In this thesis, an intelligent controller for gun and/or sight stabilization of turret subsystems is developed using artificial neural networks. A classical proportional, integral and derivative (PID) controller equipped with a non-linear unbalance compensation algorithm is used as the low-level controller. The gains of this PID controller are tuned using a multilayered back-propagation neural network. These gains are modeled as a function of the error between the command and feedback signals and this model is generated by the function fitting property of neural networks as an estimate. The network is called as the &ldquo / Neural PID Tuner&rdquo / and it takes the current and previous errors as inputs and outputs the PID gains of the controller. Columb friction is the most important non-linearity in turret subsystems that heavily lower the efficiency of the controller. Another multilayered back-propagation neural network is used in order to increase the performance of the PID controller by identifying and compensating this Columb friction. This network utilizes the error between the output of the PID controller driving the physical system with Columb friction and the output of the identical PID controller driving a virtual equivalent linear system without Columb friction. The linear dynamics of the physical system is identified using a single layer linear neural network with pure linear activation function and the equivalent virtual linear system is emulated using this identification. The proposed methods are applied to both computer simulations and hardware experimental setup. In addition, sensitivity and performance analysis are performed both by using the mathematical model and hardware experimental setup.

Fuzzy logic systems (FLSs) are well known in the literature for their ability to model linguistics and system uncertainties. Due to this ability, FLSs have been successfully used in modeling and control applications such as medicine, finance, communications, and operations research. Moreover, the ability of higher order fuzzy systems to handle system uncertainty has become an interesting topic of research in the field. In particular, type-2 FLSs (T2 FLSs), systems consisting of fuzzy sets with fuzzy grades of membership, a feature that type-1 (T1) does not offer, are most well-known for this capability. The structure of T2 FLSs allows for the incorporation of uncertainty in the input membership grades, a common situation in reasoning with physical systems. General T2 FLSs have a complex structure, thus making them difficult to adopt on a large scale. As a result, interval T2 FLSs (IT2 FLSs), a special class of T2 FLSs, have recently shown great potential in various applications with input-output (I/O) system uncertainties. Due to the sophisticated mathematical structure of IT2 FLSs, little to no systematic analysis has been reported in the literature to use such systems in control design. Moreover, to date, designers have distanced themselves from adopting such systems on a wide scale because of their design complexity. Furthermore, the very few existing control methods utilizing IT2 fuzzy logic control systems (IT2 FLCSs) do not guarantee the stability of their system. Therefore, this thesis presents a systematic method for designing stable IT2 Takagi-Sugeno-Kang (IT2 TSK) fuzzy systems when antecedents are T2 fuzzy sets and consequents are crisp numbers (A2-C0). Five new inference mechanisms are proposed that have closed-form I/O mappings, making them more feasible for FLCS stability analysis. The thesis focuses on control applications for when (a) both plant and controller use A2-C0 TSK models, and (b) the plant uses T1 Takagi-Sugeno (T1 TS) and the controller uses IT2 TS models. In both cases, sufficient stability conditions for the stability of the closed-loop system are derived. Furthermore, novel linear matrix inequality-based algorithms are developed for satisfying the stability conditions. Numerical analyses are included to validate the effectiveness of the new inference methods. Case studies reveal that a well-tuned IT2 TS FLCS using the proposed inference engine can potentially outperform its T1 TSK counterpart, a result of IT2 having greater structural flexibility than T1. Moreover, due to the simple nature of the proposed inference engine, it is easy to implement in real-time control systems. In addition, a novel design methodology is proposed for IT2 TSK FLC for modular and reconfigurable robot (MRR) manipulators with uncertain dynamic parameters. A mathematical framework for the design of IT2 TSK FLCs is developed for tracking purposes that can be effectively used in real-time applications. To verify the effectiveness of the proposed controller, experiments are performed on an MRR with two degrees of freedom which exhibits dynamic coupling behavior. Results show that the developed controller can outperform some well-known linear and nonlinear controllers for different configurations. Therefore, the proposed structure can be adopted for the position control of MRRs with unknown dynamic parameters in trajectory-tracking applications. Finally, a rigorous mathematical analysis of the robustness of FLSs (both T1 and IT2) is presented in the thesis and entails a formulation of the robustness of FLSs as a constraint multi-objective optimization problem. Consequently, a procedure is proposed for the design of robust IT2 FLSs. Several examples are presented to demonstrate the effectiveness of the proposed methodologies. It was concluded that both T1 and IT2 FLSs can be designed to achieve robust behavior in various applications. IT2 FLSs, having a more flexible structure than T1 FLSs, exhibited relatively small approximation errors in the several examples investigated. The rigorous methodologies presented in this thesis lay the mathematical foundations for analyzing the stability and facilitating the design of stabilizing IT2 FLCSs. In addition, the proposed control technique for tracking purposes of MRRs will provide control engineers with tools to control dynamic systems with uncertainty and changing parameters. Finally, the systematic approach developed for the analysis and design of robust T1 and IT2 FLSs is of great practical value in various modeling and control applications.

Implementing self-management for enterprise systems is difficult. First, the scale and complexity of such systems makes it hard to understand and interpret system behavior or worse, the root causes of certain behaviors. Second, it is not clear how the goals specified at a system-level translate to component-level actions that drive the system. Third, the dynamic environments in which such systems operate requires self-management techniques that not only adapt the system but also adapt their own decision making processes. Finally, to build a self-management solution that is acceptable to administrators, it should have the properties of tractability and trust, which allow an administrator to both understand and fine-tune self-management actions. This dissertation work introduces, implements, and evaluates iManage, a novel system state-space based framework for enabling self-management of enterprise-scale systems. The system state-space, in iManage, is defined to be a collection of monitored system parameters and metrics (termed system variables). In addition, from amongst the system variables, it identifies the variables of interest, which determine the operational status of a system, and the controllable variables, which are the ones that can be deterministically modified to affect the operational status of a system. Using this formal representation, we have developed and integrated into iManage techniques that establish a probabilistic model relating the variables of interest and the controllable variables under the prevailing operational conditions. Such models are then used by iManage to determine corrective actions in case of SLA violations and/or to determine per-component ranges for controllable variables, which if independently adhered to by each component, lead to SLA compliance. To address the issue of scale in determining system models, iManage makes use of a novel state-space partitioning scheme that partitions the state-space into smaller sub-spaces thereby allowing us to more precisely model the critical system aspects. Our chosen modeling techniques are such that the generated models can be easily understood and modified by the administrator. Furthermore, iManage associates each proposed self-management action with a confidence-attribute that determines whether the action in question merits autonomic enforcement or not.

[Truncated abstract] This thesis analyses the problem of Intelligent Control for large industrial plants and suggests a hierarchical, distributed, object-oriented architecture for Intelligent Control. The architecture is called MLIAC (Multi Level Intelligent Adaptive Control) Architecture. The MLIAC architecture is inspired by biological control systems (which are flexible, and are capable of adapting to unstructured environments with ease) and the success of the distributed architecture SCADA (Supervisory Control and Data Acquisition) Systems. The MLIAC Architecture structures the decision and control mechanism for the real-time properties namely safety, liveliness, and timeliness . . . In addition, three case studies have been reported. The case studies cover the control of a Flexible Manufacturing System and the Mine Products Quality Control. The results show that MLIAC Knowledge Representation model meets the requirements of the Roth-Hayes benchmark regarding Knowledge Representation. The decisions taken are logically tractable. The software architecture is effective and easily implemented. The actual performance has been found to depend upon a number of factors discussed in this thesis. For the specification and design of Potline MLIAC software, a CASE package ("Software Through Pictures") has been used. The Potline MLIAC software has been developed using C⁄C++, SQL, 4 GL and RDBMS based on a Client-Server model. For computer simulation the Potline MLIAC software has been integrated with the MATLAB⁄SIMULINK package.

Thesis (M.Sc. [i.e. M.C.M.S.])--University of Waikato, 2006. / Includes bibliographical references (p. 167-173) Also available via the World Wide Web.

Conselho Nacional do Desenvolvimento Científico e Tecnológico (CNPq) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Fundação Araucária de Apoio ao Desenvolvimento Científico e Tecnológico do Paraná / Nos dias atuais, um enfoque considerável tem sido dado por pesquisas científicas aos acionamentos de motores elétricos, em destaque aos motores de indução trifásicos com rotor gaiola de esquilo, por serem comumente empregados em aplicações do dia-a-dia, principalmente em ambientes industriais, devido às suas inúmeras e bem conhecidas vantagens. Dentre algumas estratégias existentes para o acionamento destes motores, encontra-se o Controle Direto de Torque, um acionamento vetorial que proporciona um bom desempenho para aplicações de médias e baixas potências. Uma topologia dessa estratégia que envolve a utilização de um algoritmo de Modulação por Vetores Espaciais, denominada de DTC-SVM, tem sido amplamente considerada devido às suas vantagens frente à estrutura convencional. Contudo, em seu algoritmo existem malhas de controle de torque, fluxo e velocidade, que adotam geralmente controladores Proporcional-Integral. Neste trabalho, um estimador de fluxo baseado em modelo de tensão e corrente é considerado, no qual sua estrutura também contém um controlador Proporcional- Integral. Diante desse cenário, este trabalho visa contribuir com o aspecto de sintonia destes controladores, empregando metaheurísticas de otimização para efetuar o ajuste otimizado dos ganhos. Três algoritmos metaheurísticos são considerados: a Otimização por Colônia de Formigas, o Evolução Diferencial e a Otimização por Enxame de Partículas. Basicamente, intervalos de busca para as variáveis envolvidas, ou seja, os ganhos dos controladores, assim como uma função objetivo, que relacione os principais aspectos desejados de melhoria do sistema, devem ser definidos para o processo de otimização, o qual é realizado via simulação computacional. Visando avaliar a eficiência da metodologia proposta, neste trabalho são feitas algumas análises do sistema de acionamento DTC-SVM operando com as sintonias otimizadas e também com uma sintonia inicial, obtida com base em ajustes empíricos. Tais análises são feitas tanto em um ambiente de simulações computacionais, utilizando o software MATLAB/Simulink, quanto em um ambiente experimental, considerando um protótipo desenvolvido, no qual o sistema opera com reversão de velocidade, assim como com distúrbios de carga. Os resultados evidenciam desempenhos eficientes do acionamento operando com as sintonias otimizadas, principalmente durante os regimes transitórios e em operações de baixas velocidades. / Nowadays, a considerable focus has been given by scientific research to the electric motors drive, particularly to the three-phase induction motors with squirrel cage rotor, being commonly used in day-to-day applications, especially in industrial environments, due to its numerous and well known advantages. Among some strategies surveyed for driving these motors, there is the Direct Torque Control, a vector drive which provides good performance for medium and low power applications. A topology of this strategy involving the use of a Space Vector Modulation algorithm, called DTC-SVM, has been considered due to its advantages in comparison to conventional structure. However, in their algorithm there are torque, flux and speed control loops which generally consider Proportional-Integral controllers. In this work, a stator linkage flux estimator based on model of voltage and current is considered, in which its structure also contains a Proportional-Integral controller. Given this scenario, this work aims to contribute with the tuning aspect of these controllers, employing optimization metaheuristics aiming an optimized adjustment of the gains. Three metaheuristic algorithms are considered: Ant Colony Optimization, Differential Evolution and Particle Swarm Optimization. Basically, search ranges to the variables involved, ie. Proportional-Integral controller gains, as well as an objective function, which lists the main desired aspects of improving the system, must be set to the optimization process, which is performed by the computational simulation. Aiming to evaluate the effectiveness of this proposed methodology, in this work were made some analysis at the DTC-SVM drive system operating with the optimized tunings and a initial tuning, obtained by empiric adjusts. Such analysis were carried out either in a computer simulation environment, using MATLAB/Simulink software, as in a laboratory environment, considering a prototype developed, in which the system operates with speed reversal, as well as load disturbances. The results show efficient performances of the drive operating with the optimized tunings, especially during the transient periods and low speed operations.

Made available in DSpace on 2014-06-11T19:28:21Z (GMT). No. of bitstreams: 0 Previous issue date: 2011-12-20Bitstream added on 2014-06-13T19:16:08Z : No. of bitstreams: 1 euzebio_csg_me_bauru.pdf: 3031848 bytes, checksum: 6b7741a092864eabaa133ffdbcd4eed8 (MD5) / A necessidade de reduções de custos aliada ao aumento de qualidade das peças produzidas requer a implementação de sistemas inteligentes em ambientes industriais. O controle dos danos causados no processo de retificação é de interesse direto da indústria dependente desse processo. O objetivo deste trabalho é a proposição de modelos fuzzy empregados na detecção da queima de peças de aço SAE 1020 no processo de retificação plana. Foram realizados doze testes para diferentes condições de usinagem. Para cada teste foram coletados dados referentes a potência elétrica e emissão acústica (sinal puro). Os níveis de queima das peças foram analisados visualmente e com o auxílio computacional. A partir dos sinais de emissão acústica, potência de corte e parâmetros utilizando esses dois sinais, regras linguísticas foram estabelecidas para as diversas situações de queima (leve, média, severa) com a aplicação da lógica nebulosa utilizando-se o Toolbox do MATLAB. Quatro modelos práticos de sistema fuzzy foram desenvolvidos. O primeiro modelo com duas entradas apenas resultam num processo de simples análise. O segundo modelo possui a entrada adicional da estatística do desvio do valor médio (MVD), associando uma nova informação e precisão. Esse modelo é baseado em um sistema de inferência de três entradas, combinados dois a dois. O terceiro modelo, com 64 regras, baseia-se nas mesmas três entradas utilizadas no segundo modelo, combinadas três a três. Esses dois modelos diferem entre si pela base de regras desenvolvidas. O quarto modelo difere do terceiro devido ao número de regras e a entrada adicional baseada na potência de corte, do desvio padrão da mesma e do sinal RMS de emissão acústica. Apresentando respostas válidas, os quatro modelos desenvolvidos mostraram eficiência, precisão, confiabilidade e... / The need of costs reduction and quality increase of the produced pieces requires the implementation of intelligent systems in industrial environments. The control of damages caused during the grinding process is interesting to the industry that depends on such process. This work uses fuzzy logic as tool to classify and estimate burn levels in the grinding process in order to help controlling such process. Twelve tests were performed for different grinding conditions. For each test, data were concerning electrical power and acoustic emission (raw signal). The levels of burning parts were analyzed visually and with computer assistance. Based on acoustic emission signals, cutting power, and statistics using these two signals, liguistic rules were established for the various burn situations (slight, intermediate, sever) by applying fuzzy logic using the MATLAB toolbox. Four practical fuzzy system models were developed. This first model with two inputs resulted only in a simple analysis process. The second model has an additional MVD statistic input, associating information and precision. This model is base d on an inference system of three inputs, combined two by two. The third model with 64 rules is based on the same three inputs used in the second model, differ by the rule base developed. The forth model is different from the third one due to the number of rules, the additional input based on the cutting power, the standard deviation and the acoustic emission RMS signal. The four developed models presented valid responses, proving effective, accurate, reliable and easy to use for the determination of ground workpiece burn. In this analysis... (Complete abstract click electronic access below)

Thesis (MTech (Faculty of Engineering))--Cape Peninsula University of Technology, 2009.Included bibliographical references (p. 98-99).

Thesis (MTech (Information Technology))--Cape Peninsula University of Technology, 2005. / This thesis explores the design and implementation of an intelligent semantic machine control system with specific reference to human-machine interface design. The term "intelligent" refers to machines that can execute some level of decision taking in context. The term "semantic" refers to a structured language that allows user and machine to communicate. This study will explore all the key concepts about an intelligent semantic machine control system with human-machine interface. The key concepts to be investigated will include Artificial Intelligence, Intelligent Control, Semantics, Intelligent Machine Architecture, Human-Machine Interaction, Information systems and Graphical User Interface. The primary purpose of this study is to develop a methodology for designing a machine control system and its related human-machine interface.

Submitted by Liliane Ferreira (ljuvencia30@gmail.com) on 2018-12-04T14:59:58Z No. of bitstreams: 2 Dissertação - Ruan Michel Martins Domingos - 2018.pdf: 5209833 bytes, checksum: 69d9378d6ad33cb6458c4dc9035813bf (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2018-12-05T10:25:02Z (GMT) No. of bitstreams: 2 Dissertação - Ruan Michel Martins Domingos - 2018.pdf: 5209833 bytes, checksum: 69d9378d6ad33cb6458c4dc9035813bf (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2018-12-05T10:25:02Z (GMT). No. of bitstreams: 2 Dissertação - Ruan Michel Martins Domingos - 2018.pdf: 5209833 bytes, checksum: 69d9378d6ad33cb6458c4dc9035813bf (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2018-11-01 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / This work proposes a hierarchical control architecture to deal with the Trajectory Tracking Problem while an autonomous omnidirectional wheeled mobile robot operates. A traditional velocity controller and an intelligent decision-making neural network controller address the problem, considering the robot's kinematic and dynamic models. A neuroevolution technique evolves a smart Neurocontroller functionally attached to a Resolved Acceleration PI/PD Controller. The resulting control strategy shows to improve trajectory tracking errors during simulation studies. The Traditional and Intelligent controller combination showed very promising results even when applied in other trajectories that didn't belong to the original training set. / Este trabalho propõe uma arquitetura de controle hierárquico para lidar com o Problema de Rastreamento de Trajetória durante a operação de um robô móvel omnidirecional autônomo. Um controlador de velocidade tradicional e um controlador inteligente baseado em Redes Neurais para a tomada de decisão buscam resolvem o problema, considerando os modelos cinemático e dinâmico do robô. Uma técnica de neuroevolução evolui o neurocontrolador inteligente acoplado funcionalmente a um Controlador Dinâmico PI/PD de Aceleração Resolvida. A estratégia ou política de decisão de controle resultante mostra melhorias nos erros de rastreamento de trajetória durante estudos de simulação. A combinação entre Controle Tradicional e Controle Inteligente mostrou-se bastante eficaz mesmo aplicado em trajetórias não constantes do conjunto de treinamento.