Annual Reviews in Control最新文献

An autoimmune disease known as type 1 diabetes occurs when the immune system mistakenly attacks and destroys the beta cells in the pancreas, impairing their ability to produce insulin. An artificial pancreas is a device that is able to analyze information from sensors, such as continuous glucose monitoring, to deliver the correct amount of insulin by subcutaneous injection via a pump. The design and development of such an artificial pancreas poses several challenges. One of these is the need for an appropriate mathematical model of the patient’s physiology in order to develop a suitable controller. Over the past three decades, a number of artificial pancreas control techniques have been investigated in simulation and clinical research. This review aims to advance the knowledge of artificial pancreas system development by providing a comprehensive overview of recent advances in modeling the biological processes involved and in developing nonlinear control strategies. Real-time parameter estimation and effective uncertainty management as well as in-depth clinical studies and long-term investigations are relevant aspects that need to be evaluated for assessing the efficacy and safety of the artificial pancreas in practice. Further perspectives on control techniques that address patient-specific conditions and enable effective and individualized diabetes management will also be discussed.

The monitoring process for complex infrastructure requires collecting various data sources with varying time scales, resolutions, and levels of abstraction. These data sources include data from human inspections, historical failure records, cost data, high-fidelity physics-based simulations, and online health monitoring. Such heterogeneity presents significant challenges in implementing a diagnostic and prognostic framework for decision-making regarding maintenance (and other life cycle actions). The core challenge lies in the effective integration of physical information and data-driven models, aiming to synergize their strengths to overcome individual limitations. One possible solution is to propose an approach that considers the strengths and limitations of each data source, as well as their compatibility with each other. The flexibility and efficacy of contemporary learning approaches can be used with more systematic and informative physics-based models that draw on domain expertise. This represents an inherent desire to base all inferences on both our engineering knowledge and monitoring data that is at our disposal. In this context, the article reviews recent advances in this field, particularly in physics-based and deep learning techniques. It looks at new theories and models developed in the last five years, especially those used in system health monitoring, predicting damage, and planning maintenance. These new methods are proving to be more accurate and efficient than older, more traditional techniques. However, there are still challenges to be addressed. These include the need for high-quality data, finding the right balance between accuracy and the time it takes to compute, and effectively combining physical models with data-driven models. The paper calls for further research into methods that can handle large amounts of complex data and consider uncertainties in both the models and the data. Finally, it highlights the need to explore how these models can be adapted for different systems and used in real-time applications.

This article reviews and investigates several basic recursive parameter identification methods for a general stochastic system with colored noise (i.e., output-error autoregressive moving average system or Box–Jenkins system). These recursive identification methods are derived by means of the hierarchical identification principle and the filtering identification idea, including a filtered auxiliary-model hierarchical generalized extended stochastic gradient algorithm, a filtered auxiliary-model hierarchical multi-innovation generalized extended stochastic gradient algorithm, a filtered auxiliary-model hierarchical recursive generalized extended gradient algorithm, a filtered auxiliary-model hierarchical multi-innovation recursive generalized extended gradient algorithm, a filtered auxiliary-model hierarchical generalized extended least squares algorithm, and a filtered auxiliary-model hierarchical multi-innovation generalized extended least squares algorithm by using the auxiliary-model identification idea. The presented filtered auxiliary-model hierarchical generalized extended identification algorithms can be extended to other linear and nonlinear systems with colored noises.

This paper presents the development of fault-tolerant controller and their application for multirotor unmanned aerial vehicles – specifically an octorotor – in challenging environments e.g. nuclear power plant inspection or other dull, dirty and dangerous applications. This paper considers a combination of sliding mode control robustness properties (to deal with actuator faults) and control allocation (to automatically redistribute the control signals to healthy actuators, especially in the event of actuator failures). The resultant controller has the ability to operate in both fault-free and fault/failure conditions without reconfiguring the main baseline controller. The proposed controller also has the ability to operate for up to six rotor failures which represent an under-actuation condition i.e., a case when only two rotors are available. The under-actuation scenarios are conditions when most FTC schemes are not able to operate due to the lack of redundancy. The simulation results conducted on the nonlinear model with wind/gusts and sensor noise, show a good tracking performance under various fault-free and fault/failure scenarios (over-actuation, sufficient actuation and under-actuation conditions).

The transition from automated processes to mechanisms that manifest intelligence through cognitive abilities such as memorisation, adaptability and decision-making in uncertain contexts, has marked a turning point in the field of industrial systems, particularly in the development of cyber–physical systems and digital twins. This evolution, supported by advances in cognitive science and artificial intelligence, has opened the way to a new era in which systems are able to adapt and evolve autonomously, while offering more intuitive interaction with human users. This article proposes a systematic literature review to gather and analyse current research on Cognitive Cyber–Physical Systems (CCPS), Cognitive Digital Twins (CDT), and cognitive interoperability, which are pivotal in a contemporary Cyber–Physical Enterprise (CPE). From this review, we first seek to understand how cognitive capabilities that are traditionally considered as human traits have been defined and modelled in cyber–physical systems and digital twins in the context of Industry 4.0/5.0, and what cognitive functions they implement. We explore their theoretical foundations, in particular in relation to cognitive psychology and humanities definitions and theories. Then we analyse how interoperability between cognitive systems has been considered, leading to cognitive interoperability, and we highlight the role of knowledge representation and reasoning.

Over the past decade, the advancement of digital technology has significantly enhanced operations management in complex cyber-physical systems (CPSs), especially in the production and manufacturing sectors. In such systems, the physical and cyber spaces are generally connected through sensors, networking, and control actions. With the surge in available real-time data, automation and intelligence have become increasingly prevalent. However, full automation and sophisticated intelligence often remain challenging to achieve in real-world CPSs. Currently, many practical tasks in CPSs are best tackled through the integration of human cognitive skills with autonomous systems, highlighting the indispensable role that humans play in these settings. In this study, we present a framework for real-time decision-making and control in complex cyber-physical-human systems. The framework consists of three main modules: intelligent data processing, intelligent decision-making and control, and human-computer interaction. It is designed to provide a practical and implementable framework for supporting real-time decision-making and control in cyber-physical-human system applications. To demonstrate the applicability of the framework, we build a comprehensive decision support tool to manage several important real-time decision-making and control tasks at a container terminal. The tool is seamlessly integrated into the main operating system of the container terminal and aids decision-makers in making optimal decisions and generating appropriate control actions. The effectiveness of the tool is confirmed by observed improvements in several key operational efficiency indicators at the container terminal.

Nowadays, the use of robotic systems for inspection and maintenance is gaining importance due to the number of scenarios in which robots can operate. Indeed, robotic systems provide many advantages in harsh and hostile environments, improving workers’ safety and overall efficiency. Given their ability to perform different tasks, robotic manipulators constitute a significant proportion of the possible robotic systems employed in these environments. The category of manipulators is a heterogeneous group that comprises many different types of robots: non-redundant, redundant, and hyper-redundant manipulators, the latter being subdivided into discrete-joint manipulators and continuum manipulators. Among these types of robots, hyper-redundant manipulators play a crucial role in operating in challenging environments due to their ability to perform auxiliary tasks, such as obstacle avoidance and joint limits satisfaction. Furthermore, manipulators can be made of rigid or soft mechanisms and can be mobile, operating in aerial, ground, and underwater environments. The objective of this review article is to provide a reference point for researchers interested in modelling and controlling manipulators for inspection and maintenance in challenging environments.