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Experimental Verification of Control Strategies for Satellite Magnetic-Based Attitude Control System Under a Three-Axis Helmholtz Cage Environment 38

Experimental Verification of Control Strategies for Satellit...

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38th IEEE Region 10 Conference, TENCON 2023

作者： Panyalert, Thanayuth Manuthasna, Shariff Chaisakulsurin, Jormpon Masri, Tanawish Palee, Kritsada Prasit, Pakawat Torteeka, Peerapong Poonpakdee, Pasu Konghuayrob, Poom School of Engineering King Mongkut's Institute of Technology Ladkrabang Department of Robotics and Artificial Intelligence No.1 Chalong-Krung 1-Alley Ladkrabang Bangkok10520 Thailand Thai Space Consortium Project and Center of Observatory Operation and Engineering No.260 Don-Kaew Mae-Rim Chiang-Mai50180 Thailand

ISBN: (纸本)9798350302196

During satellite mission planning and operation, the main function of the satellite's attitude determination and control subsystem (ADCS) is to gather information about the satellite's orientation relative to the inertial reference frame. Additionally, this subsystem generates control actions that produce the required torques for adjusting the satellite's orientation, particularly in the context of the Low-Earth Orbit (LEO) regime. This paper focuses on the satellite three-axis attitude control problem for a de-tumbling mode of spacecraft using only magnetorquers as actuators under the presence of noise and investigates their performance through Hardware-in-the-Loop simulation (HiLs) tests, which consisted of a relative Earth's magnetic field generator along with the SGP-4-based satellite orbital propagator high-level control software. The design, development, and verification of proposed satellite attitude control system (ACS) strategies are presented. In detail, as an example of experimentation, the classical B-dot control algorithm is used for the de-tumbling mode to stabilize and reduce the angular rate, along with the pointing algorithm for orienting the satellite to the desired attitude. Then, a cascade Proportional-Integral-Derivative (PID) is implemented to generate enough torque through the three-axis magnetorquers on the frictionless air-bearing platform to verify the performance of the controller using an onboard computer. Finally, the effectiveness of the co-simulation tested as the primary experiment was confirmed through the integrated simulation process. © 2023 IEEE.

关键词： Hardware-in-the-loop simulation

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An efficient Deep Spatio-Temporal Context Aware decision Network (DST-CAN) for Predictive Manoeuvre Planning

arXiv

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arXiv 2022年

作者： Chowdhury, Jayabrata Sundaram, Suresh Rao, Nishanth Sundararajan, Narasimhan Robert Bosch Centre for Cyber-Physical Systems Artificial Intelligence and Robotics Lab Dept. of Aerospace Engineering Indian Institute of Science Bangalore India The Artificial Intelligence and Robotics Lab Dept. of Aerospace Engineering Indian Institute of Science Bangalore India WIRIN Project Dept. of Aerospace Engineering Indian Institute of Science Bangalore India

The safety and efficiency of an Autonomous Vehicle (AV) manoeuvre planning heavily depend on the future trajectories of surrounding vehicles. If an AV can predict its surrounding vehicles’ future trajectories, it can make safe and efficient manoeuvre decisions. In this paper, we present a Deep Spatio-Temporal Context-Aware decision Network (DST-CAN) for predictive manoeuvre decisions for AVs on highways. DST-CAN has two main components, namely spatio-temporal context-aware map generator and predictive manoeuvre decisions engine. DST-CAN employ a memory neuron network to predict the future trajectories of its surrounding vehicles. Using look-ahead prediction and past actual trajectories, a spatio-temporal context-aware probability occupancy map is generated. These context-aware maps as input to a decision engine generate a safe and efficient manoeuvre decision. Here, CNN helps extract feature space, and two fully connected network generates longitudinal and lateral manoeuvre decisions. Performance evaluation of DST-CAN has been carried out using two publicly available NGSIM US-101 and I-80 highway datasets. A traffic rule is defined to generate ground truths for these datasets in addition to human decisions. Two DST-CAN models are trained using imitation learning with human driving decisions from actual traffic data and rule-based ground truth decisions. The performances of the DST-CAN models are compared with the state-of-the-art Convolutional Social-LSTM (CS-LSTM) models for manoeuvre prediction. The results clearly indicate that the context-aware maps help DST-CAN to predict the decision accurately over CS-LSTM. Further, an ablation study has been carried out to understand the effect of prediction horizons of performance and a robustness study to understand the near collision scenarios over actual traffic observations. The context-aware map with a 3 second prediction horizon is most suitable and robust against near collision. © 2022, CC BY.

关键词： Autonomous vehicles

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Experimental Verification of Control Strategies for Satellite Magnetic-Based Attitude Control System Under a Three-Axis Helmholtz Cage Environment

Experimental Verification of Control Strategies for Satellit...

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IEEE Region 10 International Conference TENCON

作者： Thanayuth Panyalert Shariff Manuthasna Jormpon Chaisakulsurin Tanawish Masri Kritsada Palee Pakawat Prasit Peerapong Torteeka Pasu Poonpakdee Poom Konghuayrob Department of Robotics and Artificial Intelligence School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Thai Space Consortium Project and Center of Observatory Operation and Engineering National Astronomical Research Institute of Thailand (Public Organization) Chiang-Mai Thailand

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Sparse Bayesian Correntropy Learning for Robust Muscle Activity Reconstruction from Noisy Brain Recordings

arXiv

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arXiv 2024年

作者： Li, Yuanhao Chen, Badong Yoshimura, Natsue Koike, Yasuharu Yamashita, Okito Center for Advanced Intelligence Project RIKEN Tokyo103-0027 Japan Brain Information Communication Research Laboratory Group Advanced Telecommunications Research Institute International Kyoto619-0237 Japan Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an710049 China School of Computing Tokyo Institute of Technology Yokohama226-8503 Japan Institute of Innovative Research Tokyo Institute of Technology Yokohama226-8503 Japan

Sparse Bayesian learning has promoted many effective frameworks for brain activity decoding, especially for the reconstruction of muscle activity. However, existing sparse Bayesian learning mainly employs Gaussian distribution as error assumption in the reconstruction task, which is not necessarily the truth in the real-world application. On the other hand, brain recording is known to be highly noisy and contains many non-Gaussian noises, which could lead to significant performance degradation for sparse Bayesian learning method. The goal of this paper is to propose a new robust implementation for sparse Bayesian learning, so that robustness and sparseness can be realized simultaneously. Motivated by the great robustness of maximum correntropy criterion (MCC), we proposed an integration of MCC into the sparse Bayesian learning regime. To be specific, we derived the explicit error assumption inherent in the MCC and then leveraged it for the likelihood function. Meanwhile, we used the automatic relevance determination (ARD) technique for the sparse prior distribution. To fully evaluate the proposed method, a synthetic dataset and a real-world muscle activity reconstruction task with two different brain modalities were employed. Experimental results showed that our proposed sparse Bayesian correntropy learning framework improves significantly the robustness in a noisy regression task. The proposed method can realize higher correlation coefficient and lower root mean squared error in the real-world muscle activity reconstruction tasks. Sparse Bayesian correntropy learning provides a powerful tool for neural decoding which can promote the development of brain-computer interfaces. © 2024, CC BY.

关键词： Brain

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Correntropy-Based Improper Likelihood Model for Robust Electrophysiological Source Imaging

arXiv

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arXiv 2024年

作者： Li, Yuanhao Chen, Badong Hu, Zhongxu Suzuki, Keita Bai, Wenjun Koike, Yasuharu Yamashita, Okito Center for Advanced Intelligence Project RIKEN Tokyo103-0027 Japan Department of Computational Brain Imaging ATR Neural Information Analysis Laboratories Kyoto619-0237 Japan Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an710049 China School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Department of Computational Brain Imaging ATR Neural Information Analysis Laboratories Kyoto619-0237 Japan Institute of Innovative Research Tokyo Institute of Technology Yokohama226-8503 Japan

Bayesian learning provides a unified skeleton to solve the electrophysiological source imaging task. From this perspective, existing source imaging algorithms utilize the Gaussian assumption for the observation noise to build the likelihood function for Bayesian inference. However, the electromagnetic measurements of brain activity are usually affected by miscellaneous artifacts, leading to a potentially non-Gaussian distribution for the observation noise. Hence the conventional Gaussian likelihood model is a suboptimal choice for the real-world source imaging task. In this study, we aim to solve this problem by proposing a new likelihood model which is robust with respect to non-Gaussian noises. Motivated by the robust maximum correntropy criterion, we propose a new improper distribution model concerning the noise assumption. This new noise distribution is leveraged to structure a robust likelihood function and integrated with hierarchical prior distributions to estimate source activities by variational inference. In particular, the score matching is adopted to determine the hyperparameters for the improper likelihood model. A comprehensive performance evaluation is performed to compare the proposed noise assumption to the conventional Gaussian model. Simulation results show that, the proposed method can realize more precise source reconstruction by designing known ground-truth. The real-world dataset also demonstrates the superiority of our new method with the visual perception task. This study provides a new backbone for Bayesian source imaging, which would facilitate its application using real-world noisy brain signal. © 2024, CC BY.

关键词： Gaussian noise (electronic)

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A study on a swirl type multi-hole fuel injector for a PFI system to improve air-fuel mixture formation

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IOP Conference Series: Materials Science and Engineering 2017年第1期243卷

作者： A J Alimin Y Kuang He A Sapit H Selamat Head Automotive Powertrain Research Group (ARG) Faculty of Mechanical & Manufacturing Engineering UniversitiTun Hussein Onn Malaysia 86400 Parit Raja BatuPahat Johor Malaysia Bachelor Degree Project Student Faculty of Mechanical & Manufacturing Engineering UniversitiTun Hussein Onn Malaysia 86400 Parit Raja BatuPahat Johor Malaysia Principal Researcher Flow Analysis Simulation and Turbulence Research Group (FAST) Faculty of Mechanical & Manufacturing Engineering UniversitiTun Hussein Onn Malaysia 86400 Parit Raja BatuPahat Johor Malaysia Director Centre for Artificial Intelligence and Robotics (CAiRO) UniversitiTeknologi Malaysia 81310 Skudai Johor Malaysia

This paper described the development and simulation works thatwere conducted on an injector that has a combined swirl spray and multi-hole injection functions. It was expected that those two functions would provide beneficial effects to the fuel injector, in order to provide better fuel and air mixing, especially for Port Fuel Injection (PFI) application. The combination of both functions was aimed for achieving high quality spray formation, which might be useful in cases when the combustion time is limited. Swirl spray pattern is known for fine droplets production with relatively smaller SMD and bigger coverage area in the combustion chamber. On the other hand, a multi-hole injector has the advantages of simplicity in manufacturing and operation. Preliminary results on the simulation works that were done separately, demonstrated the ability of the injector's design to produce swirl spray as well as injecting near-symmetrical sprays from the four holes at the injector's end. Nevertheless, these findings must be improved further in the future, especially on simulating the benefits of having a combined swirl and multi-hole functions inside a single injector. Furthermore, detailed assessment on manufacturability and cost effectiveness of this injector must also be addressed.

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Multiagent system based scientific discovery within information society

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Mind & Society 2002年第1期3卷 111-127页

作者： Amigoni, Francesco Schiaffonati, Viola Somalvico, Marco Politecnico di Milano-Artificial Intelligence and Robotics Project Dipartimento di Elettronica e Informazione Politecnico di Milano Milano Italy Politecnico di Milano-Artificial Intelligence and Robotics Project Milano Italy

In this paper we investigate the role of information machines in the scientific enterprise intended as a social activity. Our discussion is based on a powerful kind of information machines called scientific social agencies, which are multiagent systems of distributed artificial intelligence. Scientific social agency, on the one hand, can provide great benefits to the present common scientific practice but, on the other hand, its development represents a strong and still open technical challenge. This paper shows a coherent framework in which scientific social agency is settled. Moreover, the double role of a scientific social agency in assisting scientists in their activity and in representing the products of their research is illustrated by means of some examples.

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Anchoring: do we need new solutions to an old problem or do we have old solutions for a new problem?

Anchoring: do we need new solutions to an old problem or do ...

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2001 AAAI Fall Symposium

作者： Bonarini, Andrea Matteucci, Matteo Restelli, Marcello Politecnico di Milano Artificial Intelligence and Robotics Project Department of Electronics and Information Politecnico di Milano Milan Italy

We propose a model to represent knowledge in an agent possibly participating to a multi-agent system, such that the anchoring problem can be faced by well known techniques. We discuss how this model could reliably support the instantiation of concepts by aggregating percepts affected by uncertainty, sensed by several sensors, and obtained from different agents. Copyright © 2001, American Association for artificial intelligence (***). All rights reserved.

关键词： Multi agent systems

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Concepts for anchoring in robotics 7th

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7th Congress of the Italian Association for artificial intelligence, AIIA 2001

ISBN: (纸本)3540426019

Anchoring is the activity an agent does to classify the objects it perceives and to track them. With the increasing abilities of mobile robots, anchoring is becoming a major research issue to make robots perform their task in unstructured and partially unknown environments. We propose a model to represent knowledge in an agent, possibly participating to a multi–agent system, showing that the anchoring problem can be successfully faced by well known AI techniques. We discuss how this model can reliably support the instantiation of concepts by aggregating percepts affected by uncertainty, sensed by several sensors, and obtained from different agents. © Springer-Verlag Berlin Heidelberg 2001.

关键词： artificial intelligence

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A multilevel architecture of creative dynamic agency

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Foundations of Science 2000年第2期5卷 157-184页

作者： Amigoni, Francesco Schiaffonati, Viola Somalvico, Marco Politecnico di Milano Artificial Intelligence and Robotics Project Dipartimento di Elettronica e Informazione Politecnico di Milano Milano (MI) I-20133 Piazza Leonardo da Vinci 32 Italy Politecnico di Milano Artificial Intelligence and Robotics Project Dipartimento di Elettronica e Informazione Politecnico di Milano Milano (MI) I-20133 Piazza Leonardo da Vinci 32 Italy Politecnico di Milano Artificial Intelligence and Robotics Project Dipartimento di Elettronica e Informazione Politecnico di Milano Milano (MI) I-20133 Piazza Leonardo da Vinci 32 Italy

There are two classical and opposite positions about scientific discovery: the one that conceives scientific discovery activity as fully rational and the one that conceives scientific discovery activity as fully irrational. In the first case, machines are regarded as able to perform the scientific discovery process whereas, in the second case, machines are considered unable to perform any part of the scientific discovery process. We adopt a third intermediate approach that envisages a new role for machines, which are conceived as descriptions of the results of scientific discovery activity. More precisely, the purpose of the paper is to illustrate the multilevel structure of a machine, called creative dynamic agency, that represents the articulated and incremental description of the product of scientific discovery process. The multilevel architecture reflects the composition relation that holds among phenomena described by creative agents that compose creative dynamic agency. © 2001 Kluwer Academic Publishers.

关键词： Creative agent Creative dynamic agency Field of phenomena Mosaic of models Multilevel insertion of models

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