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ieee International Conference on Mechatronics, Electronics and Automotive Engineering (ICMEAE)

作者： Blanco-Ortega, A. Gomez-Becerra, F. A. Magadan-Salazar, A. Guzman-Valdivia, C. H. Delgado-Arcega, R. O. Robles, J. C. Arjona-Ulloa, M. J. Tecnol Nacl Mexico Ctr Nacl Invest & Desarrollo Tecnol Cuernavaca Morelos Mexico Tecnol Nacl Mexico Inst Tecnol Jose Mario Molina Pasquel & Henriquez Campus Puerto Vallarta Puerto Vallarta Jalisco Mexico Univ Politecn Zacatecas Zacatecas Zacatecas Mexico

ISBN: (纸本)9781728199047

In this paper a four floor elevator system as platform for Mechatronics engineering education is presented. The elevator system is proposed as a device to support undergraduate and postgraduate courses such as: dynamic analysis, control engineering, sensors and actuators, and programmable logic controllers. A Proportional Derivative (PD) type controller with trajectory tracking is proposed to carry the passengers (load) between the different floors. Then an algebraic identifier is used to estimate the mass in the elevator car (cabin) that can be applied online to avoid parametric uncertainty in the PD type controller and improve the performance in trajectory tracking task. Also, as an alternative solution, a robust GPI controller is proposed for better performance and minimize the tracking error using only cabin position measurements. Some simulation results are included using an elevator system virtual prototype in MSC Adams View (R) in co-simulation with Simulink-Matlab (R), to illustrate the performance of the algebraic identifier and the controllers proposed.

关键词： Algebraic identification control automático Seguimiento de trayectoria Sistemas electromecanicos

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Cascade control Solutions for Level control of Vertical Three Tank Systems 13

Cascade Control Solutions for Level Control of Vertical Thre...

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ieee 13th International symposium on Applied computational intelligence and Informatics (SACI)

作者： Hedrea, Elena-Lorena Precup, Radu-Emil Bojan-Dragos, Claudia-Adina Hedrea, Ciprian Ples, Denisa Popovici, Daniela Politehn Univ Timisoara Autom Appl Informat Dept Timisoara Romania Politehn Univ Timisoara Dept Math Timisoara Romania Politehn Univ Timisoara Autom Comput Fac Timisoara Romania

ISBN: (纸本)9781728106854

This paper presents the design and validation of two cascade control system (CCS) structures for the level control of vertical three tank systems. The first CCS structure consists of a state feedback controller in the inner control loop and a Proportional-Integral-Derivative (PID) controller in the outer control loop and the second CCS structure consists of a Tensor Product-based controller in the inner control loop and a PID controller in the outer control loop. The simulation results validate the suggested control structures Finally a comparative study is also given.

关键词： cascade control system structures PID controllers TP-based controllers vertical three tank systems

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Induction Radius-Based Hierarchical Flocking Algorithm for UAVs Clustering

Induction Radius-Based Hierarchical Flocking Algorithm for U...

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ieee symposium Series on computational intelligence (SSCI)

作者： Qiao, Xuhui Xu, Ziqiang Luo, Yanhong Sun, Yifan Liu, Yuxuan Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China

ISBN: (纸本)9781728124858

In this paper, a modified Reynold flocking algorithm is proposed based on the similarity between formation flying of UAVs and the behavior of biological groups. As the number of UAVs increasing, the UAVs far away from the leader cannot get good flocking effects due to time lag and communication failure. Therefore, in order to get better performance, a new hierarchical control approach is firstly developed according to the induction radius of leaders. The UAVs within the leader's induction radius follow the leader, while the UAVs outside the radius follow the second-level UAVs. Then an artificial potential field function is further constructed to keep the UAV separated and aggregated, so that the UAVs can realize the obstacle avoidance. Finally, it is verified by simulation that the UAVs can realize the expected formation under the proposed hierarchical flocking algorithm.

关键词： flocking clustering formation control UAVs

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Robust Trajectory Tracking of Delta Parallel Robot Using Sliding Mode control

Robust Trajectory Tracking of Delta Parallel Robot Using Sli...

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ieee symposium Series on computational intelligence (SSCI)

作者： Su, Tingting Liang, Xu He, Guangping Zhao, Quanliang Zhao, Lei North China Univ Technol Beijing Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing Peoples R China

ISBN: (纸本)9781728124858

For purpose of realizing robust trajectory tracking of parallel Delta robot with uncertain model parameters, a sliding mode control approach based on Delta robot dynamics is proposed. Once the reference trajectory is given, we can obtain the sliding mode controller, so that Delta parallel robot can move along the reference trajectory. Meanwhile, the disturbance and uncertainty of robot system are considered in the proposed method, which is beneficial to improve the reliability of controller and practicability of robot. The system stability is proved by Lyapunov method. Simulation experiment results demonstrate that the robust trajectory tracking of parallel Delta robot can be realized by the proposed method, and the designed controller is simple, robust and reliable.

关键词： parallel robot sliding mode control stability trajectory tracking

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Distributed Collision-Avoidance Formation control: A Velocity Obstacle-Based Approach

Distributed Collision-Avoidance Formation Control: A Velocit...

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ieee symposium Series on computational intelligence (SSCI)

作者： Hu, Yifan Yu, Han Zhong, Yang Lv, Yuezu Southeast Univ Sch Math Nanjing 210000 Jiangsu Peoples R China Southeast Univ Sch Cyber Sci & Engn Nanjing Peoples R China

ISBN: (纸本)9781728124858

This paper proposes a discrete-time algorithm for formation control of multi-agent systems with collision avoidance, where the agents' velocity and steering constraints are also considered. To ensure the collision avoidance, the velocity obstacle and reciprocal velocity obstacle methods are introduced to modify the distributed formation algorithm, where each agent uses velocity obstacle method to avoid collision with obstacles, and reciprocal velocity obstacle method to avoid collision with other agents. In this sense, each agent has the ability to pass through complex obstacle environments by autonomously changing prefer velocity. Simulation results show that the proposed algorithm can achieve formation task and meanwhile guarantee collision avoidance.

关键词： Formation control collision avoidance velocity and steering constraints

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Reinforcement Learning based Lane Change Decision-Making with Imaginary Sampling

Reinforcement Learning based Lane Change Decision-Making wit...

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ieee symposium Series on computational intelligence (SSCI)

作者： Li, Dong Zhao, Dongbin Zhang, Qichao Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781728124858

This paper investigates the autonomous driving lane change problem with reinforcement learning methods. A two-stage control method is proposed which includes a decision-making module computing the high-level lane change action and a lateral control module outputting the low-level steering angle. Due to the decoupling system framework, the proposed controller can flexibly cancel the previous improper lane change command. To improve the data efficiency in the lateral control module, the Gaussian process is employed to modeling the local time-interval system models. These models are used to generate imaginary samples and facilitate the reinforcement learning training process. The lateral control experiments show that the imaginary samples can improve the data efficiency and speed up the training process. Finally, the two-lane scene and three-lane scene experiments validate the effectiveness of the proposed two-stage lane change method.

关键词： reinforcement learning gaussian process autonomous driving Lane-Changing

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A Symmetrical Component Feature Extraction Method for Fault Detection in Induction Machines

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ieee TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2019年第9期66卷 7281-7289页

作者： St-Onge, Xavier F. Cameron, James Saleh, Saleh Scheme, Erik J. Univ New Brunswick Dept Elect & Comp Engn Fredericton NB E3B 5A3 Canada

Induction motors (IMs) are among the fully developed electromechanical technologies that are still in use today. Over the course of the last century, their structure, control, and operation have been undergone through several stages of development. Among stages of development, the automated control and continuous monitoring of IMs has benefited from the emergence of modern artificial intelligence (Al) methods. IM automation schemes have demonstrated the ability to provide machine fault detection and diagnosis (FDD) function. Al based FDD methods in IMs have employed frequency-domain, time-frequency, and time-domain analyses as the basis of their feature extraction schemes. A promising feature extraction scheme is one that uses symmetrical components (SCs) in time-domain FDD systems. Current SC-based approaches, however, are limited in their generalizability to different fault classes, may require detailed machine models, and can suffer from computational limitations. In this paper, an improved feature extraction method that uses SCs for a pattern recognition based FDD scheme for three-phase (3 phi) IMs will be presented. This novel feature extraction method will be implemented and verified experimentally to demonstrate high classification performance, increased generalizability, and low computational cost.

关键词： Artificial intelligence (AI) fault detection and diagnosis (FDD) feature extraction induction machines (IMs) machine learning symmetrical components (SCs) monitoring

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Deep Recurrent Neural Networks for Nonlinear System Identification

Deep Recurrent Neural Networks for Nonlinear System Identifi...

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ieee symposium Series on computational intelligence (SSCI)

作者： Schuessler, Max Muenker, Tobias Nelles, Oliver Univ Siegen Automat Control Mechatron D-57076 Siegen Germany

ISBN: (纸本)9781728124858

Deep recurrent neural networks are used as a means for nonlinear system identification. It is shown that state space models can be transformed into recurrent neural networks and vice versa. This transformation and the understanding of the long short-term memory cell in terms of common system identification nomenclature makes the advances in deep learning more accessible to the controls and system identification communities. A systematic study of deep recurrent neural networks is carried out on a state-of-the-art system identification benchmark. The results indicate that if high amounts of data are available, standard recurrent neural networks achieve comparable performance to state-of-the-art system identification methods.

关键词： nonlinear system identification deep learning recurrent neural networks

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BCINet: An Optimized Convolutional Neural Network for EEG-Based Brain-Computer Interface Applications

BCINet: An Optimized Convolutional Neural Network for EEG-Ba...

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ieee symposium Series on computational intelligence (SSCI)

作者： Avinash Kumar Singh Xian Tao Australian Artificial Intelligence Institute University of Technology Sydney Ultimo Australia Chinese Academy of Sciences Research Center of Precision Sensing and Control Institute of Automation Beijing China

ISBN: (数字)9781728125473

ISBN: (纸本)9781728125480

EEG based brain-computer interface (BCI) allows people to communicate and control external devices using brain signals. The application of BCI ranges from assisting in disabilities to interaction in a virtual reality environment by detecting user intent from EEG signals. The major problem lies in correctly classifying the EEG signals to issue a command with minimal requirement of pre-processing and resources. To overcome these problems, we have proposed, BCINet, a novel optimized convolution neural network model. We have evaluated the BCINet over two EEG based BCI datasets collected in mobile brain/body imaging (MoBI) settings. BCINet significantly outperforms the classification for two datasets with up to 20% increase in accuracy while fewer than 75% trainable parameters. Such a model with improved performance while less requirement of computation resources opens the possibilities for the development of several real-world BCI applications with high performance.

关键词： Electroencephalography Convolution Brain modeling Task analysis Deep learning Two dimensional displays Solid modeling

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An Analytical Approach to Predict the COVID-19 Death Rate in Bangladesh Utilizing Multiple Regression and SEIR Model

An Analytical Approach to Predict the COVID-19 Death Rate in...

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

作者： Tamal Joyti Roy Md. Ashiq Mahmood Aninda Mohanta Diti Roy Institute of Information and Communication Technology Khulna University of Engineering & Technology (KUET) Khulna Bangladesh Institute of Engineering & Management Kolkata

ISBN: (数字)9781665478694

ISBN: (纸本)9781665478700

The epidemic of COVID-19 has turned out to be a huge fear for the world. There is currently no advisable drug or cure available to treat this condition. According to WHO statistics, COVID-19 has become a progressive lung illness that is spread by respiratory droplets and other forms of contact. According to WHO, there is still no treatment or defensive plan that has risen till the period to encounter the COVID- 19 pandemic that was arisen in China in late 2019. The purpose of our study is to predict the COVID-19 situation by analyzing the death rate, recovery rate, and susceptibility rate with the help of the regression model and SEIR model. Two analytical models (SEIR and Regression) have been used. Our analysis has shown the prediction of the COVID-19 death rate in Bangladesh with the help of a Regression and SEIR model. We have analyzed the instances per million, number of death rates per million from the SEIR and Regression results and compared them with the real-time result. We have used a valid data set of Bangladesh, collected from the Institute of Epidemiology, Disease control and Research (ICR) from 18 March 2020 to July 18, 2021. Our experimental result shows promising performance. Examples and descriptions are provided to explain the technique.

关键词： COVID-19 Analytical models computational modeling Government Predictive models Real-time systems Standards

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