Annual Reviews in Control最新文献

This paper presents a unified framework for exponential stability analysis of linear stationary systems with irrational transfer functions in the space of an arbitrary number of unknown parameters. Systems described by irrational transfer functions may be of infinite dimension, typically having an infinite number of poles and/or zeros, rendering their stability analysis more challenging as compared to their finite-dimensional counterparts. The analysis covers a wide class of distributed parameter systems, time delayed systems, or even fractional systems. First, it is proven that, under mild hypotheses, new poles may appear to the right of a vertical axis of abscissa $\gamma$ (imaginary axis, when $\gamma =0$) through a continuous variation of parameters only if existing poles to the left of $\gamma$ cross the vertical axis. Hence, by determining parametric values for which the crossing occurs, known as stability crossing sets (SCS), the entire parametric space is separated into regions within which the number of right-half poles (including multiplicities) is invariant. Based on the aforementioned result, a constraint satisfaction problem is formulated and a robust estimation algorithm, from interval arithmetics that uses contraction and bisection, is used to solve it. Applications are provided for determining the SCS of (i) a controlled parabolic 1D partial differential equation, namely the heat equation, in finite and semi-infinite media, (ii) time-delay rational systems with distributed and retarded type delays, (iii) fractional systems, providing stability results even for incommensurate differentiation orders.

Operating electrolyzers for producing green hydrogen is a critical emerging issue because of either the broader use of hydrogen for several scopes or the short life span and efficiency of these components. Digital Twin offers a new opportunity to effectively face these problems by improving online control and providing fault detection, diagnosis, and prediction services. Since the Digital Twin is, in fact, a virtual mirror of a real system continuously updated by information received from the field, it allows it to swiftly react to small signals of departure from standard or optimal conditions. Although Digital Twins are widely applied in different fields, comprehensive guidance on developing and designing a Digital Twin in the literature is still lacking. This manuscript aims to provide a comprehensive guide on how to build the Digital Twin of a PEM-Electrolyzer. In particular, the architecture of the Digital Twin is initially presented, then all its components are analyzed, showing the steps to be performed to build a Digital Twin for operating PEM-Electrolyser system.

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 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).

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.

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.