Annual Reviews in Control最新文献

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.

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.

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.

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.

This paper presents an optimization-based perspective for incorporating disturbance decoupling constraints into controller synthesis, which paves the way for utilizing numerical optimization tools. We consider the constraints arising from the following sets of static state feedback: (i) The set of all disturbance decoupling controllers; (ii) The set of all disturbance decoupling and stabilizing controllers. To inner approximate these sets by means of matrix equations or inequalities, we provide a unifying review of the relevant results of the geometric control theory. The approximations build on the characterization of controlled invariant subspaces in terms of the solvability of a linear matrix equation (LME) involving the state feedback. The set (i) is inner approximated through the LME associated with any element of an upper semilattice generated by controlled invariant subspaces. The set (ii) is inner approximated through a bilinear matrix inequality (BMI) and the LME associated with any element of a different upper semilattice generated by internally stabilizable controlled invariant subspaces. However, the resulting inner approximations depend on the subspaces chosen from the semilattices. It is shown that a specific (internally stabilizable) self-bounded controlled invariant subspace, which is the best choice regarding eigenvalue assignment, yields the largest inner approximation for both of the sets among (internally stabilizable) self-bounded controlled invariant subspaces. The inner approximations exactly characterize the controller sets under particular structural conditions. We have been driven by two primary motivations in investigating inner approximations for the sets above: (i) Enable the formulation of a variety of equality (and inequality) constrained optimization problems, where cost functions, such as a norm of the state feedback, can be minimized over a large subset of the set of all disturbance decoupling (and stabilizing) controllers; (ii) Introduce the disturbance decoupling constraints to members of the control systems community who might not be quite familiar with the elegant geometric state-space theory, similar to the authors themselves. This can add another dimension to research endeavors in resilient control of networked multi-agent systems.

The use of wireless communication within the civil nuclear industry can bring many benefits over wired solutions, such as reducing lifecycle costs and enabling new applications in asset and process management. This paper will discuss aspects of wireless communication in industrial control systems, i.e. termed wireless control systems, of the civil nuclear industry. In this respect, we will review previous use of wireless communication in the nuclear industry, and provide the results of a recent feasibility study of wireless communication for an industrial, civil nuclear control system. The studied use case was of an advanced nuclear modular reactor, the Stable Salt Reactor (SSR), and the augmentation of one of its control systems, the refuelling control system, with wireless communication. Hence, in contrast to previous work on wireless control systems, this paper here will focus on the complex and rigorous processes required for regulated safety which have to be followed to allow for wireless control to be implemented in the nuclear civil sector. The following analysis and design procedure was followed: (a) the decision process for choosing the refuelling control system, (b) the review for a suitable communication protocol and technology, the analysis for placement of wireless transceivers for sensors and actuators, (c) the analysis for wireless communication integrity, (d) the basic analysis and guidelines for control system robustness under packet loss, (e) the discussion of possible self-powering options and (f) the safety analysis of the control system under communication failure. Our initial hypothesis is that wireless control systems in Nuclear Applications can improve asset integrity. Control systems can be made more robust and secure to external influences by securely communicating control responses and asset information within a Nuclear Plant. Safety is also improved by reducing the number of operator interactions required for servicing connections, as failures are reduced overall. The removal of power/data harnesses from in-reactor applications can enable faster deployment and replacement of instrumentation for new builds, existing plants and decommissioning.