Results 61 to 70 of about 16,024 (227)

A Model Reference Adaptive Control Approach to Terrain Following Flight Control System

Düzce Üniversitesi Bilim ve Teknoloji Dergisi
Automatic Flight Control System (AFCS) Terrain Following (TF) mode allows military aircraft to fly at a certain altitude above ground level at a low altitude. TF mode reduces the probability of aircraft detection by enemy airborne radars.
Berk İnan, İbrahim Aliskan
doaj   +1 more source

Adaptive Control of an Inverted Pendulum by a Reinforcement Learningbased LQR Method

Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 2023
Inverted pendulums constitute one of the popular systems for benchmarking control algorithms. Several methods have been proposed for the control of this system, the majority of which rely on the availability of a mathematical model.
Uğur Yıldıran
doaj   +1 more source

Modeling the Interaction Between two-Wheeled Self-Balancing Vehicle and its Rider

International Journal of Applied Mechanics and Engineering, 2013
The paper presents a modeling method and mathematical description of a two-wheeled self-balancing vehicle and its rider. A model of the rider that was used contains a model of the ankle joint, so we could determine the interaction between the rider and ...
M. Ciężkowski
doaj   +1 more source

Reguladores basados en observadores de perturbaciones: principios de funcionamiento y métodos de diseño

Revista Iberoamericana de Automática e Informática Industrial RIAI, 2022
Durante las últimas décadas, los reguladores lineales basados en observadores de perturbaciones han experimentado un interés creciente entre la comunidad científica, principalmente, debido a sus buenas propiedades para tolerar cambios o incertidumbres en
Alberto Castillo, Pedro García, Pedro Albertos   +2 more
doaj   +1 more source

Adaptive State Feedback Control Method Based on Recursive Least Squares

Elektronika ir Elektrotechnika, 2022
This study revealed an adaptive state feedback control method based on recursive least squares (RLS) that is introduced for a time-varying system to work with high efficiency. Firstly, a system identification block was created that gives the mathematical
Mehmet Latif Levent, Omer Aydogdu
doaj   +1 more source

Nonlocomotory Robotic Strategies for Dynamic Rotation Control in Terrestrial Robots: A Review

Advanced Robotics Research, EarlyView.
Terrestrial robots increasingly require rapid body rotation to maintain stability and agility in complex environments. This review shows nonlocomotory rotational control strategies that operate without ground contact, including reaction wheels, tails, bars, limbs, and thrusters.
Y. Liang, J. An, X. Zheng, D. Wang, S. Wang, X. Wang, P. Zhu, J. Pei, S. Li, J. Paripurana, Y. Han, Z. Tang, X. Gan, Q. He, X. Ma   +14 more
wiley   +1 more source

A State‐Adaptive Koopman Control Framework for Real‐Time Deformable Tool Manipulation in Robotic Environmental Swabbing

Advanced Robotics Research, EarlyView.
This work presents a state‐adaptive Koopman linear quadratic regulator framework for real‐time manipulation of a deformable swab tool in robotic environmental sampling. By combining Koopman linearization, tactile sensing, and centroid‐based force regulation, the system maintains stable contact forces and high coverage across flat and inclined surfaces.
Siavash Mahmoudi, Amirreza Davar, Dongyi Wang   +2 more
wiley   +1 more source

Vehicle Stability Analysis under Extreme Operating Conditions Based on LQR Control

Italian National Conference on Sensors, 2022
Under extreme working conditions such as high-speed driving on roads with a large road surface unevenness coefficient, turning on a road with a low road surface adhesion coefficient, and emergency acceleration and braking, a vehicle’s stability ...
Liping Wu, Ran Zhou, Junshan Bao, Guang Yang, F. Sun, Fangchao Xu, Junjie Jin, Qi Zhang, Weikang Jiang, Xiaoyou Zhang   +9 more
semanticscholar   +1 more source

Performance–Complexity Trade‐Offs in Battery Lifetime Prediction with Task‐Aware Transformers

Advanced Science, EarlyView.
FAST‐BatPro integrates convolutional feature extraction, flash Attention, and sparse attention for efficient battery lifetime prediction. Using limited early‐cycle data across multiple chemistries and operating conditions, it achieves robust accuracy while reducing inference latency, computational cost, and energy consumption.
Jingyuan Zhao, Misheng Cai, Zhenghong Wang, Yan Wang, Yuqi Li, Bo Dong, Hewu Wang, Andrew F. Burke, Simona Onori, Stephen J. Harris   +9 more
wiley   +1 more source

Transfer Learning for LQR Control

CoRR
In this paper, we study a transfer learning framework for Linear Quadratic Regulator (LQR) control, where (i) the dynamics of the system of interest (target system) are unknown and only a short trajectory of impulse responses from the target system is provided, and (ii) impulse responses are available from $N$ source systems with different dynamics. We
Taosha Guo, Fabio Pasqualetti
openaire   +2 more sources