2014 IEEE International Symposium on Intelligent Control (ISIC)最新文献

Centralized non-convex model predictive control for cooperative collision avoidance of networked vehicles 网络车辆协同避碰集中非凸模型预测控制

2014 IEEE International Symposium on Intelligent Control (ISIC) Pub Date : 2014-12-01 DOI: 10.1109/ISIC.2014.6967623

Bassam Alrifaee, Masoumeh Ghanbarpour Mamaghani, D. Abel

引用次数: 21

Fault tolerant control of an omnidirectional robot using a switched Takagi-Sugeno approach 基于切换Takagi-Sugeno方法的全向机器人容错控制

2014 IEEE International Symposium on Intelligent Control (ISIC) Pub Date : 2014-12-01 DOI: 10.1109/ISIC.2014.6967646

D. Rotondo, F. Nejjari, V. Puig

引用次数: 2

Indoor granular presence sensing with an ultrasonic circular array sensor 室内颗粒存在感与超声圆形阵列传感器

2014 IEEE International Symposium on Intelligent Control (ISIC) Pub Date : 2014-12-01 DOI: 10.1109/ISIC.2014.6967633

S. Naghibzadeh, A. Pandharipande, D. Caicedo, G. Leus

引用次数: 4

Design of robust predictive fault-tolerant control for Takagi-Sugeno fuzzy systems: Application to the twin-rotor system Takagi-Sugeno模糊系统鲁棒预测容错控制设计:在双转子系统中的应用

2014 IEEE International Symposium on Intelligent Control (ISIC) Pub Date : 2014-12-01 DOI: 10.1109/ISIC.2014.6967616

P. Witczak, M. Witczak, K. Patan, R. Stetter

引用次数: 3

Consensus with convergence rate in directed networks with multiple non-differentiable input delays 具有多个不可微输入时滞的有向网络的收敛速率一致性

2014 IEEE International Symposium on Intelligent Control (ISIC) Pub Date : 2014-12-01 DOI: 10.1109/ISIC.2014.6967595

H. Savino, F. O. Souza, L. Pimenta

引用次数: 9

Model predictive control with on-line optimal linearisation 在线最优线性化模型预测控制

2014 IEEE International Symposium on Intelligent Control (ISIC) Pub Date : 2014-12-01 DOI: 10.1109/ISIC.2014.6967645

M. Lawrynczuk

引用次数: 1

Fault tolerant control design for a wind farm benchmark via fuzzy modelling and identification 基于模糊建模与辨识的风电场基准容错控制设计

2014 IEEE International Symposium on Intelligent Control (ISIC) Pub Date : 2014-12-01 DOI: 10.1109/ISIC.2014.6967650

S. Simani, S. Farsoni, P. Castaldi

{"title":"Fault tolerant control design for a wind farm benchmark via fuzzy modelling and identification","authors":"S. Simani, S. Farsoni, P. Castaldi","doi":"10.1109/ISIC.2014.6967650","DOIUrl":"https://doi.org/10.1109/ISIC.2014.6967650","url":null,"abstract":"In order to improve the safety, the reliability, and the efficiency of wind farm installations, thus avoiding expensive unplanned maintenance, the accommodation of faults in their earlier occurrence is fundamental. Therefore, the main contribution of this paper consists of the development of a tolerant control scheme applied by means of a direct and viable approach. In particular, a data-driven strategy based on fuzzy logic is exploited for deriving the fault tolerant control scheme. Fuzzy theory is exploited since it is able to approximate easily unknown nonlinear models and manage uncertain data. Moreover, these fuzzy prototypes are directly identified from the wind farm measurements and lead to the straightforward design of the fault tolerant control scheme. In general, an analytic approach, where the system nonlinearity is explicitly considered, would require more complex control design methodologies. This aspect of the work, followed by the simpler solution relying on fuzzy rules, represents the key point when on-line implementations are considered for a viable application of the proposed methodology. A realistic wind farm simulator is used to validate the achieved performances of the suggested methodology.","PeriodicalId":309111,"journal":{"name":"2014 IEEE International Symposium on Intelligent Control (ISIC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129974198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Intelligent redundant systems for small turbo-compressor engines 小型涡轮压缩发动机的智能冗余系统

2014 IEEE International Symposium on Intelligent Control (ISIC) Pub Date : 2014-12-01 DOI: 10.1109/ISIC.2014.6967641

R. Andoga, L. Főző, Jozef Povazan, J. Judičák, L. Madarász

引用次数: 3

Distributed sensing, learning and control in an assistive manipulator 辅助机械臂的分布式感知、学习与控制

2014 IEEE International Symposium on Intelligent Control (ISIC) Pub Date : 2014-12-01 DOI: 10.1109/ISIC.2014.6967619

M. Stoelen, V. F. Tejada, Alberto Jardón Huete, F. Bonsignorio, C. Balaguer

{"title":"Distributed sensing, learning and control in an assistive manipulator","authors":"M. Stoelen, V. F. Tejada, Alberto Jardón Huete, F. Bonsignorio, C. Balaguer","doi":"10.1109/ISIC.2014.6967619","DOIUrl":"https://doi.org/10.1109/ISIC.2014.6967619","url":null,"abstract":"One of the grand challenges for the robotics community is to create robots that operate robustly in realworld scenarios. Most current robots are limited to factories, laboratories or similar controlled settings. This contrasts with the seeming ease with which insects, animals and humans handle uncertainty, dynamic events and complexity. Assistive robots are for example being envisioned for aiding elderly and disabled persons in their homes. A key skill for these robots will be to operate in, and physically manipulate, daily life environments. However, it is unclear how to achieve this while complying with the safety and reliability requirements of such devices. Distributed Adaptive Control (DAC) is an example of a biologically inspired architecture for control and adaptation, where the lowest unit is the reflex. We here explore recent work on extending this idea to shared control of assistive robot manipulators. That is, where sensing, learning and control are distributed throughout the system, and across both user and robot. We show that such a distributed approach can reduce the need for central information processing, exact internal representations, and “global” approaches to learning in the robot. The reduced algorithmic complexity can help increase the robustness and usability of the system on real-world tasks.","PeriodicalId":309111,"journal":{"name":"2014 IEEE International Symposium on Intelligent Control (ISIC)","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117229501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Robust model predictive control using neural networks 神经网络鲁棒模型预测控制

2014 IEEE International Symposium on Intelligent Control (ISIC) Pub Date : 2014-12-01 DOI: 10.1109/ISIC.2014.6967615

K. Patan, P. Witczak

引用次数: 4