No description available.

The digitization of classical control systems presents a number of challenges and opportunities with respect to the miniaturization, distribution, reliability verification and obsolescence of both the controller and the underlying system under control. A method for the design of proportional-integral-derivative (PID) compensators realized in the form of all-digital components is presented. All-digital refers to a system implementation that is realizable with a wide range of digital logic components including discrete digital logic elements and programmable logic devices (PLDs) such as field-programmable gate arrays. The proportional, integral and derivative components of the classical PID control law were re-envisioned in terms of frequency of occurrences or counts for adaptation to combinatorial and sequential digital logic. Modification of the control scheme around this newly formed representation of system error enables the development of a PID-like FPGA-based or PLD-based controller. Details of the design of an all-digital PID-like controller including abstract, causal block diagrams and a MATLAB® and Simulink® based implementation are presented. The compensator was simulated in a velocity tracking DC motor control application and was found to perform comparably to that of a classical PID based control. Methods for assessing the resultant stability of an all-digital PID compensated system under control are discussed.

This thesis deals with chemical plants constituted by parallel batch-continuous production lines with shared resources. For such plants, it is highly desirable to have optimal operation schedules which determine the starting times of the various batch processes and the flow rates of the continuous processes in order to maximize the average plant productivity and to have a continuous production without interruptions. This optimization problem is constrained by the limitation of the resources that are shared by the reactors and by the capacities of the various devices that constitute the plant. Such plants are "hybrid" by nature because they combine both continuous-time dynamics and discrete-event dynamics. The formalism of "Hybrid Automata" is there fore well suited for the design of plant models. The first contribution of this thesis is the development of a hybrid automaton model of the chemical plant in the Matlab-Simulink-Stateflow environment and its use for the design of an optimal periodic schedule that maximises the plant productivity. Using a sensitivity analysis and the concept of Poincaré; map, it is shown that the optimal schedule is a stable limit cycle of the hybrid system that attracts the system trajectories starting in a wide set of initial conditions. The optimal periodic schedule is valid under the assumption that the hybrid model is an exact description of the plant. Under perturbations on the plant parameters, it is shown that two types of problems may arise. The first problem is a drift of the hybrid system trajectory which can either lead to a convergence to a new stable sub-optimal schedule or to a resource conflict. The second problem is a risk of overflow or underflow of the output buffer tank. The second contribution of the thesis is the analysis of feedback control strategies to avoid these problems. For the first problem, a control policy based on a model predictive control (MPC) approach is proposed to avoid resource conflicts. The feedback control is run on - line with the hybrid Simulink-Stateflow simulator used as an internal model. For the solution of the second problem, a classical PI control is used. The goal is not only to avoid over- or under-filling of the tank but also to reduce the amplitude of outflow rate variations as much as possible. A methodological analysis for the PI controller tuning is presented in order to achieve an acceptable trade-off between these conflicting objectives.

High blood pressure, also called hypertension is one of the most common worldwide diseases afflicting humans and is a major risk factor for stroke, myocardial infarction, vascular disease, and chronic kidney disease. If blood pressure is controlled and oscillations in the hemodynamic variables are reduced, patients experience fewer complications after surgery. In clinical practice, this is usually achieved using manual drug delivery. Given that different patients have different sensitivity and reaction time to drugs, determining manually the right drug infusion rates may be difficult. This is a problem where automatic drug delivery can provide a solution, especially if it is designed to adapt to variations in the patient’s conditions. This research work presents an investigation into the development of abnormal blood pressure (hypertension) controllers for postoperative patients. Control of the drugs infusion rates is used to simultaneously regulate the hemodynamic variables such as the Mean Arterial Pressure (MAP) and the Cardiac Output (CO) at the desired level. The implementation of optimal control system is very essential to improve the quality of patient care and also to reduce the workload of healthcare staff and costs. Many researchers have conducted studies earlier on modelling and/or control of abnormal blood pressure for postoperative patients. However, there are still many concerns about smooth transition of blood pressure without any side effect. The blood pressure is classified in two categories: high blood pressure (Hypertension) and low blood pressure (Hypotension). The hypertension often occurred after cardiac surgery, and the hypotension occurred during cardiac surgery. To achieve the optimal control solution for these abnormal blood pressures, many methods are proposed, one of the common methods is infusing the drug related to blood pressure to maintain it at the desired level. There are several kinds of vasodilating drugs such as Sodium Nitroprusside (SNP), Dopamine (DPM), Nitro-glycerine (NTG), and so on, which can be used to treat postoperative patients, also used for hypertensive emergencies to keep the blood pressure at safety level. A comparative performance of two types of algorithms has been presented in chapter four. These include the Internal Model Control (IMC), and Proportional-Integral-Derivative (PID) controller. The resulting controllers are implemented, tested and verified for three sensitivity patient response. SNP is used for all three patients’ situation in order to reduce the pressure smoothly and maintain it at the desire level. A Genetic Algorithms (GAs) optimization technique has been implemented to optimise the controllers’ parameters. A set of experiments are presented to demonstrate the merits and capabilities of the control algorithms. The simulation results in chapter four have demonstrated that the performance criteria are satisfied with the IMC, and PID controllers. On the other hand, the settling time for the PID control of all three patients’ response is shorter than the settling time with IMC controller. Using multiple interacting drugs to control both the MAP and CO of patients with different sensitivity to drugs is a challenging task. A Multivariable Model Reference Adaptive Control (MMRAC) algorithm is developed using a two-input, two-output patient model. Because of the difference in patient’s sensitivity to the drug, and in order to cover the wide ranges of patients, Model Reference Adaptive Control (MRAC) has been implemented to obtain the optimal infusion rates of DPM and SNP. This is developed in chapters five and six. Computer simulations were carried out to investigate the performance of this controller. The results show that the proposed adaptive scheme is robust with respect to disturbances and variations in model parameters, the simulation results have demonstrated that this algorithm cannot cover the wide range of patient’s sensitivity to drugs, due to that shortcoming, a PID controller using a Neural Network that tunes the controller parameters was designed and implemented. The parameters of the PID controller were optimised offline using Matlab genetic algorithm. The proposed Neuro-PID controller has been tested and validated to demonstrate its merits and capabilities compared to the existing approaches to cover wide range of patients.

Orientador: Prof. Dr. José Luis Azcue Puma / Dissertação (mestrado) - Universidade Federal do ABC, Programa de Pós-Graduação em Engenharia Elétrica, 2016. / Este trabalho apresenta o controle direto de potencia do gerador de relutancia variavel de 12/8 utilizando o controlador fuzzy PI auto-ajustavel e o controle vetorial do inversor conectado à rede eletrica. Inicialmente são estudados os principios de operação da maquina e a topologia do conversor eletronico de potencia que sera utilizado para aciona-lo. Usando o software de simulaçãoMatlab/Simulink é implementado o modelo da maquina de relutancia variavel utilizando as curvas caracteristicas, de corrente e torque, que foram extraídas utilizando dados experimentais. Utilizando o modelo da maquina é projetado o controlador de velocidade para sua operação no modo motor. Tambem sera projetado o controlador de potência PI utilizando o modelo estimado do sistema de conversão de energia eólica. A partir dos ganhos do controlador PI é projetado o controlador fuzzy PI autoajustavel com o objetivo de melhorar a resposta em regime permanente da potencia controlada. A potencia gerada pelo gerador de relutancia variavel é injetada à rede eletrica, atraves do controle independente das potencias ativa e reativa, para este proposito é utilizado um conversor trifasico de dois níveis. Na parte experimental deste trabalho foi implementado o controle de corrente por histerese para maquina operando no modo motor, posteriormente foi implementado o controle de corrente e controle da tens~ao no barramento CC para a máquina operando no modo gerador. Os resultados de simulação e experimentais se mostraram coerentes validando os controladores propostos. / This work presents the direct power control of the switched reluctance generator of 12/8 using the self-tuning fuzzy PI controller and the vector control for inverter connected to the grid. Initially, the machine's operating principles and the topology of the power electronics converter used to drive this machine are studied. The model of switched reluctance machine is implemented in Matlab/Simulink simulation software using the characteristic curves of current and torque extracted using experimental data. Using the machine's model, It is designed the speed controller for its operation in motor mode. Also, the PI controller is designed for the power control loop based on the estimation of the wind energy conversion system. Using the gains of the PI controller, It is designed the self-tuning fuzzy PI controller, in order to improve the steady state response of the power control loop. The power generated by the switched reluctance generator is injected into the power grid through the independent control of active and reactive power, for this purpose is used a three-phase two level converter. In the experimental part of this work was implemented the hysteresis current control for machine operating in motor mode, also, It was implemented the current and DC bus voltage control for the machine operating in generator mode. The simulation and experimental results were proved consistent and these results validate the proposed controllers.

Made available in DSpace on 2014-06-11T19:27:14Z (GMT). No. of bitstreams: 0 Previous issue date: 2006-08-03Bitstream added on 2014-06-13T20:27:41Z : No. of bitstreams: 1 morais_ts_me_ilha.pdf: 1779620 bytes, checksum: b9fc2164652299690dfd01e30502deab (MD5) / Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) / Neste trabalho desenvolveram-se metodologias de diagnóstico de falhas utilizando observadores de estado do tipo Filtro de Kalman, nas quais, as entradas para os observadores são identificadas utilizando as funções ortogonais de Fourier, Legendre e Chebyshev. Um tipo de observador denominado Proporcional-Integral é apresentado para a identificação de entradas desconhecidas. Este observador consegue estimar, sob certas condições, as entradas e ou distúrbios presentes no sistema e essas entradas são utilizadas para a diagnose de falha utilizando um observador do tipo Filtro de Kalman. Também é apresentado o desenvolvimento de uma metodologia de identificação de parâmetros bem como das forças de excitação, através das funções ortogonais, utilizando somente a resposta. Apresentam-se resultados obtidos por meio de simulações computacionais e realizados experimentalmente numa bancada de teste pertencente ao laboratório de vibrações mecânicas do Departamento de Engenharia Mecânica de Ilha Solteira. / In this work a methodology for fault diagnosis of mechanical systems was developed by using Kalman Filter state observes, in which the input of the observers are identified by using Fourier, Legendre and Chebyshev orthogonal functions. A proportional-integral observer is presented to the unknown input identification. This observer is able to find the unknown inputs of the system and these inputs are used to fault detection purposes by using a Kalman Filter Observer. The methodology for the identification of system parameters and excitation forces by using only the response of the system, through orthogonal functions. The methodology developed is applied to a mechanical structure containing vibrating tables, in the Mechanical Vibrations Laboratory, at Unesp, Ilha Solteira.

Over the last years, electrical roads have emerged as a cost effective and environmentally friendly solution towards a transportation system with less dependency on fossil fuels. This thesis presents the design of a search and locate system for road-bound conductive electrical roads with position control in two axes. The system is intended to find and follow the position of an electrified rail. The actuator system constitutes a firm groundwork for further research and development in this field. A laboratory test set up has been designed and both hardware and software parts have been constructed. The control of the system uses the CompactRIOTM technology from National Instruments. Results from the practical evaluation suggest that the non-linear characteristic of the system and the lack of direct position feedback from the motor rotation are the main causes of a non-accurate position control. Future development steps should focus on improving the mechanical design and include encoder feedback for the control loop as well as absolute automatic control with the incorporation of rail sensors.

High blood pressure, also called hypertension is one of the most common worldwide diseases afflicting humans and is a major risk factor for stroke, myocardial infarction, vascular disease, and chronic kidney disease. If blood pressure is controlled and oscillations in the hemodynamic variables are reduced, patients experience fewer complications after surgery. In clinical practice, this is usually achieved using manual drug delivery. Given that different patients have different sensitivity and reaction time to drugs, determining manually the right drug infusion rates may be difficult. This is a problem where automatic drug delivery can provide a solution, especially if it is designed to adapt to variations in the patient’s conditions. This research work presents an investigation into the development of abnormal blood pressure (hypertension) controllers for postoperative patients. Control of the drugs infusion rates is used to simultaneously regulate the hemodynamic variables such as the Mean Arterial Pressure (MAP) and the Cardiac Output (CO) at the desired level. The implementation of optimal control system is very essential to improve the quality of patient care and also to reduce the workload of healthcare staff and costs. Many researchers have conducted studies earlier on modelling and/or control of abnormal blood pressure for postoperative patients. However, there are still many concerns about smooth transition of blood pressure without any side effect. The blood pressure is classified in two categories: high blood pressure (Hypertension) and low blood pressure (Hypotension). The hypertension often occurred after cardiac surgery, and the hypotension occurred during cardiac surgery. To achieve the optimal control solution for these abnormal blood pressures, many methods are proposed, one of the common methods is infusing the drug related to blood pressure to maintain it at the desired level. There are several kinds of vasodilating drugs such as Sodium Nitroprusside (SNP), Dopamine (DPM), Nitro-glycerine (NTG), and so on, which can be used to treat postoperative patients, also used for hypertensive emergencies to keep the blood pressure at safety level. A comparative performance of two types of algorithms has been presented in chapter four. These include the Internal Model Control (IMC), and Proportional-Integral-Derivative (PID) controller. The resulting controllers are implemented, tested and verified for three sensitivity patient response. SNP is used for all three patients’ situation in order to reduce the pressure smoothly and maintain it at the desire level. A Genetic Algorithms (GAs) optimization technique has been implemented to optimise the controllers’ parameters. A set of experiments are presented to demonstrate the merits and capabilities of the control algorithms. The simulation results in chapter four have demonstrated that the performance criteria are satisfied with the IMC, and PID controllers. On the other hand, the settling time for the PID control of all three patients’ response is shorter than the settling time with IMC controller. Using multiple interacting drugs to control both the MAP and CO of patients with different sensitivity to drugs is a challenging task. A Multivariable Model Reference Adaptive Control (MMRAC) algorithm is developed using a two-input, two-output patient model. Because of the difference in patient’s sensitivity to the drug, and in order to cover the wide ranges of patients, Model Reference Adaptive Control (MRAC) has been implemented to obtain the optimal infusion rates of DPM and SNP. This is developed in chapters five and six. Computer simulations were carried out to investigate the performance of this controller. The results show that the proposed adaptive scheme is robust with respect to disturbances and variations in model parameters, the simulation results have demonstrated that this algorithm cannot cover the wide range of patient’s sensitivity to drugs, due to that shortcoming, a PID controller using a Neural Network that tunes the controller parameters was designed and implemented. The parameters of the PID controller were optimised offline using Matlab genetic algorithm. The proposed Neuro-PID controller has been tested and validated to demonstrate its merits and capabilities compared to the existing approaches to cover wide range of patients. / Libyan Ministry of Higher Education scholarship

No description available.

Orientador: Gilberto Pechoto de Melo / Banca: Amarildo Tabone Paschoalini / Banca: Valder Steffen Júnior / Resumo: Neste trabalho desenvolveram-se metodologias de diagnóstico de falhas utilizando observadores de estado do tipo Filtro de Kalman, nas quais, as entradas para os observadores são identificadas utilizando as funções ortogonais de Fourier, Legendre e Chebyshev. Um tipo de observador denominado Proporcional-Integral é apresentado para a identificação de entradas desconhecidas. Este observador consegue estimar, sob certas condições, as entradas e ou distúrbios presentes no sistema e essas entradas são utilizadas para a diagnose de falha utilizando um observador do tipo Filtro de Kalman. Também é apresentado o desenvolvimento de uma metodologia de identificação de parâmetros bem como das forças de excitação, através das funções ortogonais, utilizando somente a resposta. Apresentam-se resultados obtidos por meio de simulações computacionais e realizados experimentalmente numa bancada de teste pertencente ao laboratório de vibrações mecânicas do Departamento de Engenharia Mecânica de Ilha Solteira. / Abstract: In this work a methodology for fault diagnosis of mechanical systems was developed by using Kalman Filter state observes, in which the input of the observers are identified by using Fourier, Legendre and Chebyshev orthogonal functions. A proportional-integral observer is presented to the unknown input identification. This observer is able to find the unknown inputs of the system and these inputs are used to fault detection purposes by using a Kalman Filter Observer. The methodology for the identification of system parameters and excitation forces by using only the response of the system, through orthogonal functions. The methodology developed is applied to a mechanical structure containing vibrating tables, in the Mechanical Vibrations Laboratory, at Unesp, Ilha Solteira. / Mestre