Annual Reviews in Control最新文献

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.

Risk-sensitive control has received considerable interest since the seminal work of Howard and Matheson (Howard and Matheson, 1971/72) because of its ability to account for fluctuations about the mean, its connection with $H_{\infty }$ control, and its application to financial mathematics. In this article we attempt to put together a comprehensive survey on the research done on ergodic risk-sensitive control over the last four decades.

So-called robot–object Lagrangian systems consist of a class of nonsmooth underactuated complementarity Lagrangian systems, with a specific structure: an “object” and a “robot”. Only the robot is actuated. The object dynamics can thus be controlled only through the action of the contact Lagrange multipliers, which represent the interaction forces between the robot and the object. Juggling, walking, running, hopping machines, robotic systems that manipulate objects, tapping, pushing systems, kinematic chains with joint clearance, crawling, climbing robots, some cable-driven manipulators, and some circuits with set-valued nonsmooth components, belong this class. This article aims at presenting their main features, then many application examples which belong to the robot–object class, then reviewing the main tools and control strategies which have been proposed in the Automatic Control and in the Robotics literature. Some comments and open issues conclude the article.

A longstanding open problem of systems neuroscience is to understand how the brain calibrates thousands of reflexes to achieve near instantaneous disturbance rejection. While reflexes typically act locally at the site of sensory measurements, the adaptation of reflex gains is the result of an ingenious architecture in which knowledge of disturbances is transferred from the cerebellum to the deep cerebellar nuclei or the brainstem. This paper investigates the use of control theory as the mathematical foundation to explain the mechanisms by which such forms of learning, as well as forgetting, manifest themselves in systems neuroscience. Particularly, we use adaptive control and averaging theory to model the computations performed in learning appropriate reflex gains. While forgetting is perceived as counter-productive to learning, we show that if incorporated correctly, it can endow the much needed robustness to train thousands of reflexes without interfering with their adaptation. This is accomplished using the $\mu$-modification which achieves robustness of adaptive schemes through the estimation of exciting subspaces. Our techniques are combined in a comprehensive model, with simulations illustrating their effectiveness.

We propose a method for designing policies for convex stochastic control problems characterized by random linear dynamics and convex stage cost. We consider policies that employ quadratic approximate value functions as a substitute for the true value function. Evaluating the associated control policy involves solving a convex problem, typically a quadratic program, which can be carried out reliably in real-time. Such policies often perform well even when the approximate value function is not a particularly good approximation of the true value function. We propose value-gradient iteration, which fits the gradient of value function, with regularization that can include constraints reflecting known bounds on the true value function. Our value-gradient iteration method can yield a good approximate value function with few samples, and little hyperparameter tuning. We find that the method can find a good policy with computational effort comparable to that required to just evaluate a control policy via simulation.

In this paper, we focus on the optimal operation of a multi-agent system affected by uncertainty. In particular, we consider a cooperative setting where agents jointly optimize a performance index compatibly with individual constraints on their discrete and continuous decision variables and with coupling global constraints. We assume that individual constraints are affected by uncertainty, which is known to each agent via a private set of data that cannot be shared with others. Exploiting tools from statistical learning theory, we provide data-based probabilistic feasibility guarantees for a (possibly sub-optimal) solution of the multi-agent problem that is obtained via a decentralized/distributed scheme that preserves the privacy of the local information. The generalization properties of the data-based solution are shown to depend on the size of each local dataset and on the complexity of the uncertain individual constraint sets. Explicit bounds are derived in the case of linear individual constraints. A comparative analysis with the cases of a common dataset and of local uncertainties that are independent is performed.

In this survey, we describe controlled interacting particle systems (CIPS) to approximate the solution of the optimal filtering and the optimal control problems. Part I of the survey is focussed on the feedback particle filter (FPF) algorithm, its derivation based on optimal transportation theory, and its relationship to the ensemble Kalman filter (EnKF) and the conventional sequential importance sampling–resampling (SIR) particle filters. The central numerical problem of FPF—to approximate the solution of the Poisson equation—is described together with the main solution approaches. An analytical and numerical comparison with the SIR particle filter is given to illustrate the advantages of the CIPS approach. Part II of the survey is focussed on adapting these algorithms for the problem of reinforcement learning. The survey includes several remarks that describe extensions as well as open problems in this subject.