The Experts below are selected from a list of 33384 Experts worldwide ranked by ideXlab platform
Frank L Lewis - One of the best experts on this subject based on the ideXlab platform.
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adaptive synchronisation of unknown nonlinear networked systems with prescribed performance
arXiv: Optimization and Control, 2018Co-Authors: Hashim A Hashim, Sami Elferik, Frank L LewisAbstract:This paper proposes an adaptive tracking control with prescribed performance function for distributive cooperative control of highly nonlinear multi-agent systems. The use of such approach confines the tracking error within a large Predefined Set to a Predefined smaller Set. The key idea is to transform the constrained system into unconstrained one through the transformation of the output error. Agents' dynamics are assumed unknown, and the controller is developed for a strongly connected structured network. The proposed controller allows all agents to follow the trajectory of the leader node, while satisfying the necessary dynamic requirements. The proposed approach guarantees uniform ultimate boundedness for the transformed error as well as a bounded adaptive estimate of the unknown parameters and dynamics. Simulations include two examples to validate the robustness and smoothness of the proposed controller against highly nonlinear heterogeneous multi-agent system with uncertain time-variant parameters and external disturbances. Keywords: Prescribed performance, Transformed error, Multi-agents, Distributed adaptive control, Adaptive Consensus, Transient, Steady-state error, Semi-global asymptotic stability, uniformly ultimately bounded, Nonlinear Networked Systems, Distributed Control, Robustness.
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adaptive synchronisation of unknown nonlinear networked systems with prescribed performance
International Journal of Systems Science, 2017Co-Authors: Hashim A Hashim, Sami Elferik, Frank L LewisAbstract:This paper proposes an adaptive tracking control with prescribed performance function for distributive cooperative control of highly nonlinear multi-agent systems. The use of such approach confines the tracking error within a large Predefined Set to a Predefined smaller Set. The key idea is to transform the constrained system into unconstrained one through the transformation of the output error. Agents’ dynamics are assumed unknown, and the controller is developed for a strongly connected structured network. The proposed controller allows all agents to follow the trajectory of the leader node, while satisfying the necessary dynamic requirements. The proposed approach guarantees uniform ultimate boundedness for the transformed error as well as a bounded adaptive estimate of the unknown parameters and dynamics. Simulations include two examples to validate the robustness and smoothness of the proposed controller against highly nonlinear heterogeneous multi-agent system with uncertain time-variant parameters and external disturbances.
Arben Cela - One of the best experts on this subject based on the ideXlab platform.
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brief paper trading quantization precision for update rates for systems with limited communication in the uplink channel
Automatica, 2010Co-Authors: Mohamed El Mongi Ben Gaid, Arben CelaAbstract:In many situations, control applications have to exchange information through limited bandwidth communication channels, which affect their behavior. For that reason, there is a strong need for methods that maximize the relevancy of the exchanged control signals. In general, increasing control signals' update frequency improves the disturbance rejection abilities whereas increasing their quantization precision improves the steady state performance. However, when the bandwidth is limited, increasing the update frequency necessitates the reduction of the quantization precision and vice versa. Motivated by these observations, and focusing on the uplink bandwidth limitations, an approach for the dynamical online state feedback assignment of control inputs' quantization precision and update rate is proposed. This approach, which is based on the model predictive control technique, enables us to choose the update rate and the quantization levels of control signals from a Predefined Set, in order to optimize the control performance. Practical stability properties of the approach are then studied. Finally, the effectiveness of the proposed method is illustrated on a simulation example.
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trading quantization precision for sampling rates in networked systems with limited communication
Conference on Decision and Control, 2006Co-Authors: Mohamed El Mongi Ben Gaid, Arben CelaAbstract:In many situations, control applications have to exchange a significant amount of information through limited bandwidth communication channels. This occurs in areas ranging from the underwater robotics to the control of satellites clusters. Bandwidth limitations impose constraints on information exchange. Consequently, there is a strong need for developing methods that maximize the relevancy of the exchanged control information. In general, increasing the sampling frequency improves the disturbance rejection abilities whereas increasing the quantization precision improves the steady state precision. However, when the bandwidth is limited, increasing the sampling frequency necessitates the reduction of the quantization precision. In the opposite, augmenting the quantization precision requires the lowering of the sampling frequency. Motivated by these observations, an approach for the dynamical on-line assignment of sampling frequencies and control inputs quantization is proposed. This approach, which is based on the Model Predictive Control (MPC) technique, enables to choose the sampling frequency and the quantization levels of control signals from a Predefined Set, in order to optimize the control performance. The effectiveness of this approach is illustrated on a simulation example.
Murat Demirbas - One of the best experts on this subject based on the ideXlab platform.
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short text classification in twitter to improve information filtering
International ACM SIGIR Conference on Research and Development in Information Retrieval, 2010Co-Authors: Bharath Sriram, Dave Fuhry, Engin Demir, Hakan Ferhatosmanoglu, Murat DemirbasAbstract:In microblogging services such as Twitter, the users may become overwhelmed by the raw data. One solution to this problem is the classification of short text messages. As short texts do not provide sufficient word occurrences, traditional classification methods such as "Bag-Of-Words" have limitations. To address this problem, we propose to use a small Set of domain-specific features extracted from the author's profile and text. The proposed approach effectively classifies the text to a Predefined Set of generic classes such as News, Events, Opinions, Deals, and Private Messages.
Martino Valenti - One of the best experts on this subject based on the ideXlab platform.
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short text categorization exploiting contextual enrichment and external knowledge
International ACM SIGIR Conference on Research and Development in Information Retrieval, 2014Co-Authors: Stefano Mizzaro, Marco Pavan, Ivan Scagnetto, Martino ValentiAbstract:We address the problem of the categorization of short texts, like those posted by users on social networks and microblogging platforms. We specifically focus on Twitter. Since short texts do not provide sufficient word occurrences, and they often contain abbreviations and acronyms, traditional classification methods such as "Bag-of-Words" have limitations. Our proposed method enriches the original text with a new Set of words, to add more semantic value by using information extracted from webpages of the same temporal context. Then we use those words to query Wikipedia, as an external knowledge base, with the final goal to categorize the original text using a Predefined Set of Wikipedia categories. We also present a first experimental evaluation that confirms the effectiveness of the algorithm design and implementation choices, highlighting some critical issues with short texts.
Hashim A Hashim - One of the best experts on this subject based on the ideXlab platform.
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adaptive synchronisation of unknown nonlinear networked systems with prescribed performance
arXiv: Optimization and Control, 2018Co-Authors: Hashim A Hashim, Sami Elferik, Frank L LewisAbstract:This paper proposes an adaptive tracking control with prescribed performance function for distributive cooperative control of highly nonlinear multi-agent systems. The use of such approach confines the tracking error within a large Predefined Set to a Predefined smaller Set. The key idea is to transform the constrained system into unconstrained one through the transformation of the output error. Agents' dynamics are assumed unknown, and the controller is developed for a strongly connected structured network. The proposed controller allows all agents to follow the trajectory of the leader node, while satisfying the necessary dynamic requirements. The proposed approach guarantees uniform ultimate boundedness for the transformed error as well as a bounded adaptive estimate of the unknown parameters and dynamics. Simulations include two examples to validate the robustness and smoothness of the proposed controller against highly nonlinear heterogeneous multi-agent system with uncertain time-variant parameters and external disturbances. Keywords: Prescribed performance, Transformed error, Multi-agents, Distributed adaptive control, Adaptive Consensus, Transient, Steady-state error, Semi-global asymptotic stability, uniformly ultimately bounded, Nonlinear Networked Systems, Distributed Control, Robustness.
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adaptive synchronisation of unknown nonlinear networked systems with prescribed performance
International Journal of Systems Science, 2017Co-Authors: Hashim A Hashim, Sami Elferik, Frank L LewisAbstract:This paper proposes an adaptive tracking control with prescribed performance function for distributive cooperative control of highly nonlinear multi-agent systems. The use of such approach confines the tracking error within a large Predefined Set to a Predefined smaller Set. The key idea is to transform the constrained system into unconstrained one through the transformation of the output error. Agents’ dynamics are assumed unknown, and the controller is developed for a strongly connected structured network. The proposed controller allows all agents to follow the trajectory of the leader node, while satisfying the necessary dynamic requirements. The proposed approach guarantees uniform ultimate boundedness for the transformed error as well as a bounded adaptive estimate of the unknown parameters and dynamics. Simulations include two examples to validate the robustness and smoothness of the proposed controller against highly nonlinear heterogeneous multi-agent system with uncertain time-variant parameters and external disturbances.
