Zobrazeno 1 - 10
of 42
pro vyhledávání: '"Pengzhan Chen"'
Adaptive Sliding Mode Control of an Electro-Hydraulic Actuator With a Kalman Extended State Observer
Autor:
Liming Lao, Pengzhan Chen
Publikováno v:
IEEE Access, Vol 12, Pp 8970-8982 (2024)
Electro-hydraulic actuators (EHAs) are key linear drive components in various industrial applications. This paper addresses the challenge of achieving precise displacement tracking control for EHAs with only noisy displacement measurements. We propos
Externí odkaz:
https://doaj.org/article/8fcfe7be84e44be4acbd7492f3f4eea0
Publikováno v:
Sensors, Vol 24, Iss 13, p 4178 (2024)
Visual ranging technology holds great promise in various fields such as unmanned driving and robot navigation. However, complex dynamic environments pose significant challenges to its accuracy and robustness. Existing monocular visual ranging methods
Externí odkaz:
https://doaj.org/article/7c45794cafd14028b9226824326748a3
Publikováno v:
Symmetry, Vol 16, Iss 4, p 449 (2024)
Symmetry is an important principle and characteristic that is prevalent in nature and artificial environments. In the three-dimensional packing problem, leveraging the inherent symmetry of goods and the symmetry of the packing space can enhance packi
Externí odkaz:
https://doaj.org/article/371b1ddefc0b4b409be8d19aeca07b4a
Publikováno v:
Algorithms, Vol 16, Iss 12, p 566 (2023)
This paper proposes a novel prediction model termed the social and spatial attentive generative adversarial network (SSA-GAN). The SSA-GAN framework utilizes a generative approach, where the generator employs social attention mechanisms to accurately
Externí odkaz:
https://doaj.org/article/de353fb34c0b468ebaeea7a285c48509
Autor:
Yongchao Zhang, Pengzhan Chen
Publikováno v:
Sensors, Vol 23, Iss 24, p 9802 (2023)
This paper proposes an improved Soft Actor–Critic Long Short-Term Memory (SAC-LSTM) algorithm for fast path planning of mobile robots in dynamic environments. To achieve continuous motion and better decision making by incorporating historical and c
Externí odkaz:
https://doaj.org/article/add8e1e04cf84faaa3d521e022831fa1
Publikováno v:
Sensors, Vol 23, Iss 24, p 9807 (2023)
Although numerous effective Simultaneous Localization and Mapping (SLAM) systems have been developed, complex dynamic environments continue to present challenges, such as managing moving objects and enabling robots to comprehend environments. This pa
Externí odkaz:
https://doaj.org/article/8bb1d49e82e640c58c9e4299ff335fda
Publikováno v:
Applied Sciences, Vol 12, Iss 10, p 5171 (2022)
Due to the advantages of their drive configuration form, skid-steering vehicles with independent wheel drive systems are widely used in various special applications. However, obtaining a reasonable distribution of the driving torques for the coordina
Externí odkaz:
https://doaj.org/article/69bdfa541c3b47478063cec17a12aaaa
Publikováno v:
Actuators, Vol 11, Iss 3, p 72 (2022)
Meta-reinforcement learning (meta-RL), used in the fault-tolerant control (FTC) problem, learns a meta-trained model from a set of fault situations that have a high-level similarity. However, in the real world, skid-steering vehicles might experience
Externí odkaz:
https://doaj.org/article/0a245ae9bbdf4762a66ee7c8619a42be
Metalearning-Based Fault-Tolerant Control for Skid Steering Vehicles under Actuator Fault Conditions
Publikováno v:
Sensors, Vol 22, Iss 3, p 845 (2022)
Using reinforcement learning (RL) for torque distribution of skid steering vehicles has attracted increasing attention recently. Various RL-based torque distribution methods have been proposed to deal with this classical vehicle control problem, achi
Externí odkaz:
https://doaj.org/article/301e3774ea27440cb9406f50d9b0eb99
Publikováno v:
Applied Sciences, Vol 8, Iss 3, p 407 (2018)
Vehicle navigation is widely used in path planning of self driving travel, and it plays an increasing important role in people's daily trips. Therefore, path planning algorithms have attracted substantial attention. However, most path planning method
Externí odkaz:
https://doaj.org/article/77bc2839484c4a66b69f84559d2a03b8