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ICINCO 2008 - Proceedings of the 5th international conference on Informatics in Control, automation and robotics 2008年 2 RA卷 XIII-XIV页

作者： Filipe, Joaquim Andrade-Cetto, Juan Ferner, Jean-Louis Polytechnic Institute of Setúbal INSTICC Portugal Institut de Robotica i Informatica Industrial CSIC-UPC Spain LISA-ISTIA Université d'Angers France

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ISBN: (纸本)9789898111319

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Modeling and Performance Analysis of a Model Predictive and Feedback Linearization Controlled UAV Autonomous Quadcopter Drone in 3D Dynamics

Modeling and Performance Analysis of a Model Predictive and ...

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5th international conference on Recent Developments in Control, automation and Power Engineering, RDCAPE 2023

作者： Nuhel, Ahsan Kabir Sheham, Md. Nursari Hasan Mohibullah Sazid, Mir Babu, Riad Mollik Arif Shah, Hossain Md. Saha, Bishal Bhatia, Diva American International University-Bangladesh Dhaka Bangladesh University of Asia Pacific Dhaka Bangladesh Vellore Institute of Technology Vellore India

ISBN: (纸本)9789819600465

Autonomous drones are one of the key technological advances of the modern era and have a high impact on delivering different items and military usage. thanks to its advanced technology, which has made the system faster and safer in all its uses, in this paper, a fully autonomous drone is built using model predictive control (MPC) and feedback linearization controllers. the quadcopter is modeled for its better performance and mobility. the unmanned aerial vehicle (UAV) uses performance control inputs, and the authors also presented preliminary flight experiments in which the UAV was limited to vertical and yaw movements. the performance is tested in a vacuum as well as against the drag force. For a better understanding of the output of the systems, different user-defined trajectories are built to analyze the performance attributes. In order to evaluate the characteristics and capacities of the overall system, the aforementioned systems underwent carefully planned planning, design, and implementation processes. the accompanying visual aids illustrate various scenarios in which a drone successfully monitors and maintains its set reference trajectory. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Drones

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AGC of Two-Area Non-reheat Power System Using GWO Technique

AGC of Two-Area Non-reheat Power System Using GWO Technique

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5th international conference on Recent Developments in Control, automation and Power Engineering, RDCAPE 2023

作者： Mohammed, Ahmed Nura Chatterjee, Shamik Bello, Mohammad Department of EEE HAFEDPOLY Jigawa State Kazaure Nigeria SEEE LPU Punjab Phagwara India

ISBN: (纸本)9789819600885

the stringent reliability requirement and rapid technological advancement that are arising in power system as essential services are set out to be more digital and automated, making automatic generation control (AGC) study essential for innovation of new ideas of handling emergent AGC problems. the novel gray wolf optimization (GWO) algorithm is applied here as an optimization apparatus for AGC of two-area power systems because of its high adeptness in unraveling complex engineering problems and its ability to converge swiftly toward the optimum. the study utilized area control error control strategy and employed PID controller as secondary controller because of its well-known attributes;the gains of the controller were systematically adjusted with novel GWO technique based on multi-objective approach. the dynamic performance obtained with the GWO was collated with other intelligent optimization algorithms which are said to be coherent and revealed high performance in the survey carried out on AGC, currently, as specified in the literature. the outcomes procured evidently specify the superior performance of the GWO algorithm in contrast to these algorithms. However, robustness analysis carried out to the system illustrates the high flexibility of the proposed method to parameter uncertainty. Hence, the study conclude that the algorithm can be applied as a powerful tool for optimizing AGC of power system. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： three term control systems

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An introduction - 5th international conference on robotics and automation in industry (ICRAI 2023)

2023 International Conference on Robotics and Automation in ...

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2023 international conference on robotics and automation in Industry, ICRAI 2023 2023年

作者： Khan, Muhammad Tahir

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A Real-Time High-Precision Visual-Inertial SLAM Algorithm in Dynamic Environments

A Real-Time High-Precision Visual-Inertial SLAM Algorithm in...

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Advances in Electrical, Electronics and Computing Technology (EECT), international conference on

作者： Jingyao Xing Bo Wang Zhaojie Yin Automation Department Beijing Institute of Technology Beijing China NO.208 Research Institute of China Ordnance Industries Beijing China

ISBN: (数字)9798331541545

ISBN: (纸本)9798331541552

Visual simultaneous localization and mapping technology has achieved promising results in static scenes, yet it still encounters significant challenges in dynamic scenes. A prevalent approach in dynamic SLAM is to detect potential dynamic objects in the environment and eliminate dynamic features using deep learning. Unfortunately, deep learning algorithms relying on pixel-level object segmentation pose challenges for real-time operation. While methods combining deep-learning based object detection with multi-view geometry can achieve real-time performance, those based on multi-view geometry often presuppose precise camera poses. Given the difficulty in balancing accuracy and real-time performance of existing methods in dynamic environments, this paper introduces a high-precision and real-time visual-inertial SLAM algorithm. Our proposed algorithm employs multiple threads running in parallel, including object detection and dynamic object classification, feature tracking, local state optimization. Within the object detection and dynamic object classification thread, detected objects are categorized based on their relationship with prior dynamic objects, and further segmentation of dynamic objects within this class is performed using depth information. We propose an optical flow tracking method to robustly track dynamic objects, addressing situations of missed detections. Experiments conducted on the TUM RGB-D public dataset demonstrate that, with GPU acceleration, our proposed algorithm achieves a balance between accuracy and real-time performance in dynamic environments.

关键词： Deep learning Simultaneous localization and mapping Accuracy Heuristic algorithms Dynamics Object detection Feature extraction Real-time systems Classification algorithms Optical flow

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Self Attention-based Sparse Graph Convolutional Neural Network for Instant Parameter Prediction to Optimize Wireless Sensor Network Performance in Electrical systems

Self Attention-based Sparse Graph Convolutional Neural Netwo...

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Trends in Material Science and Inventive Materials (ICTMIM), international conference on

作者： Asma Anjum thilak Raj Balasubramanian M. Lakshminarayana Daxa Vekariya Madhu B K Ramya Maranan Department of Computer Science and Engineering HKBK College of Engineering Bengaluru Karnataka Personalization Data Infrastructure/ ML Infrastructure Walmart Inc San Jose California USA Department of Medical Electronics Engineering M S Ramaiah Institute Of Technology Bengaluru Karnataka Department of Computer Science & Engineering Parul Institute of Engineering and Technology Parul University Department of Computer Science and Engineering Vidya Vikas Institute of Engineering and Technology Mysore Karnataka Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

WSNs function as key components across environmental monitoring systems as well as healthcare facilities and industrial automation networks. WSN performance optimization faces ongoing challenges because the networks must deal with restricted energy supplies in addition to shifting operational environments and restricted data transfer capabilities. the current optimization techniques implement static hierarchical approaches combined with heuristic methods yet these prove unable to adjust in real-time thus leading to deteriorating network conditions and resource inefficiency. the research puts forward Self Attention-based Sparse Graph Convolutional Neural Network (SA-SGCN) as an instant parameter prediction framework to optimize WSN performance through automatic adjustments of critical metrics including energy usage and packet success ratios as well as latency and data transfer speed. Real-time prediction becomes possible through the self-attention mechanisms used in SA- SGCN models to detect distant dependencies in the sensor network in combination with sparse graph convolution that decreases computational requirements. the Crested Porcupine Optimizer (CPO) optimizes the hyperparameters of the SA- SGCN model for better efficiency and accuracy measurement. the experimental evidence shows that using the proposed method results in superior performance compared to current approaches because it reaches a prediction accuracy level of 99.9% for WSN configurations. the research presents a dependable energy-efficient adaptive framework for WSNs which enables high scalability and reliable operation within fast-changing environments.

关键词： Wireless sensor networks Adaptation models Accuracy Convolution Computational modeling Predictive models Data transfer Real-time systems Convolutional neural networks Optimization

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An introduction - 5th international conference on robotics and automation in industry (ICRAI 2023)

An introduction - 5th international conference on robotics a...

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international conference on robotics and Artificial Intelligence (ICRAI)

Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.

ISBN: (数字)9781665464727

ISBN: (纸本)9781665464734

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Electric Vehicle Charging Demand Prediction using Multiresolution Sinusoidal Neural Network Optimized with Addax Optimization

Electric Vehicle Charging Demand Prediction using Multiresol...

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Trends in Material Science and Inventive Materials (ICTMIM), international conference on

作者： Rajesh Munirathnam Rohit P. Jadhav Nilesh M. Mahajan Amit Barve M. E. Shashi Kumar Natrayan L Data & Analytics Division TechnoGen Inc Hillsborough New Jersey USA Department of Robotics and Automation D.Y. Patil College of Engineering Akurdi Pune Maharashtra Department of Computer Science and Engineering Parul Institute of Technology Faculty of Engineering and Technology Parul University Limda Gujarat Department of Mechanical Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru Karnataka Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Electric Vehicle (EV) charging demand prediction, while essential for optimizing charging infrastructure and energy management, faces challenges such as data inaccuracies and uncertainties in user behavior patterns. these issues define inaccurate demand forecasts which cause the wrong placement of charging stations and distribution of energy. Also, as the pattern of the EV charging is not static may change due to many factors such as climate, time of day, price preference among others, the prediction models may not handle the dynamism and hence lower the reliability of the forecast results. To overcome these drawbacks, this manuscript proposes an efficient approach for EV charging demand prediction. the data is gathered from a dataset on EV charging. the data is then sent to pre-processing. Using the Maximum Correntropy Quaternion Kalman Filter (MCQKF), the pre-processing section eliminates missing values and normalizes the input. To forecast EV charging demand, the Multiresolution Sinusoidal Neural Network (MSNN) receives the results of the pre-processing data. MSNN’s weight parameter is optimized using Addax Optimization (AO). the proposed MSNN-AO is utilized within the MATLAB platform. the proposed MSNN-AO technique is compared with the existing techniques such as Long Short-Term Memory Neural Network (LSTMNN), Heterogeneous Spatial-Temporal Graph Convolutional Network (HSTGNN) and Artificial Neural Networks (ANN), respectively. the MSNN-AO method achieves an accuracy of 97%, precision of 95%, and a Root Mean Square Error (RMSE) of 2.2%, demonstrating its superior performance in predicting EV charging demand. this highlights the proposed method’s effectiveness in minimizing prediction errors, reducing RMSE by 22.36%, and improving precision by 14.89% compared to existing methods. the MSNN-AO method’s higher accuracy and precision, coupled with its robust performance, make it a reliable and efficient solution for EV charging demand forecasting.

关键词： Accuracy Uncertainty Quaternions Energy resolution Electric vehicle charging Reliability Kalman filters Spatial resolution Root mean square Optimization

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ANN Based Control Strategy for Automatic Generation Control of an Interconnected Power systems

ANN Based Control Strategy for Automatic Generation Control ...

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5th international conference on Recent Developments in Control, automation and Power Engineering, RDCAPE 2023

作者： Singh, Omveer Chaudhary, Shreya Department of Electrical Engineering Gautam Buddha University Greater Noida201312 India

ISBN: (纸本)9789819600465

Automatic generation control of a two-area power network in an interconnection system is considered. the most effective controller is determined by comparing the performance of three controllers: Proportional-Integral (PI), Proportional-Integral-Derivative (PID), and Artificial Neural Network (ANN) controllers. For the analysis and evaluation of system frequency and tie-line power deviations, MATLAB/Simulink is utilized. Rise time, settling time, overshoot, undershoot, and slew rate are evaluated and compared for both frequency deviation and tie-line power responses. the comprehensive simulation results, supported by tables and bar graphs, indicate that the ANN controller outperforms the classical controllers in terms of faster response, enhanced damping of system oscillations, reduced settling time, enhanced accuracy, and efficient power transfer. the bar graphs further emphasize the superiority of the ANN controller in terms of frequency deviation and tie-line power response parameters by providing visual representations of the data. this study contributes to the comprehension and advancement of AGC control strategies in interconnected power networks by highlighting the potential of ANN controllers to achieve greater stability and faster, more precise regulation. For further improvement, future research directions include optimizing the ANN controller's hyperparameters and investigating advanced neural network approaches such as ReLU activation functions. the combination of quantitative analysis and visual representations provides a comprehensive evaluation of controller performance in AGC applications, thereby facilitating well-informed power system control decisions. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electric power system control

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A Backstepping Control Strategy for Constrained Tendon Driven Robotic Finger 5

A Backstepping Control Strategy for Constrained Tendon Drive...

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5th international conference on Control, automation and robotics (ICCAR)

作者： Narkhede, Kunal Sanjay Bhise, Aashay Anil Sainul, I. A. Deb, Sankha Indian Inst Technol Dept Mech Engn Kharagpur 721302 W Bengal India

ISBN: (纸本)9781728133263

the task of controlling an underactuated robotic finger with a single tendon and a single actuator is difficult. Methods for controlling the class of underactuated systems are available in the literature. However, this particular system does not fall into the class of underactuated system. this paper presents a design change which introduces kinematic constraints into the system, making the system controllable. Backstepping control strategy is used to control the system. Simulation results are presented for single finger driven by a single actuator.

关键词： constrained robotic systems tendon driven robotic gripper backstepping control stability analysis

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