Annual Reviews in Control最新文献

With an increasing focus on climate action and energy security, an appropriate mix of renewable energy technologies is imperative. Despite having considerable global potential, wave energy has still not reached a state of maturity or economic competitiveness to have made an impact. Challenges include the high capital and operational costs associated with deployment in the harsh ocean environment, so it is imperative that the full energy harnessing capacity of wave energy devices, and arrays of devices in farms, is realised. To this end, control technology has an important role to play in maximising power capture, while ensuring that physical system constraints are respected, and control actions do not adversely affect device lifetime. Within the gamut of control technology, a variety of tools can be brought to bear on the wave energy control problem, including various control strategies (optimal, robust, nonlinear, etc.), data-based model identification, estimation, and forecasting. However, the wave energy problem displays a number of unique features which challenge the traditional application of these techniques, while also presenting a number of control ‘paradoxes’. This review articulates the important control-related characteristics of the wave energy control problem, provides a survey of currently applied control and control-related techniques, and gives some perspectives on the outstanding challenges and future possibilities. The emerging area of control co-design, which is especially relevant to the relatively immature area of wave energy system design, is also covered.

In this paper, leak detection and localization in water distribution networks will be reviewed. In particular, the paper presents the evolution of the methods from model-based towards data-based approaches, listing, describing and comparing the main and/or most recent methods of both categories. Besides, the practical applicability in real water utilities of different existing methods is discussed, outlining the advantages and limitations of model-based and data-driven methods for this task. A well-known case study is used to compare some of the more promising methods and illustrate their performances. Perspectives of the future evolution of the current existing methods are also provided.

The Kalman filter (KF) is a popular state estimation technique that is utilized in a variety of applications, including positioning and navigation, sensor networks, battery management, etc. This study presents a comprehensive review of the Kalman filter and its various enhanced models, with combining the Kalman filter with neural network methodologies. First, we provide a brief overview of the classical Kalman filter and its variants, including the extended Kalman filter (EKF), unscented Kalman filter (UKF), cubature Kalman filter (CKF), etc. It is pointed out that the traditional Kalman filter faces two main problems: system model and noise model parameter identification. To overcome these obstacles, researchers have developed novel solutions by integrating machine learning techniques with the Kalman filter. Secondly, this paper classifies the related models into two categories: both the internal cross-combination of the Kalman filter and neural network and their external combinations. Two different hybrid models and typical structures show that the hybrid model performs more accurately and robustly overall. Finally, the characteristic of the two hybrid models is summarized so that readers can understand them more intuitively.

Multi-time scale techniques based on singular perturbations and averaging theory are among the most powerful tools developed for the synthesis and analysis of feedback control algorithms. This paper introduces some of the recent advances in singular perturbation theory and averaging theory for continuous-time dynamical systems modeled as ordinary differential equations (ODEs), as well as for hybrid dynamical systems that combine continuous-time dynamics and discrete-time dynamics. Novel multi-time scale analytical tools based on higher-order averaging and singular perturbation theory are also discussed and illustrated via different examples. In the context of hybrid dynamical systems, a class of sufficient Lyapunov-based conditions for global stability results are also presented. The analytical tools are illustrated through various new architectures and algorithms within the context of adaptive and extremum-seeking systems. These tools are suitable for the study of model-free optimization and stabilization problems that require the synergistic use of continuous-time and discrete-time feedback. The paper aims to acquaint the reader with a range of modern tools for studying multi-time scale phenomena in optimization and control systems, providing some guidelines for future research in this field.

This article reviews the state-of-the-art modeling and control techniques for aerial robots such as quadrotor systems and presents several future research directions in this area. The review starts by introducing the benefits and drawbacks of classic physic-based dynamic modeling and control techniques. Subsequently, the manuscript presents the key challenges to augment or replace classic techniques with data-driven approaches that can offer several key benefits in terms of flight precision, safety, adaptation, and agility.

Active Queue Management (AQM) for mitigating Internet congestion has been addressed via various feedback control syntheses, especially P, PI, and PID regulators, by using a linear approximation where the “round trip time,” i.e., the delay, is assumed to be constant. This constraint is lifted here by using a nonlinear modeling with a variable delay, introduced more than 20 years ago. This delay, intimately linked to the congestion phenomenon, may be viewed as “ a flat output.” All other system variables, especially the control variable, i.e., the packet loss ratio, are expressed as a function of the delay and its derivatives: they are frozen if the delay is kept constant. This flatness-like property, which demonstrates the mathematical discrepancy of the linear approximation adopted until today, yields also our control strategy in two steps: Firstly, designing an open-loop control, thanks to straightforward Flatness-Based Control (FBC) techniques, and secondly, closing the loop via Model-Free Control (MFC) in order to take into account severe model mismatches, like, here, the number of TCP sessions. Several convincing computer simulations, which are easily implementable, are presented and discussed.

The regulatory endorsement of the International Maritime Organization (IMO) and the support of pivotal shipping market players in recent years motivate the investigation of the potential role that autonomous vessels play in the shipping industry. As the complexity and scale of the envisioned applications increase, research works gradually transform the focus from single-vessel systems to multi-vessel systems. Thus, autonomous multi-vessel systems applied in the shipping industry are becoming a promising research direction. One of the typical research directions is floating object manipulation by multiple tugboats.

This paper offers a comprehensive literature review of the existing research on floating object manipulation by autonomous multi-vessel systems. Based on the prior knowledge of object manipulation problems in multi-robot systems, four typical ways of maritime object manipulation are summarized: attaching, caging, pushing, and towing. The advantages and disadvantages of each manipulation way are discussed, including its typical floating object and application scenarios. Moreover, the aspects of control objective, control architecture, collision avoidance operation, disturbances consideration, and role of each involved vessel are analyzed for gaining insight into the approaches for solving these problems. Finally, challenges and future directions are highlighted to give possible inspiration.