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11th IEEE International Conference on Intelligent Systems, IS 2022

作者： Smitiukh, Yaroslav Samoilenko, Yuliia Kostiuk, Yuliia Kryvoruchko, Olena Stepashkina, Kateryna National University of Food Technologies Department of Automation and Computer Technologies of Control Systems Kyiv Ukraine State University of Trade and Economics Department of Software Engineering and Cyber Security Kyiv Ukraine

ISBN: (纸本)9781665456562

The technological process of the churning process in continuous butter manufacture were considered. The qualitative indicator of the water content of butter was modeled on the basis of a set of industrial data using an artificial neural network (ANN). The prototype of an intelligent system for predicting of the water content of butter allows to increase the information support of the operator-technologist. The proposed model predicts the water content of butter with an error of less than 2%. The input variables of the forecast model prototype are cream fat content, cream ripening temperature, cream supply temperature, frequency of revolutions of the stirrer of the whipping device, and consumption of the normalizing component. Further research is aimed at the development of a decision support information system for dairy manufacture and should be integrated into the subsystem of automated control of the technological process to ensure proper functioning in real time. © 2022 IEEE.

关键词： Intelligent systems

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Skeleton-based Action Recognition Using Two-stream Graph Convolutional Network with Pose Refinement 41

Skeleton-based Action Recognition Using Two-stream Graph Con...

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第41届中国控制会议

作者： Biao Zheng Luefeng Chen Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Department of Electrical and Computer Engineering University of Alberta Tokyo Institute of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

With the development of science and technology,graph convolutional network has made great progress in improving the accuracy of action ***,there still exists some deficiencies in current ***,the human skeleton point coordinates entering into the network are barely refined,which may cause large ***,the second-order information(the length and direction of bones),which can reflect action characteristics discriminatively,is rarely *** solve the above issues,a two stream graph convolutional network with pose refinement for skeleton based action recognition is ***,we use an adaptive block to to help improve the *** test our method on Kinetics dataset and the experiment show it can get better results than some recent methods,which plays a positive role in future research.

关键词： Graph convolutional network Pose refinement Skeleton based action recognition Adaptive block

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Online Trajectory Replanner for Dynamically Grasping Irregular Objects

arXiv

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arXiv 2025年

作者： Vu, Minh Nhat Grander, Florian Nguyen, Anh TU Wien Vienna1040 Austria Automation and Control Center Egger GmbH Austria Department of Computer Science University of Liverpool United Kingdom

This paper presents a new trajectory replanner for grasping irregular objects. Unlike conventional grasping tasks where the object’s geometry is assumed simple, we aim to achieve a "dynamic grasp" of the irregular objects, which requires continuous adjustment during the grasping process. To effectively handle irregular objects, we propose a trajectory optimization framework that comprises two phases. Firstly, in a specified time limit of 10 s, initial offline trajectories are computed for a seamless motion from an initial configuration of the robot to grasp the object and deliver it to a pre-defined target location. Secondly, fast online trajectory optimization is implemented to update robot trajectories in real-time within 100 ms. This helps to mitigate pose estimation errors from the vision system. To account for model inaccuracies, disturbances, and other non-modeled effects, trajectory tracking controllers for both the robot and the gripper are implemented to execute the optimal trajectories from the proposed framework. The intensive experimental results effectively demonstrate the performance of our trajectory planning framework in both simulation and real-world scenarios. © 2025, CC BY.

关键词： Trajectories

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A Hardware-Efficient Vector Limiter Algorithm and Its Programmable Logic Realization

A Hardware-Efficient Vector Limiter Algorithm and Its Progra...

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China International Electrical and Energy Conference (CIEEC)

作者： Zhao Song Yiyan Wang Tianyi Xiong Computer and Electronics System and Engineering Department AVIC Xi'an Flight Automation Control Research Institute (FACRI) Xi'an Shanxi Province China

ISBN: (数字)9798350359558

ISBN: (纸本)9798350359565

Vector limiter is a computationally complex component of vector control, but its hardware-friendly algorithm is rarely discussed. This paper presents an efficient solution with pure shift-and-add operations, namely the dual vector rotation algorithm (DVRA). Its programmable logic realization is also provided. The DVRA is then simulated in a motor driving system, and physical validation is made on a Xilinx FPGA. Furthermore, performance comparison is conducted between the DVRA and four control groups (conventional algorithms). All control groups are built by Xilinx IPs under diversified configurations, thus human bias can be minimized. Post-implementation results evince that the DVRA exhibits significantly less resource expenditure and better timing, with power consumption and accuracy comparable to the control groups.

关键词： Power demand Accuracy Motors Vectors Hardware Timing System-on-chip

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Safe Reinforcement Learning for Constrained Markov Decision Processes with Stochastic Stopping Time

arXiv

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arXiv 2024年

作者： Mazumdar, Abhijit Wisniewski, Rafal Bujorianu, Manuela L. Section of Automation & Control Aalborg University Aalborg East9220 Denmark The Department of Computer Science University College London United Kingdom

In this paper, we present an online reinforcement learning algorithm for constrained Markov decision processes with a safety constraint. Despite the necessary attention of the scientific community, considering stochastic stopping time, the problem of learning optimal policy without violating safety constraints during the learning phase is yet to be addressed. To this end, we propose an algorithm based on linear programming that does not require a process model. We show that the learned policy is safe with high confidence. We also propose a method to compute a safe baseline policy, which is central in developing algorithms that do not violate the safety constraints. Finally, we provide simulation results to show the efficacy of the proposed algorithm. Further, we demonstrate that efficient exploration can be achieved by defining a subset of the state-space called proxy set. © 2024, CC BY-NC-ND.

关键词： Reinforcement learning

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Optimizing Steering Angle Predictive Convolutional Neural Network for Autonomous Car

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computers, Materials & Continua 2022年第5期71卷 2285-2302页

作者： Hajira Saleem Faisal Riaz Asadullah Shaikh Khairan Rajab Adel Rajab Muhammad Akram Mana Saleh Al Reshan Control Automotive and Robotics Lab Affiliated lab of National Center of Robotics and Automation(NCRA HEC Pakistan)and with the Department of Computer Science and Information TechnologyMirpur University of Science and Technology(MUST)Mirpur Azad Kashmir10250Pakistan College of Computer Science and Information System Najran UniversityNajran61441Saudi Arabia College of Computer Science and Engineering University of South FloridaTampa33620United States

Deep learning techniques,particularly convolutional neural networks(CNNs),have exhibited remarkable performance in solving visionrelated problems,especially in unpredictable,dynamic,and challenging *** autonomous vehicles,imitation-learning-based steering angle prediction is viable due to the visual imagery comprehension of *** this regard,globally,researchers are currently focusing on the architectural design and optimization of the hyperparameters of CNNs to achieve the best *** has proven the superiority of metaheuristic algorithms over the manual-tuning of ***,to the best of our knowledge,these techniques are yet to be applied to address the problem of imitationlearning-based steering angle ***,in this study,we examine the application of the bat algorithm and particle swarm optimization algorithm for the optimization of the CNN model and its hyperparameters,which are employed to solve the steering angle prediction *** validate the performance of each hyperparameters’set and architectural parameters’set,we utilized the Udacity steering angle dataset and obtained the best results at the following hyperparameter set:optimizer,Adagrad;learning rate,0.0052;and nonlinear activation function,exponential linear *** per our findings,we determined that the deep learning models show better results but require more training epochs and time as compared to shallower *** show the superiority of our approach in optimizing CNNs through metaheuristic algorithms as compared with the manual-tuning *** testing was also performed using the model trained with the optimal architecture,which we developed using our approach.

关键词： Bat algorithm convolutional neural network hyperparameters metaheuristic optimization algorithm steering angle prediction

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Model predictive control for tracking using artificial references: Fundamentals, recent results and practical implementation 63

Model predictive control for tracking using artificial refer...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Krupa, P. Kohler, J. Ferramosca, A. Alvarado, I. Zeilinger, M.N. Alamo, T. Limon, D. Computer Science Group L'Aquila Italy Institute for Dynamic Systems and Control Eth Zürich ZürichCH-8092 Switzerland University of Bergamo Information and Production Engineering Department of Management Via Marconi 5 BG Dalmine24044 Italy Universidad de Sevilla Department of Systems Engineering and Automation Seville Spain

ISBN: (纸本)9798350316339

This paper provides a comprehensive tutorial on a family of Model Predictive control (MPC) formulations, known as MPC for tracking, which are characterized by including an artificial reference as part of the decision variables in the optimization problem. These formulations have several benefits with respect to the classical MPC formulations, including guaranteed recursive feasibility under online reference changes, as well as asymptotic stability and an increased domain of attraction. This tutorial paper introduces the concept of using an artificial reference in MPC, presenting the benefits and theoretical guarantees obtained by its use. We then provide a survey of the main advances and extensions of the original linear MPC for tracking, including its non-linear extension. Additionally, we discuss its application to learning-based MPC, and discuss optimization aspects related to its implementation. © 2024 IEEE.

关键词： Predictive control systems

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Design of a Multi-Storey Model of an Automated Garage House Based on ATmega2560 Microcontroller 5

Design of a Multi-Storey Model of an Automated Garage House ...

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5th IEEE International Conference on Modern Electrical and Energy System, MEES 2023

作者： Jacko, Patrik Kalinov, Andrii Burdilna, Evgeniia Babjak, Jaroslav Technical University of Košice Department of Theoretical and Industrial Electrical Ingeneering Košice Slovakia Kremenchuk Mykhailo Ostrohradskyi National University Department of Systems of Automatic Control and Electric Drive Kremenchuk Ukraine Kremenchuk Mykhailo Ostrohradskyi National University Department of Automation and Computer Systems Kremenchuk Ukraine

ISBN: (纸本)9798350359787

This article describes in detail the implementation of an automated parking house model based on ATmega2560 microcontroller that enables simple and efficient parking of cars in precisely determined positions. Described model offers to user convenient parking of the car model. In addition to the automated parking system, the system provides several additional functions realized by displacement sensors and smoke sensors. One of the goals of this paper is to eliminate potentially dangerous situations and collisions during parking and thereby increase safety in this area. The garage house model is not designed solely for parking purposes but also as a practical teaching tool in the field of sensors and actuators. It is precisely in this area that students can expand their skills in designing and implementing programs that can enable all the functions of the garage house - temperature and smoke detection, precise positioning of the platform through stepper motors, display outputs, and more. At the same time, they will learn teamwork, where the functionality of the garage house is divided into multiple parts, and individual teams of students must collaborate to successfully solve and create a fully functional garage house software. © 2023 IEEE.

关键词： Microcontrollers

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Improvement of the Predictive control Method for the High-Level controller 17

Improvement of the Predictive Control Method for the High-Le...

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17th IEEE International Conference on Advanced Trends in Radioelectronics, Telecommunications and computer Engineering, TCSET 2024

作者： Budashko, Vitalii Sandler, Albert Glazeva, Oksana Educational and Scientific Institute of Automation and Electrical Engineering National University 'Odessa Maritime Academy' NUOMA Odesa Ukraine National University 'Odessa Maritime Academy' NUOMA Department of the theory of automatic control and computer technology Odesa Ukraine National University 'Odessa Maritime Academy' NUOMA Department of electrical engineering and electronics Odesa Ukraine

ISBN: (纸本)9798331520564

automation of ship maneuvering in limited sailing conditions usually requires 100% redundancy of thrusters (THRs) of various modifications and their locations in accordance with the matrix. The hierarchy of the motion control system is branched by levels using high-level motion controllers and THRs control distribution algorithms, which allows for a modular design with software, where the high-level controller (HLC) can be designed without comprehensive information about the thruster motors, and the input signal branching and speed limits are handled by the motion controller. But, for certain THRs configurations, this branching leads to the decrease in control efficiency due to the limitation of data on the physical characteristics of the ships and the operation of the motion controller. This research examines different approaches to improving control efficiency using methods of nonlinear predictive control models as a basis for developing motion controllers with decision optimization based on relevant constraints. The implemented branched system is the result of solving two simple problems of ship's motion, which highlight the related problems. The use of different approaches to eliminate the identified shortcomings made it possible to develop the nonlinear controller that combines the motion controller with distributed control of the THRs and ensuring the certain level of system reliability. The modularity of the control system is provided by the expansion of the closed system, which made it possible to achieve an increase in efficiency during the combined mode of operation. The obtained solution of nonlinear control with time-varying constraints made it possible to increase the accuracy of the control with a decrease in the duration of the pause in response to the change of the disturbance within 10% © 2024 IEEE.

关键词： Predictive control systems

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TreeSwap: Data Augmentation for Machine Translation via Dependency Subtree Swapping

arXiv

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arXiv 2023年

作者： Nagy, Attila Lakatos, Dorina Barta, Botond Ács, Judit Department of Automation and Applied Informatics Budapest University of Technology and Economics Hungary Institute for Computer Science and Control Hungary

Data augmentation methods for neural machine translation are particularly useful when limited amount of training data is available, which is often the case when dealing with low-resource languages. We introduce a novel augmentation method, which generates new sentences by swapping objects and subjects across bisentences. This is performed simultaneously based on the dependency parse trees of the source and target sentences. We name this method TreeSwap. Our results show that TreeSwap achieves consistent improvements over baseline models in 4 language pairs in both directions on resource-constrained datasets. We also explore domain-specific corpora, but find that our method does not make significant improvements on law, medical and IT data. We report the scores of similar augmentation methods and find that TreeSwap performs comparably. We also analyze the generated sentences qualitatively and find that the augmentation produces a correct translation in most cases. Our code is available on Github. © 2023, CC BY.

关键词： Neural machine translation

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