In this paper we address the problem of path planning in an unknown environment with an aerial robot. The main goal is to safely follow the planned trajectory by avoiding obstacles. The proposed approach is suitable f...
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Fault detection in electric drives is crucial for ensuring operational reliability and minimizing downtime. This paper provides a brief overview of the methods based on machine learning used for fault detection in ele...
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ISBN:
(数字)9798350389234
ISBN:
(纸本)9798350353051
Fault detection in electric drives is crucial for ensuring operational reliability and minimizing downtime. This paper provides a brief overview of the methods based on machine learning used for fault detection in electric drives. Additional this research presents a machine learning approach that utilizes the Long Short-Term Memory Autoencoder model for fault detection deal with bearing faults. A flowchart illustrating the implementation steps of the Long Short-Term Memory Autoencoder algorithm is included. Additionally, a case study on bearing faults in an electric drive system is presented to demonstrate the advantage of the proposed approach. By training the Long Short-Term Memory Autoencoder on operational data, accurate prediction of bearing faults is achieved, enabling timely maintenance interventions. It is shown that in the 7th epoch of training, the model achieved a notably high accuracy more than $\mathbf{9 9 \%}$. The results highlight the potential of Long Short-Term Memory Autoencoder models as a valuable tool for fault detection in electric drives, thereby improving system reliability and performance.
Drones are increasingly operating autonomously, and the need for extending drone power autonomy is rapidly increasing. One of the most promising solutions to extend drone power autonomy is the use of docking stations ...
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Accurate parameter estimation in ship dynamics models is pivotal for enhancing navigation precision, optimizing controlsystems, and ensuring maritime safety. This study explores two distinct methodologies: the “Grey...
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ISBN:
(数字)9798331511241
ISBN:
(纸本)9798331511258
Accurate parameter estimation in ship dynamics models is pivotal for enhancing navigation precision, optimizing controlsystems, and ensuring maritime safety. This study explores two distinct methodologies: the “Grey Box” approach, where the governing equations of ship dynamics are known but certain parameters remain unidentified, and the “Black Box” approach, which relies entirely on trajectory data without any prior knowledge of the underlying mathematical models. Leveraging machine learning techniques, specifically Long Short-Term Memory (LSTM) networks, alongside optimization algorithms like L-BFGS-B, this research aims to evaluate and compare the efficacy of both approaches in estimating model parameters and predicting ship trajectories. The findings demonstrate that while the Grey Box approach benefits from incorporating physical laws for parameter tuning, the Black Box method offers flexibility in modeling complex, nonlinear dynamics purely based on empirical data. The integrated use of these methodologies provides a robust framework for enhancing the accuracy and reliability of ship dynamics models, contributing significantly to maritime engineering applications.
Hyperexponential stability is investigated for dynamical systems with the use of both, explicit and implicit, Lyapunov function methods. A nonlinear hyperexponential control is designed for stabilizing linear systems....
Hyperexponential stability is investigated for dynamical systems with the use of both, explicit and implicit, Lyapunov function methods. A nonlinear hyperexponential control is designed for stabilizing linear systems. The tuning procedure is formalized in LMI form. Through numeric experiments, it is observed that the proposed hyperexponential control is less sensitive with respect to noises and discretization errors than its finite-time analog. It also demonstrates better performance in the presence of delays as well. Theoretical results are supported by numerical simulations.
作者：
Zulkifli MansorAddie IrawanRobotics
Intelligent Systems & Control Engineering (RiSC) research group Faculty of Electrical & Electronics Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah Pekan Pahang Malaysia
The paper presents a Coxa Coordination System-Based Hierarchical control Framework (CCS-HCF) for a Bipedal Wheel-Leg Robot with a Sprawling Mechanism (BWLS), aimed at enhancing the flexibility navigation in rugged env...
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ISBN:
(数字)9798350391398
ISBN:
(纸本)9798350391404
The paper presents a Coxa Coordination System-Based Hierarchical control Framework (CCS-HCF) for a Bipedal Wheel-Leg Robot with a Sprawling Mechanism (BWLS), aimed at enhancing the flexibility navigation in rugged environments. The proposed method leverages the Coxa-Based Coordinate System (CCS) for optimizing leg motion and trajectory, facilitating stable and efficient locomotion across challenging terrains. The proposed method’s effectiveness is verified through various simulation works, highlighting the importance of specific parameter settings in the proposed CCSHCF, such as a Coxa angle for sprawling and mammalian postures, along with a swing height position. These configurations are crucial for maintaining stability and balance during operation. The findings demonstrate the bipedal wheel-legged robot such as BWLS system’s with the proposed CC-SHCF ready to be integrated by various stability and robust control layers for stable walking and skiing.
This paper presents the modeling and analysis of the novel Bipedal Wheel-Legged Robot with Sprawling Legs Mechanism (BWLS) utilizing multi body dynamics approaches and the proposed Coxa-Based Coordinate System (CCS) i...
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ISBN:
(数字)9798350349788
ISBN:
(纸本)9798350349795
This paper presents the modeling and analysis of the novel Bipedal Wheel-Legged Robot with Sprawling Legs Mechanism (BWLS) utilizing multi body dynamics approaches and the proposed Coxa-Based Coordinate System (CCS) in kinematics modeling. The primary focus lies in transitioning the conceptual model of the BWLS into the Simscape Multibody (SM) environment. The proposed CCS is subjected to verification through the implementation of a Hierarchical control Framework (HCF) utilizing a simple stand-up input trajectory. Through the analysis, initial angular positions and stable range angular joints are determined, revising previous determinations based on hardware structure. Furthermore, employing the Forward Kinematics (FK) model, the stable boundary is identified through spatial leg motion plots. The reliability of the proposed CCS kinematics model is affirmed, rendering the model ready for further in-depth studies and applications.
This article presents the results of implementing a digital signal processing method for analyzing the acoustic emission (AE) signature and detecting defects such as cracks and pores. The signal processing method is b...
This article presents the results of implementing a digital signal processing method for analyzing the acoustic emission (AE) signature and detecting defects such as cracks and pores. The signal processing method is based on a cascade connection of digital high-pass filters and ensures the selection of informative signals and increases the signal-to-interference ratio. In order to control the formation of defects in the process of selective laser melting, the paper presents the results of experimental testing of the cascade filtration method for detecting defects in the internal structure such as cracks and pores. The signal components recorded during the development of defects during the growth of additive manufacturing products were extracted. AE signatures were analyzed and statistical relationships were assessed. The relationship between the parameters of the AE signal and the values of laser radiation power has been identified, which characterizes the process of defect formation.
This paper proposes a combined initial alignment algorithm for strapdown inertial navigation system, in which the initial alignment is carried out in two stages during the process of preflight preparations while the a...
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Wind turbines based on the Magnus effect open new prospects in terms of energy generation for areas with low average wind speeds. The main feature of these turbines is rotating cylinders which are used to create lift ...
Wind turbines based on the Magnus effect open new prospects in terms of energy generation for areas with low average wind speeds. The main feature of these turbines is rotating cylinders which are used to create lift instead of blades. Due to this unconventional design, different parameters of the cylinder, such as mass and moment of inertia, can greatly affect the dynamic characteristics of such energy systems. The goal of this paper was to present an approach to structural optimization of a cylinder flange of the experimental Magnus effect-based wind turbine. The proposed approach, presented as an algorithm, allows us to determine input data for the preferred structural optimization software and achieve lightweight, low-inertia parts. control simulation has shown that optimized parts ensure a high enough factor of safety while also significantly reducing the moment of inertia.
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