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Validation of a new technique of production data analysis for single and multi-layer formations under controlled environmentMata, Domingo A. January 2007 (has links)
Thesis (M.S.)--West Virginia University, 2007. / Title from document title page. Document formatted into pages; contains viii, 105 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 95).
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Cycle-to-cycle control of plastic sheet heating on the AAA thermoforming machineYang, Shuonan, 1984- January 2008 (has links)
No description available.
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Terminal iterative learning for cycle-to-cycle control of industrial processesGauthier, Guy, 1960- January 2008 (has links)
No description available.
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Adaptive controller design for an autonomous twin-hulled surface vessel with uncertain displacement and dragUnknown Date (has links)
The design and validation of a low-level backstepping controller for speed and
heading that is adaptive in speed for a twin-hulled underactuated unmanned surface
vessel is presented. Consideration is given to the autonomous launch and recovery of an
underwater vehicle in the decision to pursue an adaptive control approach. Basic system
identification is conducted and numerical simulation of the vessel is developed and
validated. A speed and heading controller derived using the backstepping method and a
model reference adaptive controller are developed and ultimately compared through
experimental testing against a previously developed control law. Experimental tests show
that the adaptive speed control law outperforms the non-adaptive alternatives by as much
as 98% in some cases; however heading control is slightly sacrificed when using the
adaptive speed approach. It is found that the adaptive control law is the best alternative
when drag and mass properties of the vessel are time-varying and uncertain. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2014. / FAU Electronic Theses and Dissertations Collection
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Discrete iterative learning control of robotic manipulators馬裕旭, Ma, Yu-xu, Lecky. January 1991 (has links)
published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy
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Evolutionary design of fuzzy-logic controllers for overhead cranes張大任, Cheung, Tai-yam. January 2001 (has links)
published_or_final_version / Industrial and Manufacturing Systems Engineering / Doctoral / Doctor of Philosophy
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Aplicação de sistemas neuro-fuzzy e espectrometria no infravermelho próximo para a identificação em tempo real do teor de nitrogênio foliar em cana-de-açúcar /Coelho, Saulo Silva. January 2014 (has links)
Orientador: José Alfredo Colovan Ulson / Banca: Ricardo Augusto Souza Fernandes / Banca: André Luíz Andreoli / Resumo: O Brasil possui um grande potencial no setor do agronegócio e a associação desse setor com o desenvolvimento tecnológico deu origem à Agricultura de Precisão. Nesse contexto, o uso de sensores de Nitrogênio foliar de tempo real, especificamente os que utilizam como princípio de funcionamento e espectrometria, em conjunto com sistemas inteligentes computacionais, tem contribuído de forma decisiva para o incremento da produtividade no campo, evitando a aplicação excessiva de insumos e, assim, preservando o meio ambiente. Um insumo comumente aplicado na cultura de cana-de-açúcar é o Nitrogênio que, apesar de ter grande contribuição econômica, impõe grande impacto ao meio ambiente, principalmente na poluição de aquíferos e mananciais. Dessa maneira, a quantidade aplicada desse nutriente é de grande importância, pois sua falta limita o crescimento da cultura e seu excesso polui o meio ambiente. A determinação da quantidade de Nitrogênio pode ser feita por meio do uso de sensores espectrométricos na faixa do infravermelho próximo visando a cobertura verde da cultura. Entretanto, no estágio inicial de crescimento, a cobertura verde não é plena, de forma que o sensor detecta, além da cobertura verde, o solo e cobertura morta, acrescentando ruído à medida da refletância usada para a estimação do teor de Nitrogênio na planta. Nesse cenário, este trabalho tem o objetivo de mapear a relação entre os valores fornecidos pelo sensor N-SENSOR ALS do fabricante norueguês YARA e os teores reais de Nitrogênio na planta medidos em laboratório. Mais especificamente, sistema de inferência neuro-fuzzy adaptativo (ANFIS), redes neurais artificiais do tipo Perceptron de Múltiplas Camadas (PMC) e General Regression Neural Network (GRNN) serão empregados visando a identificação e o aprendizado da relação entre os valores medidos pelo sensor N-SENSOR ALS e os valores reais obtidos em laboratório, eliminando os ruídos... / Abstract: Brazil has a great potential in the agribusiness sector and the association of this sector with technological development gave rise to precision agriculture. In this context, the use of soil sensors for real-time, specifically those using operating principle as spectrometry, together with computational intelligent systems are contributing decisively to increasing productivity in the field, avoiding excessive use of inputs and thus preserving the environment. An ingredient commonly used in the cultivation of sugar cane is the nitrogen that, depite great economic contribution, imposes great impact on the environment, especially in aquifers and fountains pollution. Thus, the applied amount of this nutrient is of great importance, since the lack of limits crop growth and excess pollute the environment. The determination of the nitrogen content can be made through the use of spectrometric sensors in the near-infrared aiming the green cover crop. However, in the initial stage of growth the green coverage is not complete, so the sensor "sees" beyond the green cover, soil and mulch, adding noise to the measurement of reflectance used to estimate the percentage of nitrogen in plant. In this scenario, this paper aims to map the relationship between the values provided by the sensor N-SENSOR ALS Norwegian YARA manufacturer and the actual levels of nitrogen in the plant measured in the laboratory. More specifically, systems of inference adaptive neuro-fuzzy (ANFIS), artificial neural network Multilayer Perceptron type (MLP) and General Regression Neural Network (GRNN) are employed in order to identify learning and the relationship between the values measured by the sensor N-SENSOR ALS and actual obtaind in the laboratory, eliminating the noise imposed by non-green roof and external disturbances such as the variation of ambient light. The results indicate that the neuro-fuzzy approach has superior performance and neural networks can be used to correct... / Mestre
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Robot behavior learning with adaptive categorization in logical-perceptual space. / CUHK electronic theses & dissertations collectionJanuary 2001 (has links)
Fung Wai-keung. / "February 5, 2001." / Thesis (Ph.D.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (p. 109-116). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.
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Simulation studies of formation maneuvering under interactive force.January 2005 (has links)
by Chiu, Kit Chau. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaves 90-92). / Abstracts in English and Chinese. / ABSTRACT --- p.02 / 摘要 --- p.04 / ACKNOWLEDGEMENTS --- p.06 / TABLE OF CONTENTS --- p.07 / LIST OF FIGURES --- p.10 / LIST OF TABLES --- p.12 / Chapter 1 --- INTRODUCTION --- p.13 / Chapter 1.1 --- Application with formation flying --- p.14 / Chapter 1.2 --- Previous work --- p.16 / Chapter 1.3 --- The present work --- p.18 / Chapter 1.4 --- Thesis outline --- p.19 / Chapter 2 --- OPTIMIZATION IN DESIRED TRAJECTORY --- p.21 / Chapter 2.1 --- Problem formulation --- p.21 / Chapter 2.1.1 --- System model --- p.21 / Chapter 2.1.2 --- System constraints --- p.22 / Chapter 2.1.3 --- Cost function of the system --- p.23 / Chapter 2.2 --- Reformation as optimal control problem --- p.23 / Chapter 2.2.1 --- Polynomial form for input --- p.24 / Chapter 2.2.2 --- Problem simplification --- p.26 / Chapter 2.3 --- Numerical case studies --- p.27 / Chapter 2.3.1 --- Case study 2-1: Equal weightings in all units and directions --- p.27 / Chapter 2.3.2 --- Case study 2-2: Equal weightings in all directions but different weightings in control units --- p.30 / Chapter 2.3.3 --- Case 2-3: Different weightings in x-y-z directions but equal weightings in all control units --- p.33 / Chapter 2.4 --- Chapter summary --- p.35 / Chapter 3 --- OBSTACLE AVOIDANCE --- p.36 / Chapter 3.1 --- Additions of obstacle constraints --- p.36 / Chapter 3.2 --- Simulation case studies --- p.37 / Chapter 3.2.1 --- Case study 3-1: No obstacle --- p.38 / Chapter 3.2.2 --- Case study 3-2: Single obstacles --- p.40 / Chapter 3.2.3 --- Case study 3-3: Two obstacles --- p.42 / Chapter 3.2.4 --- Case study 3-4: Two obstacles and optimal velocity --- p.48 / Chapter 3.3 --- Chapter summary --- p.51 / Chapter 4 --- FUZZY INTERACTIVE FORCE BETWEEN ELEMENTS --- p.52 / Chapter 4.1 --- Region of repulsive force --- p.52 / Chapter 4.2 --- Region of attractive force --- p.53 / Chapter 4.3 --- Beyond the attractive region --- p.53 / Chapter 4.4 --- Interactive force as function of separation --- p.54 / Chapter 4.5 --- Fuzzy mapping --- p.55 / Chapter 4.6 --- Chapter summary --- p.58 / Chapter 5 --- VIRTUAL LEADER --- p.59 / Chapter 5.1 --- Virtual leader --- p.59 / Chapter 5.2 --- Two maneuverable elements and two virtual leaders --- p.60 / Chapter 5.3 --- Rotational Trajectories for the two virtual leaders --- p.61 / Chapter 5.4 --- Chapter summary --- p.65 / Chapter 6 --- OPIMIZATION BY INTERACTIVE FORCE --- p.66 / Chapter 6.1 --- Narrow channel passage --- p.66 / Chapter 6.2 --- Interactive forces --- p.68 / Chapter 6.3 --- Definition of interactive force --- p.69 / Chapter 6.4 --- Formulation as optimization problem --- p.71 / Chapter 6.4.1 --- Parameterization of f1 and f2 --- p.71 / Chapter 6.4.2 --- Reformulated optimization problem --- p.73 / Chapter 6.5 --- Simulation results --- p.74 / Chapter 6.6 --- Chapter summary --- p.77 / Chapter 7 --- MODIFICATION IN OBSTACLE --- p.78 / Chapter 7.1 --- Modification for interactive force --- p.78 / Chapter 7.2 --- Modification in obstacle description --- p.79 / Chapter 7.3 --- """Shortest distance"" between control unit and obstacle" --- p.80 / Chapter 7.4 --- Simulation case studies --- p.81 / Chapter 7.4.1 --- Case study 7-1: Single triangular obstacle --- p.81 / Chapter 7.4.2 --- Case study 7-2: Two triangular obstacles --- p.83 / Chapter 7.5 --- Chapter summary --- p.85 / Chapter 8 --- Conclusions and future works --- p.86 / Chapter 8.1 --- Conclusions --- p.86 / Chapter 8.2 --- Future works --- p.88 / Chapter 8.2.1 --- Fuzzy mapping --- p.88 / Chapter 8.2.2 --- Intrinsic parameters and properties --- p.89 / BIBLIOGRAPHY --- p.90
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Fuzzy neural networks for control of dynamic systems.Frayman, Yakov, mikewood@deakin.edu.au January 1999 (has links)
This thesis provides a unified and comprehensive treatment of the fuzzy neural networks as the intelligent controllers. This work has been motivated by a need to develop the solid control methodologies capable of coping with the complexity, the nonlinearity, the interactions, and the time variance of the processes under control. In addition, the dynamic behavior of such processes is strongly influenced by the disturbances and the noise, and such processes are characterized by a large degree of uncertainty.
Therefore, it is important to integrate an intelligent component to increase the control system ability to extract the functional relationships from the process and to change such relationships to improve the control precision, that is, to display the learning and the reasoning abilities. The objective of this thesis was to develop a self-organizing learning controller for above processes by using a combination of the fuzzy logic and the neural networks. An on-line, direct fuzzy neural controller using the process input-output measurement data and the reference model with both structural and parameter tuning has been developed to fulfill the above objective.
A number of practical issues were considered. This includes the dynamic construction of the controller in order to alleviate the bias/variance dilemma, the universal approximation property, and the requirements of the locality and the linearity in the parameters. Several important issues in the intelligent control were also considered such as the overall control scheme, the requirement of the persistency of excitation and the bounded learning rates of the controller for the overall closed loop stability. Other important issues considered in this thesis include the dependence of the generalization ability and the optimization methods on the data distribution, and the requirements for the on-line learning and the feedback structure of the controller. Fuzzy inference specific issues such as the influence of the choice of the defuzzification method, T-norm operator and the membership function on the overall performance of the controller were also discussed. In addition, the e-completeness requirement and the use of the fuzzy similarity measure were also investigated.
Main emphasis of the thesis has been on the applications to the real-world problems such as the industrial process control. The applicability of the proposed method has been demonstrated through the empirical studies on several real-world control problems of industrial complexity. This includes the temperature and the number-average molecular weight control in the continuous stirred tank polymerization reactor, and the torsional vibration, the eccentricity, the hardness and the thickness control in the cold rolling mills. Compared to the traditional linear controllers and the dynamically constructed neural network, the proposed fuzzy neural controller shows the highest promise as an effective approach to such nonlinear multi-variable control problems with the strong influence of the disturbances and the noise on the dynamic process behavior.
In addition, the applicability of the proposed method beyond the strictly control area has also been investigated, in particular to the data mining and the knowledge elicitation. When compared to the decision tree method and the pruned neural network method for the data mining, the proposed fuzzy neural network is able to achieve a comparable accuracy with a more compact set of rules. In addition, the performance of the proposed fuzzy neural network is much better for the classes with the low occurrences in the data set compared to the decision tree method. Thus, the proposed fuzzy neural network may be very useful in situations where the important information is contained in a small fraction of the available data.
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