Results 11 to 20 of about 1,298,312 (212)
Learning Sequential Composition Control [PDF]
IEEE Transactions on Cybernetics, 2016 Sequential composition is an effective supervisory control method for addressing control problems in nonlinear dynamical systems. It executes a set of controllers sequentially to achieve a control specification that cannot be realized by a single controller.
Esmaeil Najafi +2 more
openaire +4 more sources
Toward Multi-Agent Reinforcement Learning for Distributed Event-Triggered Control
, 2023 Event-triggered communication and control provide high control performance in networked control systems without overloading the communication network.
Kesper, Lukas +2 more
core +2 more sources
rickstaa/stable-learning-control
, 2023 A framework for training theoretically stable (and robust) Reinforcement Learning control ...
Rick Staa +3 more
core +1 more source
Journal of Advanced Mechanical Design, Systems, and Manufacturing, 2008 This paper describes the motion control of hyper redundant robots using a learning control scheme based on linear combination of error history. The learning control scheme is formulated with three elements: general solution of inverse kinematics with ...
Daisuke MATSUURA, Nobuyuki IWATSUKI
doaj +1 more source
Adaptive-Attentive Geolocalization From Few Queries: A Hybrid Approach
Frontiers in Computer Science, 2022 We tackle the task of cross-domain visual geo-localization, where the goal is to geo-localize a given query image against a database of geo-tagged images, in the case where the query and the database belong to different visual domains.
Valerio Paolicelli +5 more
doaj +1 more source
Learning Controllers for Industrial Robots [PDF]
Machine Learning, 1996 One of the most significant cost factors in robotics applications is the design and development of real-time robot control software. Control theory helps when linear controllers have to be developed, but it doesn’t sufficiently support the generation of non-linear controllers, although in many cases (such as in compliance control), nonlinear control is
Baroglio, C. +4 more
openaire +2 more sources
A Survey of Robotics Control Based on Learning-Inspired Spiking Neural Networks
Frontiers in Neurorobotics, 2018 Biological intelligence processes information using impulses or spikes, which makes those living creatures able to perceive and act in the real world exceptionally well and outperform state-of-the-art robots in almost every aspect of life. To make up the
Zhenshan Bing +4 more
doaj +1 more source
A new adaptive controller of facts-based FMRLC aimed at improving power system stability [PDF]
Serbian Journal of Electrical Engineering, 2008 Various control techniques using Advanced Super-conducting Magnetic Energy Storage (ASMES) aimed at improving power system stability have been proposed. As fuzzy controller has proved its value in some applications, the number of investigations employing
Abdellatif Naceri +2 more
doaj +1 more source
Secure energy efficiency: Power allocation and outage analysis for SWIPT-in-DAS based IoT
ICT Express, 2022 In this paper, we study secure energy efficiency (SEE) for simultaneous wireless information and power transfer (SWIPT) in a distributed antenna system (DAS) based IoT network.
Aaqib Bulla, Shahid M. Shah
doaj +1 more source
IEEE Access, 2021 A well-designed demand response (DR) program is essential in smart home to optimize energy usage according to user preferences. In this study, we proposed a multiobjective reinforcement learning (MORL) algorithm to design a DR program.
Song-Jen Chen, Wei-Yu Chiu, Wei-Jen Liu
doaj +1 more source