IEEE Control Systems Magazine

Journal: IEEE Control Systems Magazine

Abbreviation

IEEE control syst.

Publisher

IEEE

ISSN

0272-1708
1066-033X
1941-000X

Show all metadata

Search Results

Publications 1 - 10 of 24

Formal Verification and Control With Conformal Prediction: Practical Safety Guarantees For Autonomous Systems
Lindemann, Lars; Zhao, Yiqi; Yu, Xinyi; et al. (2025)
IEEE Control Systems Magazine
The design of autonomous systems, which are becoming increasingly learning enabled, has attracted much attention within the research community. Research in this area promises to enable many future technologies, such as autonomous driving, intelligent transportation, and robotics. In recent years, great progress has been made in the design of learning-enabled components (LECs), for example, with neural networks for perception tasks, such as object detection [1], [2], localization and state estimation [3], [4], and trajectory prediction [5], [6], [7]; for decision-making tasks, such as motion and behavior planning [8], [9]; and for low-level control [10], [11], [12]. However, the integration of LECs into safety-critical autonomous systems is limited by their fragility and can result in unsafe behavior, for example, inaccurate and nonrobust object detectors in self-driving cars. The fragility of LECs is the result of highly nonconvex learning problems, distribution shifts from training to the deployment domain, and a lack of model robustness [13], [14]. Unfortunately, these safety challenges are further amplified by the complexity of modern autonomous systems that operate in uncertain and dynamic environments, where traditional approaches for localization and mapping may fail to provide guarantees, for example, simultaneous localization and mapping techniques [4], [15] or Kalman/particle filters [16], [17], [18].
Distributed Generalized Nash Equilibrium Seeking: An Operator-Theoretic Perspective
Belgioioso, Giuseppe; Yi, Peng; Grammatico, Sergio; et al. (2022)
IEEE Control Systems Magazine
Generalized games model interactions between a set of selfish decision makers, called players or agents, where both the objective function and the feasible decision set of each player may depend on strategies of the competitors. Such games arise, for example, when agents compete for a share of some common but limited resources. For instance, consider a set of vehicles sharing the road, set of radio channels competing for bandwidth, or set of companies servicing an economic market. They can each be modeled as players/agents in a game, competing for a portion of available resources, that is, road capacity, total bandwidth, or market share. As resources are limited, player choices are bound together by a coupling-capacity constraint. Essentially, in a generalized game, or one with coupling constraints, the set of available choices an agent has depends on the choices of other agents. Thus, a player cannot simply optimize their own objective function without considering the decisions of the others, even though this objective might not depend on him or her. The relevant equilibrium (namely, the solution concept) for noncooperative decision making with coupling constraints is called a generalized Nash equilibrium (GNE).
Data-Driven Control Based on the Behavioral Approach: From Theory to Applications in Power Systems
Markovsky, Ivan; Huang, Linbin; Dörfler, Florian (2023)
IEEE Control Systems Magazine
Covert misappropriation of networked control systems
Smith, Roy (2015)
IEEE Control Systems Magazine
The increasing availability of Internet connectivity and networked actuation and sensing components has supported the growth in control systems operated over public networks. Controllers and plants no longer need to be physically colocated as measurements and actuation signals can be sent digitally. Supervisory systems can monitor and control geographically widespread components. However, such systems are now exposed to the risk of remote interference. A feedback structure that allows an attacker to take over control of the plant while remaining hidden from the control and supervisory system(s) is presented. The objective is not to facilitate such attacks but rather to make clear the degree to which the takeover of plant control can be hidden when a sophisticated attacker has some plant knowledge and signal intervention capabilities.
Data-Driven Control: Part Two of Two: Hot Take: Why not go with Models?
Dörfler, Florian (2023)
IEEE Control Systems Magazine
A recurring question that all authors of this special issue encounter is, "Why not go with models?" Two terms need to be clarified: In this context, a model is understood as a parametric system representation often endowed with an interpretable structure, for example, a state-space representation with a readily discernible F = m center dot a equation. Further, the term data-driven control, as we employ it in this special issue, is not just about using data from a black box to inform decision making. Researchers are exploring different paradigms, among others, model-based control design, where the model and uncertainty estimates are learned from data using contemporary system identification and uncertainty quantification techniques. In classical adaptive control terminology [1], [2], this two-stage approach is referred to as indirect. In contrast, direct data-driven control bypasses models in the decision making; see Figure 1 for a graphical illustration of the two paradigms. Hence, the more precise question should be, "When should we embrace direct or indirect data-driven control?" I will delve into the expected "it depends" answer in this "Editorial" column.
Plasma control in ITER
Lister Jo B.; Portone, Alfredo; Gribov, Yuri (2006)
IEEE Control Systems Magazine
Kernel Methods and Gaussian Processes for System Identification and Control: A Road Map on Regularized Kernel-Based Learning for Control
Carè, Algo; Carli, Ruggero; Dalla Libera, Alberto; et al. (2023)
IEEE Control Systems Magazine
The commonly adopted route to control a dynamic system and make it follow the desired behavior consists of two steps. First, a model of the system is learned from input–output data, a task known as system identification in the engineering literature. Here, an important point is not only to derive a nominal model of the plant but also confidence bounds around it. The information coming from the first step is then exploited to design a controller that should guarantee a certain performance also under the uncertainty affecting the model. This classical way to control dynamic systems has recently been the subject of new intense research, thanks to an interesting cross-fertilization with the field of machine learning. New system identification and control techniques have been developed with links to function estimation and mathematical foundations in reproducing kernel Hilbert spaces (RKHSs) and Gaussian processes (GPs). This has become known as the Gaussian regression (kernel-based) approach to system identification and control . It is the purpose of this article to give an overview of this development (see “Summary”).
The Balancing Cube
Trimpe, Sebastian; D'Andrea, Raffaello (2012)
IEEE Control Systems Magazine
Control of the Swiss Free Electron Laser: Methods for Precision Control of Pulsed-Mode Accelerator Beams
Rezaeizadeh, Amin; Schilcher, Thomas; Smith, Roy (2017)
IEEE Control Systems Magazine
The SwissFeL is capable of generating high-power, tunable X-ray pulses that are used to study molecular structure and dynamic processes at extremely fast timescales. The FeL effectively acts as a femtosecond photographic flash and will allow researchers to observe the creation of molecules in chemical reactions or study the detailed structure of proteins. The X-rays are generated by accelerating a pulsed beam of electrons through a high-energy, pulsed, radio-frequency field—to an energy of 5.8 GeV—and then a transverse periodic magnetic field. The accelerator is a series of 113 evacuated tube-like structures, energized by 32 high-power (up to 50 MW peak) amplifiers. The amplifiers are switched on for only 1–3 μs as each beam pulse is fired, and the entire process repeats 100 times per second. Precise control of each amplifier is applied every 4.2 ns. The control methods described involve learning the subtleties of the effect of adjustments on the beam pulse and applying these as corrections to the next pulse. experiments on full-scale amplifiers and shorter beam-lines show that these corrections provide the electron beam quality and repeatability for the complete FEL system.
Control of hybrid electric vehicles
Sciarretta, Antonio; Guzzella, Lino (2007)
IEEE Control Systems Magazine

Publications 1 - 10 of 24

Journal: IEEE Control Systems Magazine

Abbreviation

Publisher

Journal Volumes

ISSN

Description

Filters

Search Results