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Position and Angular control using Fuzzy Tuned PID controller for Mobile Robot Path Tracking 5

Position and Angular control using Fuzzy Tuned PID Controlle...

引用

5th ieee International symposium in Robotics and Manufacturing automation, ROMA 2022

作者： Razali, Muhammad Razmi Faudzi, Ahmad Athif Mohd Shamsudin, Abu Ubaidah Universiti Teknologi Malaysia Faculty of Electrical Engineering Johor Bahru Malaysia Universiti Teknologi Malaysia Centre for Artificial Intelligence and Robotics Kuala Lumpur Malaysia Universiti Tun Hussein Onn Malaysia Fakulti Kejuruteraan Elektrik Dan Elektronik Batu Pahat Malaysia

ISBN: (数字)9781665459327

ISBN: (纸本)9781665459327

Analyzing unstructured gain data has been a growing interest among researchers, resulting in valuable information in many fields such as path planning, and others. Furthermore, the Fuzzy Logic controller (FLC) has simplified the managing unstructured gain data on the simulator such as Gazebo. FLC and PID controller can be used as a solution to recognize path planning entities in the collision-free environment based on heuristic solution. However, without proper selection of unstructured gain data of PID such as proportional, derivative and integral, the performance maybe compromised. In this paper, PID and FLC is designed in series for position and angular control to overcome the multi-representation and the problem of uncertainty contexts. It will be able to recognize the Fuzzy Tuned PID controller gains with limited supervised data accurately and take fast and precise intervention. The efficient and precise algorithms help the mobile robot to take immediate and appropriate intervention, thus helping to preserve the path planning. Finally, in line with the FLC and PID controller, this study will fulfill two elements in the control Signal Distance (CSD) and control Signal Angle (CSA) to produce precise and optimal mobile robot path tracking. © 2022 ieee.

关键词： Mobile robots

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Cooperative Optimization Strategy for Distributed Energy Resource System using Multi-Agent Reinforcement Learning

Cooperative Optimization Strategy for Distributed Energy Res...

引用

ieee symposium Series on computational intelligence (ieee SSCI)

作者： Liu, Zhaoyang Xiang, Tianchun Wang, Tianhao Mu, Chaoxu Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China State Grid Tianjin Elect Power Co Internet Dept Tianjin Peoples R China State Grid Tianjin Elect Power Co Elect Power Res Inst Tianjin Peoples R China

ISBN: (纸本)9781728190488

In this paper, a consensus multi-agent deep reinforcement learning algorithm is introduced for distributed cooperative secondary voltage control of microgrids. To reduce dependence on the system model and enhance communication efficiency, we propose a fully decentralized multi-agent advantage actor critic (A2C) algorithm with local communication networks, which considers each distributed energy resource (DER) as an agent. Both local state and the messages received from neighbors are employed by each agent to learn a control strategy. Moreover, the maximum entropy reinforcement learning framework is applied to improve exploration of agents. The proposed algorithm is verified in two different scale microgrid setups, which are microgrid-6 and microgrid-20. Experiment results show the effectiveness and superiority of our proposed algorithm.

关键词： Distributed energy resource (DER) microgrids multi-agent deep reinforcement learning secondary voltage control

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control-Oriented Model for Energy-Efficient Electric Vehicle

Control-Oriented Model for Energy-Efficient Electric Vehicle

引用

International symposium on Applied Machine intelligence and Informatics (SAMI)

作者： Zoltán Pusztai Tamás Luspay Ferenc Friedler Vehicle Industry Research Center Széchenyi István University Győr Hungary Systems and Control Laboratory - HUN-REN Computer and Automation Research Institute Budapest Hungary

ISBN: (数字)9798350379365

ISBN: (纸本)9798350379372

In this paper, a control-oriented Linear Parameter Varying (LPV) model of an energy efficient electric vehicle is proposed, designed for model-based control to minimize energy consumption. The control inputs of the model include the torque reference and the actual cornering radius. The LPV model assesses the impact of cornering on driving resistances and, consequently, on energy consumption, which represents a novel approach. Due to the driving characteristics and the model nonlinear dynamics of the vehicle, a velocity-linearization based method was applied to obtain the parameter-dependent form. The obtained LPV model was then validated by using logged driving data, showing a root mean square error (RMSE) of 0.4682 m/s compared to the measured speed profile, thereby confirming the model's accuracy. The proposed LPV model can be utilized to develop energy-efficient driving strategies, making it highly relevant for the design and operation of energy-efficient vehicles.

关键词： Energy consumption Torque Measurement uncertainty Electric vehicles Energy efficiency Data models Nonlinear dynamical systems Root mean square Informatics Machine intelligence

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Safe and Efficient Model-free Adaptive control via Bayesian Optimization

Safe and Efficient Model-free Adaptive Control via Bayesian ...

引用

ieee International Conference on Robotics and automation (ICRA)

作者： Konig, Christopher Turchetta, Matteo Lygeros, John Rupenyan, Alisa Krause, Andreas Inspire AG Zurich Switzerland Swiss Fed Inst Technol Learning & Adapt Syst Grp Zurich Switzerland Swiss Fed Inst Technol Automat Control Lab Zurich Switzerland

ISBN: (纸本)9781728190778

Adaptive control approaches yield high-performance controllers when a precise system model or suitable parametrizations of the controller are available. Existing data-driven approaches for adaptive control mostly augment standard model-based methods with additional information about uncertainties in the dynamics or about disturbances. In this work, we propose a purely data-driven, model-free approach for adaptive control. Tuning low-level controllers based solely on system data raises concerns on the underlying algorithm safety and computational performance. Thus, our approach builds on GoOSE, an algorithm for safe and sample-efficient Bayesian optimization. We introduce several computational and algorithmic modifications in GoOSE that enable its practical use on a rotational motion system. We numerically demonstrate for several types of disturbances that our approach is sample efficient, outperforms constrained Bayesian optimization in terms of safety, and achieves the performance optima computed by grid evaluation. We further demonstrate the proposed adaptive control approach experimentally on a rotational motion system.

关键词： Adaptation models Uncertainty computational modeling Heuristic algorithms Bayes methods Safety Adaptive control

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Learning-based fast image enhancement for driving scenes 6

Learning-based fast image enhancement for driving scenes

引用

6th CAA International Conference on Vehicular control and intelligence, CVCI 2022

作者： Dong, Mingwei Liu, Runwu Kong, Huifang School of Electrical Engineering and Automation Hefei University of Technology Hefei China

ISBN: (数字)9781665453745

ISBN: (纸本)9781665453745

Visual sensors such as cameras can obtain rich image and video information, which is one of the most effective and lowest cost perception sensors for autonomous driving. However, when the environmental conditions change rapidly, the quality of acquired images decreases, resulting in various deep learning algorithms embedded in the camera may be difficult to complete object detection and other tasks. Although some existing image enhancement methods can mitigate the effects of environmental factors, these methods often involve complex calculations that ignore the importance of processing speed in the autonomous driving field. To solve these problems, double-branch image enhancement network is proposed in this paper, which includes low resolution branch and full resolution branch. With low resolution branch, local features of image can be obtained at a small computational cost. With full resolution branch, the details which are lost due to down-sampling are obtained. The enhanced image is obtained by fusing the feature graphs of two branches. The test result on BDD100K shows that our method has higher image processing speed on the premise of ensuring image quality. © 2022 ieee.

关键词： Image enhancement

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Adaptive weighted broad learning system for imbalanced classification

Adaptive weighted broad learning system for imbalanced class...

引用

ieee Advanced Information Management,Communicates,Electronic and automation control Conference (IMCEC)

作者： Yanting Li Yiping Gao Junwei Jin C. L. Philip Chen College of Computer Science and Technology Zhengzhou University of Light Industry Zhengzhou China College of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou China School of Computer Science and Engineering South China University of Technology Guangzhou China

ISBN: (数字)9798350316537

ISBN: (纸本)9798350316544

Broad Learning System (BLS) perform well in classification tasks with good computational efficiency. However, its effectiveness decreases when faced with imbalanced data distribution. The traditional BLS cannot solve this problem effectively. In this paper, we introduce an adaptive weighted method for BLS, aiming to improve its classification performance on imbalanced data. This method can assign different weights according to the distribution characteristics of samples. There exists a closed-form solution to the problem and the transformation matrix can be solved efficiently by pseudo-inverse. Experimental results on several imbalanced datasets demonstrate that the proposed method surpasses standard BLS and many advanced imbalanced classification methods.

关键词： Learning systems Adaptive systems Closed-form solutions automation Information management computational efficiency Classification algorithms

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Development of an Accelerometer-based Data Acquisition System for Hand Gesture Recognition

Development of an Accelerometer-based Data Acquisition Syste...

引用

Robotics, automation, Artificial-intelligence and Internet-of-Things (RAAICON), 2019 ieee International Conference on

作者： Atique Tajwar Md. Ettashamul Haque Md Mehedi Hasan Department of Robotics and Mechatronics Engineering University of Dhaka Dhaka Bangladesh

ISBN: (数字)9798331534400

ISBN: (纸本)9798331534417

This study presents a novel approach to Human-Robot Interaction (HRI) by employing gesture recognition to control robotic systems, such as bionic arms or manipulators, through accelerometer-based data collection. Using a low-cost glove equipped with an ESP32 microcontroller and accelerometer, data was gathered from 22 participants performing five distinct gestures: a clockwise circle, square, triangle, double tap, and the letter S. After filtering, these gestures were translated into features that trained a small neural network model designed for low-storage microcontrollers like the ESP32. With a performance accuracy of 90.4%, the neural network outperformed existing ensemble learning models, demonstrating its suitability for real-time gesture recognition tasks in embedded robotic systems. This highly efficient model enables seamless and intuitive control of robotic devices by facilitating responsive and natural HRI through gesture recognition. Designed specifically for compact and resource-limited environments, the model addresses the challenges associated with limited computational resources and restricted hardware capabilities. It provides a robust and scalable solution that can be easily integrated into various robotics applications, where gesture-based control is advantageous.

关键词： Microcontrollers computational modeling Neural networks Human-robot interaction Low-pass filters Gesture recognition Data collection Manipulators Feature extraction Data models

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A Novel Population Optimizer for Unit Scheduling Problems in Power Systems

A Novel Population Optimizer for Unit Scheduling Problems in...

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ieee symposium Series on computational intelligence (SSCI)

作者： Huashi Zhao Yubin He Shouyu Liang Huafeng Zhou Huijie GU Yingchen Li Qiujia Fu China Southern Power Grid Dispatching and Control Center Guangzhou China

The unit commitment (UC) problem is the first step in power system optimal scheduling and system planning. However, the UC problem is a mixed integer optimization problem, which usually has the characteristics of high dimension, non-convex and nonlinear. Plug-in electric vehicles (PEVs) integration into the grid can help improve stability and flexibility of the grid. However, Large-scale PEVs charging demand may put pressure on the grid and may lead to grid overloads. Recently, a competitive swarm optimizer (CSO) is proposed to settle optimization problems, which is considerably challenging in evolutionary computation. In this paper, a binary competitive swarm optimizer (BCSO) is proposed to tackle UC problems integration with PEVs. Finally, comparison experiments on economic problems with dimensionality increasing from 10 to 100 units, which confirm the competitive performance of the proposed optimizer.

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Bodymodel testing for biomechanical motion studies - Pilot Study

Bodymodel testing for biomechanical motion studies - Pilot S...

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International symposium on computational intelligence and Informatics

作者： A. Lántzky D. Joó Mollia Zrt. Budapest Hungary

ISBN: (纸本)9798350398830

In this study, the measurement accuracy of the Optitrack Mocap system biomechanical test models was tested. For testing, several marker arrays were used: These were the factory installed Baseline, Rizzoli and Helen Hayes. The results obtained were statistically compared. Our aim was to find similarities and differences between the different models.

关键词： Biomechanics Computer science Mechatronics Biological system modeling computational modeling Production facilities Informatics

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Active Inference for Integrated State-Estimation, control, and Learning

Active Inference for Integrated State-Estimation, Control, a...

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ieee International Conference on Robotics and automation (ICRA)

作者： Baioumy, Mohamed Duckworth, Paul Lacerda, Bruno Hawes, Nick Univ Oxford Oxford Robot Inst Oxford England

ISBN: (纸本)9781728190778

This work presents an approach for control, state-estimation and learning model (hyper)parameters for robotic manipulators. It is based on the active inference framework, prominent in computational neuroscience as a theory of the brain, where behaviour arises from minimizing variational free-energy. First, we show there is a direct relationship between active inference controllers, and classic methods such as PID control. We demonstrate its application for adaptive and robust behaviour of a robotic manipulator that rivals state-of-the-art. Additionally, we show that by learning specific hyperparameters, our approach can deal with unmodeled dynamics, damps oscillations, and is robust against poor initial parameters. The approach is validated on the 'Franka Emika Panda' 7 DoF manipulator. Finally, we highlight limitations of active inference controllers for robotic systems.

关键词： PI control Conferences computational neuroscience Estimation Robustness Filtering theory PD control

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