Annual Reviews in Control最新文献

Lower-limb prostheses aim to restore ambulatory function for individuals with lower-limb amputations. While the design of lower-limb prostheses is important, this paper focuses on the complementary challenge—the control of lower-limb prostheses. Specifically, we focus on powered prostheses, a subset of lower-limb prostheses, which utilize actuators to inject mechanical power into the walking gait of a human user.

In this paper, we present a review of existing control strategies for lower-limb powered prostheses, including the control objectives, sensing capabilities, and control methodologies. We separate the various control methods into three main tiers of prosthesis control: High-level control for task and gait phase estimation, mid-level control for desired torque computation (both with and without the use of reference trajectories), and low-level control for enforcing the computed torque commands on the prosthesis. In particular, we focus on the high- and mid-level control approaches in this review. Additionally, we outline existing methods for customizing the prosthetic behavior for individual human users. Finally, we conclude with a discussion on future research directions for powered lower-limb prostheses based on the potential of current control methods and open problems in the field.

This work proposes an operational management approach for water distribution networks (WDNs) that can detect and localize leakages while also mitigating contamination resulting from these leaks. The primary emphasis of this work is the development of a contamination mitigation control scheme. A leak typically leads to a drop in network pressure that increases the risk of contamination. A leakage localization algorithm is responsible for detecting and localizing the leakage in the WDN. When a leak is detected in the network the contamination mitigation control is activated. The flow and pressure settings of the pumps are regulated by the contamination mitigation control in an optimal manner to minimize the risk of contamination. The entire framework is tested on the Smart Water Infrastructure Laboratory situated at Aalborg University, Denmark and a large-scale benchmark water network, which is part of a city network, L-town.

The purpose of this Special Section is to provide an account of the state-of-the-art and perspectives for future research in the design and analysis of monitoring and control methods for smart water systems. This paper provides an overview of the six articles in the special section. Specifically, the special section consists of four review articles, as well as one vision and one tutorial article. These articles provide a review of leakage detection and isolation, a review of contamination event diagnosis, a review of state- space modelling of multiple reacting species in drinking water systems, a review of the application of model predictive control in Water Systems, a vision of optimizing pressure management and self-cleaning, as well as a tutorial for leakage detection and mitigation of potential contamination risk.

The past few decades have witnessed a prevalence of applying dynamical models to the study of social networks. This paper reviews recent advances in the investigation of social networks with a predominant focus on agent-based models. Starting from classical models of opinion dynamics, we survey several recently developed models on opinion formation and social power evolution. These models extend the classical models’ cognitive assumption that individuals’ opinions evolve on a single issue by incorporating various sociological or psychological hypotheses to account for the evolution of opinions over multiple or a sequence of interdependent issues. We summarize basic results on the asymptotic behaviors of these models and discuss their sociological interpretations. In addition, we show how these models play a role in the emergence of collective intelligence by applying them to a naïve learning setting. Novel results that reveal how individuals successfully learn an unknown truth over issue sequences are presented. Finally, we conclude the paper and discuss potential directions for future research.

Thanks to their road safety potential, automated vehicles are rapidly becoming a reality. In the last decade, automated driving has been the focus of intensive automotive engineering research, with the support of industry and governmental organisations. In automated driving systems, the path tracking layer defines the actuator commands to follow the reference path and speed profile. Model predictive control (MPC) is widely used for trajectory tracking because of its capability of managing multi-variable problems, and systematically considering constraints on states and control actions, as well as accounting for the expected future behaviour of the system. Despite the very large number of publications of the last few years, the literature lacks a comprehensive and updated survey on MPC for path tracking. To cover the gap, this literature review deals with the research conducted from 2015 until 2021 on model predictive path tracking control. Firstly, the survey highlights the significance of MPC in the recent path tracking control literature, with respect to alternative control structures. After classifying the different typologies of MPC for path tracking control, the adopted prediction models are critically analysed, together with typical optimal control problem formulations. This is followed by a summary of the most relevant results, which provides practical design indications, e.g., in terms of selection of prediction and control horizons. Finally, the most recent development trends are analysed, together with likely areas of further investigations, and the main conclusions are drawn.

Water distribution systems are susceptible to contamination events, which can occur due to naturally occurring events, accidents or even malicious attacks. When a contamination event occurs, dangerous substances infiltrating the network may be consumed thereby deteriorating the consumers’ health and possibly affecting the economy. Advances in sensor and actuator technologies are enabling water networks to become smarter and more resilient to these types of events. This paper provides a broad review of the theoretical, modeling, and computational developments in the area of contamination event diagnosis for water distribution systems. Research is segmented into three main tasks, summarized as “Preparedness”, “Event Detection and Isolation” and “Emergency Event Management”. The key research topics from each task are described within a unified systems-theoretic mathematical framework, and their open challenges are discussed.

This article provides an overview of a new approach to designing controllers for nonlinear systems using data-driven control. Data-driven control is an important area of research in control theory, and this novel method offers several benefits. It can recreate from a data-centred perspective many of the results available in the model-based case, including local stabilization based on Taylor or polynomial expansion, absolute stabilization, as well as approximate and exact feedback linearization. Moreover, the method is analytically and computationally simple, and permits to infer regions of attraction and invariant sets, also when the data are corrupted by noise.

This paper reviews the evolution of Internal Model Control (IMC) techniques developed so far for unstable processes. The IMC strategy has shown significant results over the past two decades, including recent inclusions of fractional-order approaches. After a comprehensive study of various methods, the critical tuning methods and structural changes are clearly accumulated with their significance and limitation concerning controlling unstable time-delay systems. The comparisons with main structural changes and filter designs are also included in the numerical study and in discussion. Finally, the key research gaps and future motivations are indicated in the IMC approaches, considering available methods in the literature.