Annual Reviews in Control最新文献_第3页

Smith predictor based control of multi time-delay processes 基于Smith预测器的多时滞过程控制

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.2025.101008

Pedro Albertos , Xiaoli Luan , Pedro Garcia

In the process industry, the presence of time-delays is unavoidable and these delays may happen at the input, output or at any internal point of the process. As a result, the output/input relationship may present multiple time-delays. The Smith Predictor is well recognized as the best approach to deal with time delays but its use is mainly limited to process models where the time delay is separated from the rest of the model. In this paper, a comprehensive review of the control of processes with multi time-delays has been done, identifying key research gaps in the literature. In particular, the case of multi time-delays in a single forward/feedback path is an area that remains insufficiently addressed. By means of precompensators, the initial process model is transformed and reduced to single time-delay models. Then, the Smith Predictor approach can be applied being proposed to design the final control structure. Some simplifications in the join implementation of the predictor and the precompensator are suggested. The procedure is also extended to deal with MIMO plants presenting multi time-delays in the transfer matrix elements. In this way, a general solution to design the control for multi time-delayed processes is presented and some examples illustrate the procedure. Some conclusions are drafted along with open problems for future research.

在过程工业中，时间延迟的存在是不可避免的，这些延迟可能发生在过程的输入、输出或任何内部点。因此，输出/输入关系可能呈现多个延时。Smith Predictor被公认为是处理时间延迟的最佳方法，但它的使用主要局限于时间延迟与模型其余部分分离的过程模型。在本文中，对多时滞过程的控制进行了全面的回顾，确定了文献中的关键研究空白。特别是，在单个前向/反馈路径中存在多时延的情况是一个仍然没有得到充分解决的领域。利用预补偿器对初始过程模型进行变换，简化为单个时滞模型。然后，应用Smith预测器方法设计最终的控制结构。在预测器和预补偿器的连接实现中，提出了一些简化的建议。该方法还扩展到处理传输矩阵元素中存在多时延的MIMO对象。通过这种方法，给出了多时滞过程控制设计的通解，并举例说明了设计过程。一些结论和有待进一步研究的开放性问题被起草。

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引用次数: 0

Practical implementation of gain scheduling strategies with FOPID controllers: A bumpless LPV approach FOPID控制器增益调度策略的实际实现：无颠簸LPV方法

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.2025.101007

Marco Milanesi , Fabrizio Padula , Antonio Visioli

In this paper we propose a methodology to implement gain scheduling strategies for fractional-order proportional–integral–derivative controllers. In particular, we use a finite-dimensional approximation of the fractional operators and we implement it as a linear parameter varying system. Then, we provide a solution to the initialization problem that emerges when the parameters of the FOPID controller change discontinuously so that the control action is continuous, thereby avoiding any bump. Experimental results show that this method can be used in practice and has industrial relevance.

本文提出了一种分数阶比例-积分-导数控制器增益调度策略的实现方法。特别地，我们使用分数算子的有限维近似并将其实现为线性参数变系统。然后，针对FOPID控制器参数不连续变化时出现的初始化问题，提供了一种解决方案，使控制动作连续，从而避免了任何颠簸。实验结果表明，该方法可以在实际中应用，具有一定的工业实用性。

引用次数: 0

Fractional-order PID controllers and applications: A comprehensive survey 分数阶PID控制器及其应用综述

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.2025.101013

T.K. Bashishtha , V.P. Singh , Umesh Kumar Yadav , Umesh Kumar Sahu

The scientific world has recently witnessed a rapid growth in the number of articles, addressing the importance of fractional-order controllers with a primary focus on fractional-order proportional integral derivative controller. The purpose of this article is to provide a detailed analysis of fractional-order proportional integral derivative controller, including architecture, classification, control design, toolboxes, and applications. Further, this article also presents an extensive overview on fractional-order controllers, their tuning methods, and the role of multiple classical and artificial intelligence-based algorithms in calculating optimal controller gains. The main focus of this article is to analyze various single and multi-objective functions adopted for controller tuning in various applications. In addition to this, the presented work also provides a comprehend comparative analysis through a rigorous literature survey of fractional-order proportional integral derivative controllers exploited in power system applications, electric drive applications, power electronics applications, biomedical and health science applications, chemical and process industry applications, agricultural applications, etc., in the support of superiority of fractional-order proportional integral derivative controller over conventional proportional integral derivative controller.

科学界最近见证了文章数量的快速增长，讨论分数阶控制器的重要性，主要关注分数阶比例积分导数控制器。本文的目的是详细分析分数阶比例积分导数控制器，包括体系结构、分类、控制设计、工具箱和应用。此外，本文还介绍了分数阶控制器的广泛概述，它们的调谐方法，以及多种经典算法和基于人工智能的算法在计算最优控制器增益中的作用。本文的主要重点是分析各种应用中用于控制器整定的各种单目标和多目标函数。除此之外，本文还通过对分数阶比例积分导数控制器在电力系统应用、电力驱动应用、电力电子应用、生物医学和健康科学应用、化学和加工工业应用、农业应用等方面的严格文献调查，提供了一个全面的比较分析。在分数阶比例积分导数控制器优于传统比例积分导数控制器的支持下。

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引用次数: 0

Observer-based error correction in finite memory approaches for pseudo state estimation of fractional dynamic models 分数阶动态模型伪状态估计的有限记忆观测器纠错方法

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.2025.101018

Andreas Rauh, Marit Lahme

Fractional system models have gained in importance during recent years to account for non-standard dynamics that are characterized by long-term memory effects. An important field of application is the modeling, simulation, and control design for electrochemical energy converters such as batteries and fuel cells. Although such models are generally capable to capture (infinite horizon) memory properties, the numerical evaluation may be complicated by the same effect leading to a continuous increase in memory and computing time if no appropriate countermeasures are taken. This is especially true in (pseudo) state estimation procedures in which a periodic integrator reset takes place. Such integrator resets become necessary as a countermeasure against the increase of memory requirements and computing times. Bounding both is necessary to meet the strict requirements of real-time applicability. Additionally, such resets occur when predictor–corrector state estimators are employed for systems with bounded uncertainty. This article provides a thorough review of observer-based methods for the quantification and enhancement of the aforementioned truncation errors. Exemplary results are shown for the pseudo state estimation of a simplified fractional model of charging and discharging of lithium-ion batteries.

近年来，分数系统模型在解释以长期记忆效应为特征的非标准动力学方面变得越来越重要。一个重要的应用领域是电池和燃料电池等电化学能量转换器的建模、仿真和控制设计。虽然这种模型通常能够捕获（无限视界）记忆属性，但如果不采取适当的对策，则可能由于相同的效应导致内存和计算时间的持续增加而使数值评估复杂化。在发生周期性积分器重置的（伪）状态估计过程中尤其如此。这样的积分器重置作为对抗内存需求和计算时间增加的对策是必要的。为满足实时性的严格要求，将两者绑定是必要的。此外，当对具有有限不确定性的系统使用预测校正状态估计器时，会发生这种重置。本文全面回顾了基于观测器的方法，用于量化和增强上述截断误差。给出了锂离子电池充放电简化分数阶模型的伪状态估计示例性结果。

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引用次数: 0

Control and optimization techniques for load frequency control of multi-area Cyber–Physical Power Systems: A literature review 多区域信息物理电力系统负荷频率控制与优化技术：文献综述

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.2025.101009

Bo Hu , Jianning Cai , Zuoxia Xing , Pengfei Zhang , Dongrui Wang , Komla Agbenyo Folly

In Cyber–Physical Power Systems (CPPSs), Load Frequency Control (LFC) needs an efficient information system and high performance strategies to ensure power system frequency stability. This paper focuses on these two aspects, comprehensively reviewing the challenges and research progress of LFC in multi-area CPPSs from three dimensions: modeling, control strategies, and mitigation methods of some information system abnormal conditions. Firstly, considering the high penetration of renewable energy and open network architectures, we review various LFC models applicable to CPPSs from physical dynamics and information layer features, providing a solid theoretical basis for future research. Secondly, we classify and summarize LFC control strategies in CPPSs, covering classical, modern control strategies, optimization technologies and analyze their strengths and limitations in LFC. Finally, we analyze information system challenges like cyber attacks, communication bandwidth limits and delays, and propose corresponding countermeasures. In conclusion, this paper delves into LFC issues in CPPSs, focusing on challenges such as communication bandwidth limitations, delays and cyber attacks. It is hoped to provide a powerful framework and valuable research direction for future work in the field.

在信息物理电力系统（CPPSs）中，负荷频率控制（LFC）需要一个高效的信息系统和高性能的策略来保证电力系统的频率稳定。本文围绕这两个方面，从部分信息系统异常情况的建模、控制策略和缓解方法三个维度，全面综述了多区域CPPSs中LFC面临的挑战和研究进展。首先，考虑到可再生能源的高渗透率和开放的网络架构，我们从物理动力学和信息层特征方面综述了适用于cpps的各种LFC模型，为未来的研究提供坚实的理论基础。其次，对cpps中的LFC控制策略进行了分类和总结，包括经典控制策略、现代控制策略和优化技术，并分析了它们在LFC中的优势和局限性。最后，分析了信息系统面临的网络攻击、通信带宽限制和时延等挑战，并提出了相应的对策。综上所述，本文深入研究了CPPSs中的LFC问题，重点关注通信带宽限制、延迟和网络攻击等挑战。希望为今后的工作提供一个强有力的框架和有价值的研究方向。

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引用次数: 0

Knock feedback control strategies: A review 敲击反馈控制策略：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.2025.100990

J.C. Peyton Jones, V. Patel, J. Frey

Knocking combustion is a limiting factor in otherwise highly efficient modern downsized engines, which therefore require some form of knock control strategy. These strategies are distinctive because knock behaves as a cyclically independent random process. An increased focus on the stochastic aspect of this problem has opened up new possibilities for controller design, as seen in the recent literature. This paper aims to organize and present some of the principal ideas and approaches in a consistent and structured framework. Controllers are broadly classified by the signal / statistic used for feedback and the method of its estimation. Both classical and advanced knock ‘event-based’ strategies apply a threshold to the measured knock intensity value and then regulate the resulting knock event probability. Knock ‘intensity-based’ controllers, by contrast, use the more informative raw knock intensity signal for feedback, and generally assume these intensities conform to a log-normal distribution. More recently, Bayesian estimators have been used in both event-based and intensity-based control, as an alternative to frequentist estimation methods. Within each of these broad classes, individual control strategies are discussed in detail, and, in some cases, simplifying expressions are derived. More generally, the choice of control objective, estimation method, and error metric are also discussed in depth since these have a significant impact on closed loop performance.

爆震燃烧是一个限制因素，否则高效率的现代小型化发动机，因此需要某种形式的爆震控制策略。这些策略是独特的，因为knock表现为一个周期独立的随机过程。正如最近的文献所示，对该问题随机方面的关注增加了控制器设计的可能性。本文的目的是在一个一致的和结构化的框架中组织和呈现一些主要的思想和方法。根据反馈所使用的信号/统计量及其估计方法，控制器可以大致分类。经典和先进的“基于事件”的爆燃策略都对测量的爆燃强度值应用一个阈值，然后调节产生的爆燃事件概率。相比之下，“基于强度”的敲击控制器使用信息更丰富的原始敲击强度信号作为反馈，并且通常假设这些强度符合对数正态分布。最近，贝叶斯估计器被用于基于事件和基于强度的控制，作为频率估计方法的替代方法。在这些广泛的类中，详细讨论了各个控制策略，并且在某些情况下推导了简化表达式。更一般地说，控制目标、估计方法和误差度量的选择也被深入讨论，因为它们对闭环性能有重大影响。

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引用次数: 0

Digital twins in manufacturing: A unified conceptual framework 制造业中的数字孪生：一个统一的概念框架

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.2025.101031

Luis Felipe Villegas, Marco Macchi, Adalberto Polenghi

Digital Twins (DTs) have the potential to transform how manufacturers conceive, design, manage, and optimize products, manufacturing systems, and services. However, their rapid evolution has led to a fragmented landscape in terms of conceptualization, applications, maturity levels, and enabling technological capabilities, creating a misalignment between academic research and industrial implementation. This paper addresses these challenges by proposing a unified conceptual framework for DTs in manufacturing, which integrates DT applications and maturity levels across product, production, and service domains and derives a theory-driven DT definition. To this end, two complementary frameworks are developed: the Digital Twin Application-based Framework and the Digital Twin Technological Capabilities Framework, which together classify DT applications, maturity levels, and technological capabilities within the three manufacturing domains. To establish conceptual clarity and account for the evolving, adaptive, and granular nature of DTs, the framework introduces a definition grounded in Complex Adaptive Systems (CAS) Theory, framing DTs as dynamic, interconnected systems that evolve in complexity and functionality based on domain-specific needs. Overall, this research contributes to promote an alignment between academia and industry by providing a structured analysis and comprehensive conceptualization of DTs in manufacturing.

数字孪生（DTs）有可能改变制造商构思、设计、管理和优化产品、制造系统和服务的方式。然而，它们的快速发展导致了概念化、应用程序、成熟度级别和启用技术能力方面的碎片化景观，在学术研究和工业实现之间造成了不一致。本文通过为制造业中的DT提出一个统一的概念框架来解决这些挑战，该框架集成了跨产品、生产和服务领域的DT应用和成熟度级别，并派生了一个理论驱动的DT定义。为此，开发了两个互补的框架：基于数字孪生应用的框架和数字孪生技术能力框架，它们共同对三个制造领域内的数字孪生应用、成熟度水平和技术能力进行分类。为了建立清晰的概念，并解释开发工具的演化、适应性和粒度特性，该框架引入了一个基于复杂自适应系统（CAS）理论的定义，将开发工具定义为动态的、相互关联的系统，这些系统根据特定领域的需求在复杂性和功能上进化。总体而言，本研究通过提供制造业中直接设计的结构化分析和全面概念化，有助于促进学术界和工业界之间的协调。

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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