Advanced control algorithms such as optimization-based controls are known to offer superior performance as well as systematic constraints handling when compared to classical control strategies. However, the complexity...
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ISBN:
(纸本)9781467371605
Advanced control algorithms such as optimization-based controls are known to offer superior performance as well as systematic constraints handling when compared to classical control strategies. However, the complexity of such advanced control methods makes their implementations difficult, especially on controllers with limited computational capacity such as the programmable logic controllers (PLC). This paper presents the capability of a low-cost low-end PLC for online computation of advanced control. In particular, an online quadratic program within the framework of input-constrained internal model control is implemented, and the effectiveness is illustrated using a hardware-in-the-loop experiment.
In this paper we present a Hardware-In-the-Loop setup for the simulation of complex mechatronic systems. The setup consists of two coupled brushless motors. One of them is the motor under test, which is used to design...
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ISBN:
(纸本)9781467379304
In this paper we present a Hardware-In-the-Loop setup for the simulation of complex mechatronic systems. The setup consists of two coupled brushless motors. One of them is the motor under test, which is used to design the control algorithm and to test the control software, while the other one simulates the device to be controlled. Libraries of mechanical and hydraulic components have been implemented in an IEC61131-3 language so that a complex system can be simulated in a relatively easy way and this allows for a rapid control prototyping. Practical issues are discussed and an illustrative example is shown to confirm the effectiveness of the setup.
This paper presents synthetic checking controller for robot manipulator with flexible joints, dynamics of executive motors and affect of disturbance based on radial basic function (RBF) neural network. A control algor...
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ISBN:
(纸本)9781479973972
This paper presents synthetic checking controller for robot manipulator with flexible joints, dynamics of executive motors and affect of disturbance based on radial basic function (RBF) neural network. A control algorithm is synthesized basing on the combination of backsteppping and RBF neural network in order to approximate the unknown functions. Finally, simulation results of a manipulator robot with single flexible joints based on Matlab-Simulink are presented to demonstrate the effectiveness of the proposed control algorithms
The most common approach for border patrol operations is the use of human personnel and manned ground vehicles, which is expensive, at times inefficient and sometimes even hazardous to people involved. The length of t...
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The most common approach for border patrol operations is the use of human personnel and manned ground vehicles, which is expensive, at times inefficient and sometimes even hazardous to people involved. The length of the US border, mostly covering unpopulated areas, with harsh atmospheric conditions makes it more susceptible to illegal human activities. Automated border surveillance by unattended, fixed, ground sensors forming an electronic fence has proven expensive, inefficient and was prone to unacceptable rate of false alarms. A better approach would be using Unmanned Aerial Vehicles (UAVs) in combination with such ground sensors. This would help improve the overall effectiveness of the surveillance system as a UAV could first scan the alert area before sending in personnel and vehicles, if deemed necessary. In this thesis, we are proposing border surveillance using multiple Unmanned Aerial Vehicles (UAVs) in combination with alert stations consisting of Unattended Ground Sensors (UGSs) along the border line or fence. Upon detecting an event, an alert would be triggered by any UGS. We simulate this process by reading probability data for different timestamps from a text file. And, based on utility values of each stations, two UAVs decide on which alert stations to service.
O trabalho tem como propósito principal a análise da estabilidade a pequenas perturbações em Sistemas Elétricos de Potência, que é representado pelo Modelo Sensibilidade de Potê...
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O trabalho tem como propósito principal a análise da estabilidade a pequenas perturbações em Sistemas Elétricos de Potência, que é representado pelo Modelo Sensibilidade de Potência. São analisados três sistemas teste, conhecidos na literatura como Sul-Brasileiro, simétrico de duas áreas e New England. A princípio os sistemas teste foram submetidos a um ponto de operação de modo a serem instáveis. É proposta a utilização dos Estabilizadores de Sistema de Potência e do dispositivo Thyristor Controlled Series Capacitor em conjunto com o controlador Power Oscillation Damping. Estes controladores possuem a função de inserir amortecimento adicional às oscilações de baixa frequência do Sistema Elétrico de Potência. Neste caso é necessário alocar e ajustar os parâmetros dos respectivos controladores de forma correta. As localizações dos controladores são determinadas pelos fatores de participação (Estabilizadores de Sistema de Potência) e a distância entre polo de interesse e zero da função de transferência de malha aberta do controlador Power Oscillation Damping (conjunto Thyristor Controlled Series Capacitor-Power Oscillation Damping). Para o ajuste de parâmetros dos controladores (Estabilizadores de Sistema de Potência e Power Oscillation Damping) são utilizados dois algoritmos de otimização baseados em enxame de partículas, sendo eles o Particle Swarm Optimization e de Bacterial Foraging Optimization orientado pelas formas de movimentação do Particle Swarm Optimization. A atuação dos controladores alocados e ajustados de acordo com os métodos propostos aumenta o amortecimento dos modos oscilatórios de baixa frequência dos sistemas teste. Este fato é verificado pela análise dos resultados obtidosThis work aims to analyze small signal stability in power systems represented by the power sensitivity model. Three test systems known in the literature, such as South Brazilian, Two Symmetrical Areas and New England are analyzed. At first, the systems were submitted to an opera
When confronting the complex problems, radial basis function (RBF) neural network has the advantages of adaptive and self-learning ability, but it is difficult to determine the number of hidden layer neurons, and the ...
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When confronting the complex problems, radial basis function (RBF) neural network has the advantages of adaptive and self-learning ability, but it is difficult to determine the number of hidden layer neurons, and the weights learning ability from hidden layer to the output layer is low;these deficiencies easily lead to decreasing learning ability and recognition precision. Aiming at this problem, we propose a new optimized RBF neural network algorithm based on genetic algorithm (GA-RBF algorithm), which uses genetic algorithm to optimize the weights and structure of RBF neural network;it chooses new ways of hybrid encoding and optimizing simultaneously. Using the binary encoding encodes the number of the hidden layer's neurons and using real encoding encodes the connection weights. Hidden layer neurons number and connection weights are optimized simultaneously in the new algorithm. However, the connection weights optimization is not complete;we need to use least mean square (LMS) algorithm for further leaning, and finally get a new algorithm model. Using two UCI standard data sets to test the new algorithm, the results show that the new algorithm improves the operating efficiency in dealing with complex problems and also improves the recognition precision, which proves that the new algorithm is valid.
Recent theory and experiments offer a new solution regarding how infant learners may break into word learning by using cross-situational statistics to find the underlying word referent mappings. Computational models d...
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Recent theory and experiments offer a new solution regarding how infant learners may break into word learning by using cross-situational statistics to find the underlying word referent mappings. Computational models demonstrate the in-principle plausibility of this statistical learning solution and experimental evidence shows that infants can aggregate and make statistically appropriate decisions from word referent co-occurrence data. We review these contributions and then identify the gaps in current knowledge that prevent a confident conclusion about whether cross-situational learning is the mechanism through which infants break into word learning. We propose an agenda to address that gap that focuses on detailing the statistics in the learning environment and the cognitive processes that make use of those statistics.
Ontology gives us a reliable group of concepts and the relations between concepts in an IOT system. It does not only save words of format but also accurately transfers semantic data between human users and the compute...
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Ontology gives us a reliable group of concepts and the relations between concepts in an IOT system. It does not only save words of format but also accurately transfers semantic data between human users and the computers. Hence, the usefulness of resources in IOT system depends on whether the domain ontology can be constructed effectively and correctly. In this paper we propose an automated method to construct the IOT ontology. First, we explain the necessity of introducing ontology automatic construction in IOT system and summarize the major challenges in existing approaches. Secondly, we introduced the existing ontology construction methods and summarize their issues. Thirdly, we give a framework of our ontology construction and research the key algorithms in detail: (1) knowledge-tuple extraction algorithm which contains contextual information;(2) concept semantic similarity algorithm which is based on the structure of tuple;(3) knowledge-tuple extraction model which is based on the structured information. Then we build a prototype and evaluate the ontology. Finally, we make conclusions and suggest directions for future research.
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