Annual Reviews in Control最新文献

英文中文

IF 10.7 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Annual Reviews in Control

Pub Date : 2025-01-01

引用次数: 0

IF 10.7 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Annual Reviews in Control

Pub Date : 2025-01-01

引用次数: 0

Gaussian processes for dynamics learning in model predictive control 模型预测控制中动态学习的高斯过程

IF 10.7 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Annual Reviews in Control

Pub Date : 2025-01-01 DOI: 10.1016/j.arcontrol.2025.101034

Anna Scampicchio, Elena Arcari, Amon Lahr , Melanie N. Zeilinger

Due to its state-of-the-art estimation performance complemented by rigorous and non-conservative uncertainty bounds, Gaussian process regression is a popular tool for enhancing dynamical system models and coping with their inaccuracies. This has enabled a plethora of successful implementations of Gaussian process-based model predictive control in a variety of applications over the last years. However, despite its evident practical effectiveness, there are still many open questions when attempting to analyze the associated optimal control problem theoretically and to exploit the full potential of Gaussian process regression in view of safe learning-based control. The contribution of this review is twofold. The first is to survey the available literature on the topic, highlighting the major theoretical challenges such as (i) addressing scalability issues of Gaussian process regression; (ii) taking into account the necessary approximations to obtain a tractable MPC formulation; (iii) including online model updates to refine the dynamics description, exploiting data collected during operation. The second is to provide an extensive discussion of future research directions, collecting results on uncertainty quantification that are related to (but yet unexploited in) optimal control, among others. Ultimately, this paper provides a toolkit to study and advance Gaussian process-based model predictive control.

由于其最先进的估计性能加上严格和非保守的不确定性界限，高斯过程回归是增强动力系统模型和处理其不准确性的流行工具。在过去的几年中，这使得基于高斯过程的模型预测控制在各种应用中获得了大量成功的实现。然而，尽管它具有明显的实际有效性，但在试图从理论上分析相关的最优控制问题以及从基于安全学习的控制角度充分利用高斯过程回归的潜力时，仍然存在许多悬而未决的问题。这篇综述的贡献是双重的。首先是调查关于该主题的现有文献，突出主要的理论挑战，如(i)解决高斯过程回归的可扩展性问题；（ii）考虑到必要的近似，以获得可处理的MPC配方；（iii）利用在操作过程中收集的数据，包括在线模型更新以完善动力学描述。第二是对未来的研究方向进行广泛的讨论，收集与最优控制相关（但尚未开发）的不确定性量化结果等。最后，本文提供了一个工具箱来研究和推进基于高斯过程的模型预测控制。

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引用次数: 0

Generalized inverse optimal control and its application in biology 广义逆最优控制及其在生物学中的应用

IF 10.7 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Annual Reviews in Control

Pub Date : 2025-01-01 DOI: 10.1016/j.arcontrol.2025.101029

Julio R. Banga , Sebastian Sager

Living organisms exhibit remarkable adaptations across all scales, from molecules to ecosystems. We believe that many of these adaptations correspond to optimal solutions driven by evolution, training, and underlying physical and chemical laws and constraints. While some argue against such optimality principles due to their potential ambiguity, we propose generalized inverse optimal control to infer them directly from data. This comprehensive approach incorporates multi-criteria optimality, nestedness of objective functions on different scales, the presence of active constraints, the possibility of switches of optimality principles during the observed time horizon, maximization of robustness and minimization of time as important special cases, as well as uncertainties involved with the mathematical modeling of biological systems. This data-driven approach ensures that optimality principles are not merely theoretical constructs but are firmly rooted in experimental observations. The inferred principles can also be used in forward optimal control to predict and manipulate biological systems, with possible applications in bio-medicine, biotechnology, and agriculture. As discussed and illustrated, the well-posed problem formulation and the inference are challenging and require a substantial interdisciplinary effort in the development of theory and robust numerical methods.

从分子到生态系统，生物体在所有尺度上都表现出非凡的适应性。我们相信，这些适应中的许多都是由进化、训练以及潜在的物理和化学定律和约束所驱动的最佳解决方案。虽然有些人反对这种最优性原则，因为它们潜在的模糊性，我们提出广义逆最优控制，直接从数据中推断它们。这种综合方法结合了多准则最优性、不同尺度上目标函数的嵌套性、主动约束的存在、在观察时间范围内最优性原则切换的可能性、鲁棒性最大化和时间最小化作为重要的特殊情况，以及与生物系统数学建模相关的不确定性。这种数据驱动的方法确保了最优性原则不仅仅是理论结构，而是牢固地植根于实验观察。推断出的原理也可以用于前向最优控制，以预测和操纵生物系统，可能应用于生物医学，生物技术和农业。正如所讨论和说明的那样，适定问题的表述和推理是具有挑战性的，需要在理论和稳健的数值方法的发展中进行大量的跨学科努力。

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引用次数: 0

A view on learning robust goal-conditioned value functions: Interplay between RL and MPC 鲁棒目标条件值函数的学习：RL与MPC的相互作用

IF 10.7 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Annual Reviews in Control

Pub Date : 2025-01-01 DOI: 10.1016/j.arcontrol.2025.101027

Nathan P. Lawrence , Philip D. Loewen , Michael G. Forbes , R. Bhushan Gopaluni , Ali Mesbah

Reinforcement learning (RL) and model predictive control (MPC) offer a wealth of distinct approaches for automatic decision-making under uncertainty. Given the impact both fields have had independently across numerous domains, there is growing interest in combining the general-purpose learning capability of RL with the safety and robustness features of MPC. To this end, this paper presents a tutorial-style treatment of RL and MPC, treating them as alternative approaches to solving Markov decision processes. In our formulation, RL aims to learn a global value function through offline exploration in an uncertain environment, whereas MPC constructs a local value function through online optimization. This local–global perspective suggests new ways to design policies that combine robustness and goal-conditioned learning. Robustness is incorporated into the RL and MPC pipelines through a scenario-based approach. Goal-conditioned learning aims to alleviate the burden of engineering a reward function for RL. Combining the two leads to a single policy that unites a robust, high-level RL terminal value function with short-term, scenario-based MPC planning for reliable constraint satisfaction. This approach leverages the benefits of both RL and MPC, the effectiveness of which is demonstrated on classical control benchmarks.

强化学习（RL）和模型预测控制（MPC）为不确定条件下的自动决策提供了多种不同的方法。考虑到这两个领域在许多领域都具有独立的影响，将RL的通用学习能力与MPC的安全性和鲁棒性相结合的兴趣越来越大。为此，本文提出了RL和MPC的教程式处理，将它们视为解决马尔可夫决策过程的替代方法。在我们的公式中，RL旨在通过在不确定环境下的离线探索学习全局价值函数，而MPC通过在线优化构建局部价值函数。这种局部-全球视角提出了结合鲁棒性和目标条件学习的新策略设计方法。鲁棒性通过基于场景的方法整合到RL和MPC管道中。目标条件学习旨在减轻强化学习中奖励函数的工程化负担。将两者结合起来，就形成了一个单一的策略，该策略将强大的、高水平的RL终端价值功能与短期的、基于场景的MPC规划结合起来，以实现可靠的约束满足。这种方法利用了RL和MPC的优点，其有效性在经典控制基准测试中得到了证明。

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引用次数: 0

Frequential lithium-ion battery impedance identification using automatic model selection and initialization 使用自动模型选择和初始化的高频锂离子电池阻抗识别

IF 10.7 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Annual Reviews in Control

Pub Date : 2025-01-01 DOI: 10.1016/j.arcontrol.2025.101011

Omar Arahbi, Benoît Huard, Jean-Denis Gabano, Thierry Poinot

Electrochemical Impedance Spectroscopy (EIS) is a useful tool for selecting a pertinent Equivalent Circuit Model (ECM) of a Lithium-ion battery. Impedance model is designed to describe low, middle and high frequency electrochemical processes involved. When considering low frequency restricted in the Warburg zone, diffusion impedance is modeled thanks to a Constant Phase Element (CPE) which behaves as a fractional integrator of order

n

close to 0.5. Phenomena observed in middle frequency are described using specific circuits called Zarc which consist in connecting a CPE in parallel with a resistor. Therefore, the global impedance model is characterized by non integer order operators and parameters can be estimated by a Complex Nonlinear Least Squares (CNLS) algorithm which requires a proper initialization in order to guarantee the convergence to a global optimum. The paper presents a method to analyze EIS data measurements in order to select automatically the number of middle frequency Zarc circuits required (one or two) and to initialize properly the CNLS algorithm. The method is validated using simulation data as well as experimental open source EIS data.

电化学阻抗谱（EIS）是选择锂离子电池等效电路模型（ECM）的有效工具。设计阻抗模型来描述所涉及的低、中、高频电化学过程。当考虑限制在Warburg区域的低频时，扩散阻抗的建模得益于恒定相位元件（CPE），其表现为接近0.5阶的n阶分数积分器。在中频观察到的现象是用称为Zarc的特定电路来描述的，该电路包括将CPE与电阻并联连接。因此，全局阻抗模型具有非整数阶算子的特征，参数可由复杂非线性最小二乘（CNLS）算法估计，该算法需要适当的初始化以保证收敛到全局最优。本文提出了一种分析EIS数据测量的方法，以便自动选择所需的中频Zarc电路数量（一个或两个），并适当初始化CNLS算法。利用仿真数据和实验开源EIS数据对该方法进行了验证。

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引用次数: 0

Distributed design of ultra large-scale control systems: Progress, Challenges, and Prospects 超大规模控制系统的分布式设计：进展、挑战与展望

IF 7.3 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Annual Reviews in Control

Pub Date : 2025-01-01 DOI: 10.1016/j.arcontrol.2025.100987

Leonardo Pedroso , Pedro Batista , W.P.M.H. (Maurice) Heemels

The transition from large centralized complex control systems to distributed configurations that rely on a network of a very large number of interconnected simpler subsystems is ongoing and inevitable in many applications. It is attributed to the quest for resilience, flexibility, and scalability in a multitude of engineering fields with far-reaching societal impact. Although many design methods for distributed and decentralized control systems are available, most of them rely on a centralized design procedure requiring some form of global information of the whole system. Clearly, beyond a certain scale of the network, these centralized design procedures for distributed controllers are no longer feasible and we refer to the corresponding systems as ultra large-scale systems (ULSS). For these ULSS, design algorithms are needed that are distributed themselves among the subsystems and are subject to stringent requirements regarding communication, computation, and memory usage of each subsystem. In this paper, a set of requirements is provided that assures a feasible real-time implementation of all phases of a control solution on an ultra large scale. State-of-the-art approaches are reviewed in the light of these requirements and the challenges hampering the development of befitting control algorithms are pinpointed. Comparing the challenges with the current progress leads to the identification and motivation of promising research directions.

在许多应用中，从大型集中式复杂控制系统到依赖于大量相互连接的简单子系统的网络的分布式配置的转变正在进行并且是不可避免的。这是由于对具有深远社会影响的众多工程领域的弹性，灵活性和可扩展性的追求。虽然有许多分布式和分散控制系统的设计方法，但大多数都依赖于需要整个系统的某种形式的全局信息的集中设计过程。显然，超过一定的网络规模，这些分布式控制器的集中设计程序就不再可行了，我们将相应的系统称为超大规模系统（ULSS）。对于这些ULSS，需要设计算法，这些算法本身分布在子系统之间，并且对每个子系统的通信、计算和内存使用都有严格的要求。在本文中，提供了一组要求，以确保在超大规模的控制解决方案的所有阶段都是可行的实时实现。根据这些要求，对最先进的方法进行了审查，并指出了阻碍适当控制算法发展的挑战。将挑战与当前的进展进行比较，可以识别和激励有前途的研究方向。

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引用次数: 0

Modeling of bio-heat transfers in lungs with fractional models 用分数模型模拟肺部的生物热传递

IF 10.7 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Annual Reviews in Control

Pub Date : 2025-01-01 DOI: 10.1016/j.arcontrol.2025.101010

Enso Ndreko , Stéphane Victor , Jean-François Duhé , Pierre Melchior

In cardiac surgeries, when cardiopulmonary bypass (CPB) (or extracorporeal circulation (ECC)) is employed, the lungs are temporarily disconnected from the body. To minimize the risk of tissue damage or respiratory complications, the lungs are subjected to mild hypothermia. Incorporating dynamic heat transfer modeling offers the potential to enhance temperature regulation through a more advanced approach.

A complex thermal model, based on a thermal two-port network, offers a wide frequency range applicability, making it suitable for modeling the human breathing frequencies. This modeling approach can also be adapted to incorporate the influence of blood flow, which serves as a natural temperature regulator in the human body. This is accomplished by combining the thermal two-port network with the bio-heat equation.

The main contributions focus on introducing distinctive and simplified approximation models for the equivalent global impedance of thermal transfer within the lungs. These models, featuring minimal parameters, manifest comparable dynamic traits in the frequency domain, akin to the attributes of the two-port network model. This progress clears the way for broader utilization across various domains.

在心脏手术中，当采用体外循环（ECC）或体外循环（CPB）时，肺部暂时与身体断开。为了尽量减少组织损伤或呼吸系统并发症的风险，对肺部进行轻度低温治疗。结合动态传热模型提供了通过更先进的方法来提高温度调节的潜力。基于热双端口网络的复杂热模型提供了广泛的频率范围适用性，使其适合于模拟人类呼吸频率。这种建模方法也可以适应血液流动的影响，血液流动是人体的自然温度调节器。这是通过结合热双端口网络和生物热方程来实现的。主要的贡献集中在引入独特的和简化的近似模型的等效全球热传递在肺内的阻抗。这些模型具有最小的参数，在频域中表现出可比的动态特征，类似于双端口网络模型的属性。这一进展为在各个领域进行更广泛的利用扫清了道路。

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引用次数: 0

A review on VSC-HVDC control schemes VSC-HVDC 控制方案综述

IF 7.3 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Annual Reviews in Control

Pub Date : 2025-01-01 DOI: 10.1016/j.arcontrol.2025.100988

Juan Ramón Camarillo-Peñaranda , Ana Carolina Cunha , Bruno W. França , Francis de Abreu Oliveira , Luan de Oliveira Senna

Designing a VSC-HVDC controller capable of effectively operating in most operational scenarios is challenging due to the highly complex dynamics, unexpected failures, and system uncertainty prevalent in a broad range of post-deployment situations. Furthermore, the stability of the closed-loop system stands as a crucial and undeniable aspect that necessitates special attention during the system development phase. From today’s point of view, this review provides a comprehensive overview of the literature on control technologies, their characteristics, and control options applicable to HVDC systems. This article discusses their applications, advantages, limitations, and recent developments within these techniques.

由于高度复杂的动态、意外故障以及在广泛的部署后情况下普遍存在的系统不确定性，设计能够在大多数操作场景中有效运行的vcs - hvdc控制器具有挑战性。此外，在系统开发阶段，闭环系统的稳定性是一个至关重要和不可否认的方面，需要特别注意。从今天的角度来看，这篇综述提供了一个关于控制技术的文献的全面概述，它们的特点，以及适用于高压直流系统的控制选项。本文讨论了它们的应用、优点、限制以及这些技术的最新发展。

引用次数: 0

Implementation of deep reinforcement learning in permanent magnet synchronous motors control: A review 深度强化学习在永磁同步电机控制中的应用综述

IF 10.7 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Annual Reviews in Control

Pub Date : 2025-01-01 DOI: 10.1016/j.arcontrol.2025.101014

Larbi Assem Moulai , Fardila M. Zaihidee , Saad Mekhilef , Jing Rui Tang , Marizan Mubin

Permanent Magnet Synchronous Motors (PMSMs) are recognized for high efficiency, torque-to-inertia ratio, and robust properties, making them ideal for the rapid development of electric vehicles, robotics, and the aerospace industry. Recently, Deep Reinforcement Learning (DRL) algorithms have gained significant attention in the control domain due to their independence from plant models and advanced decision-making capabilities. These features make DRL highly suitable for addressing challenges in PMSM control such as load disturbances, speed tracking, and parameter variations. This review explores recent DRL techniques applied to PMSM speed, current, and torque control. Discrete and continuous algorithms, including Deep Q-Network (DQN), Deep Deterministic Policy Gradient (DDPG), and Twin Delayed DDPG (TD3), are examined in terms of their basic principles, practical implementations, and the benefits they provide in overcoming challenges in PMSM control. In addition, to demonstrate the efficiency of DRL, the review provides a summary and comparison of DRL applied to optimize classical control methods elaborated within various PMSM control strategies. Comparisons of DRL implementations in PMSM control are highlighted to validate their real-time applicability in experiments, and potential areas for future research and improvement are outlined.

永磁同步电机（pmms）以高效率、转惯量比和坚固的性能而闻名，使其成为电动汽车、机器人和航空航天工业快速发展的理想选择。近年来，深度强化学习（DRL）算法因其独立于植物模型和先进的决策能力而在控制领域受到了广泛关注。这些特性使得DRL非常适合解决PMSM控制中的挑战，如负载干扰、速度跟踪和参数变化。本文综述了最近应用于永磁同步电机速度、电流和转矩控制的DRL技术。本文对离散和连续算法，包括深度q -网络（DQN）、深度确定性策略梯度（DDPG）和双延迟DDPG （TD3）的基本原理、实际实现以及它们在克服永磁同步电机控制挑战方面提供的好处进行了研究。此外，为了证明DRL的有效性，本文总结和比较了DRL在各种永磁同步电机控制策略中用于优化经典控制方法的应用。重点比较了DRL在PMSM控制中的实现，验证了它们在实验中的实时性，并概述了未来可能的研究和改进领域。

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