adaptation. The results indicate the adaptation step minimizes risk for over-dosing, as it minimizes modeling errors. Robustness of controllers has been assessed before the identification, encouraging the claim that predictive control closely mimics the human-in-the-loop target controlled infusion profiles. Evaluation criteria from clinical practice further enhance the added value of our solution. Real clinical data evaluation confirms the results from the simulation tests, showing a considerable match between the drug profiles titrated by anesthesiologist and those calculated by the proposed control algorithms.

由于患者安全以及伦理和监管框架的进一步执行，在临床环境中无法实现提取良好控制模型的理想条件。从之前的观察来看，药代动力学部分定义的患者模型是分段线性的，在患者之间基本不变，而药物-剂量效应关系表现出较大的变异性，导致患者模型的增益变化较大。在本文中，我们提出了一个增益适应模型，作为一个双输入（异丙酚和瑞芬太尼）一输出（催眠状态BIS变量）的非线性表面的剂量效应的线性区域。新的患者模型用于无过量给药的控制器调整，即BIS-nadir值不低于50，避免中位预测误差为负值表示过量给药。对两种控制策略进行了目标控制输注（这是麻醉师关闭回路的手动控制）的比较。比较了基于模型的预测控制和比例控制模式下模型自适应和共给药的PID控制方案在患者模型自适应前后的差异。结果表明，适应步骤最小化了过量剂量的风险，因为它最小化了建模误差。在识别之前已经评估了控制器的鲁棒性，这鼓励了预测控制密切模仿人在环目标控制输液概况的说法。来自临床实践的评估标准进一步提升了我们解决方案的附加值。真实的临床数据评估证实了模拟测试的结果，显示麻醉师滴定的药物谱与所提出的控制算法计算的药物谱之间有相当大的匹配。

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引用次数: 0

Stability analysis of feedback interconnections between systems with “mixed” properties 具有“混合”性质的系统间反馈互连的稳定性分析

IF 1.8 Q3 AUTOMATION & CONTROL SYSTEMS

IFAC Journal of Systems and Control

Pub Date : 2025-09-01 Epub Date: 2025-07-25 DOI: 10.1016/j.ifacsc.2025.100329

Liu Liu, Xinshu Wang

Sufficient conditions for the finite-gain stability of positive feedback interconnected systems are given when the subsystems have a certain mixed dissipative property, such as “mixed” small gain and passivity, “mixed” small gain and negative imaginary, “mixed” passivity and negative imaginary. In addition, the converse of the integral quadratic constraint (IQC) theorem involving nonlinear systems is provided on the basis of the S-procedure lossless theorem. Furthermore, a collection of converse results for mixed dissipative theorems is derived by the converse IQC theorem and the decomposition of the multipliers. It is demonstrated that if the feedback interconnection of a linear time-invariant (LTI) system with an arbitrary system satisfying some mixed dissipative property is finite-gain stable, then the given system must have a more strict version of the same mixed dissipative property. Meanwhile, the converse IQC theorem can cover the converse theorems of small-gain, passivity and

(Q, S, R)

-dissipativity.

给出了当子系统具有一定的混合耗散性质，如“混合”小增益与无源、“混合”小增益与负虚数、“混合”无源与负虚数时，正反馈互联系统有限增益稳定的充分条件。此外，在s过程无损定理的基础上，给出了非线性系统的积分二次约束定理的逆式。在此基础上，利用逆IQC定理和乘子分解，得到了混合耗散定理的一组逆结果。证明了如果线性定常系统与满足混合耗散性质的任意系统的反馈互连是有限增益稳定的，则该系统必须具有相同混合耗散性质的更严格版本。同时，逆IQC定理可以涵盖小增益、无源性和（Q，S，R）-耗散的逆定理。

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引用次数: 0