Annual Reviews in Control最新文献

This tutorial paper presents recent work of the authors that extends the theory of Control Barrier Functions (CBFs) to address practical challenges in the synthesis of safe controllers for autonomous systems and robots. We present novel CBFs and methods that handle safety constraints (i) with time and input constraints under disturbances, (ii) with high-relative degree under disturbances and input constraints, and (iii) that are affected by adversarial inputs and sampled-data effects. We then present novel CBFs and adaptation methods that prevent loss of validity of the CBF, as well as methods to tune the parameters of the CBF online to reduce conservatism in the system response. We also address the pointwise-only optimal character of CBF-induced control inputs by introducing a CBF formulation that accounts for future trajectories, as well as implementation challenges such as how to preserve safety when using output feedback control and zero-order-hold control. Finally we consider how to synthesize non-smooth CBFs when discontinuous inputs and multiple constraints are present.

Modern autonomous systems, such as flying, legged, and wheeled robots, are generally characterized by high-dimensional nonlinear dynamics, which presents challenges for model-based safety-critical control design. Motivated by the success of reduced-order models in robotics, this paper presents a tutorial on constructive safety-critical control via reduced-order models and control barrier functions (CBFs). To this end, we provide a unified formulation of techniques in the literature that share a common foundation of constructing CBFs for complex systems from CBFs for much simpler systems. Such ideas are illustrated through formal results, simple numerical examples, and case studies of real-world systems to which these techniques have been experimentally applied.

A current trend in research on multi-agent control systems is to consider high-level task specifications that go beyond traditional control objectives and take into account the heterogeneity of each agent in the system, i.e., the different capabilities of the agents in terms of actuation, sensing, communication and computation. This article provides an overview of our work on the problem of control of heterogeneous multi-agent systems under both spatial and temporal constraints as well as our perspective on the challenges and open problems associated with the consideration of such spatiotemporal constraints. Initially, we review a set of control strategies introduced by the authors addressing the satisfaction of cooperative tasks such as formation control as well as individual objectives such as reference tracking. The satisfaction of those objectives is ensured using prescribed performance control. Building upon these approaches we then review recent results on control under high-level spatiotemporal objectives expressed in Signal Temporal Logic, a formal language that allows to express complex spatial tasks that must be satisfied within pre-defined deadlines. Theoretical results considering multi-agent systems with various capabilities under spatiotemporal constraints are presented.

The selection of an appropriate control strategy is essential for ensuring safe operation in autonomous driving. While numerous control strategies have been developed for specific driving scenarios, a comprehensive comparative assessment of their performance using the same tuning methodology is lacking in the literature. This paper addresses this gap by presenting a systematic evaluation of state-of-the-art model-free and model-based control strategies. The objective is to evaluate and contrast the performance of these controllers across a wide range of driving scenarios, reflecting the diverse needs of autonomous vehicles. To facilitate the comparative analysis, a comprehensive set of performance metrics is selected, encompassing accuracy, robustness, and comfort. The contributions of this research include the design of a systematic tuning methodology, the use of two novel metrics for stability and comfort comparisons and the evaluation through extensive simulations and real tests in an experimental instrumented vehicle over a wide range of trajectories.

This article presents a complete review of the modeling and control schemes for overhead cranes operating in 2D and 3D spaces published to date. The modeling schemes including the pendulum-like models with rigid and flexible links are reviewed and their key characteristics are studied. Subsequently, an overview of the control methods developed for such models is presented. Afterward, a new simulation-oriented model enabling to capture both cables’ dynamic and global nonlinearities caused by the pendulation is developed, and different control methods that exist in the literature are evaluated and compared based on this model using numerical experiments. In the end, several research gaps are identified to be considered in future works.

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.