18 resultados para switch state coarse fuzzy vector controller

em Universidade Federal do Rio Grande do Norte(UFRN)


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The so-called Dual Mode Adaptive Robust Control (DMARC) is proposed. The DMARC is a control strategy which interpolates the Model Reference Adaptive Control (MRAC) and the Variable Structure Model Reference Adaptive Control (VS-MRAC). The main idea is to incorporate the transient performance advantages of the VS-MRAC controller with the smoothness control signal in steady-state of the MRAC controller. Two basic algorithms are developed for the DMARC controller. In the first algorithm the controller's adjustment is made, in real time, through the variation of a parameter in the adaptation law. In the second algorithm the control law is generated, using fuzzy logic with Takagi-Sugeno s model, to obtain a combination of the MRAC and VS-MRAC control laws. In both cases, the combined control structure is shown to be robust to the parametric uncertainties and external disturbances, with a fast transient performance, practically without oscillations, and a smoothness steady-state control signal

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This work proposes the design, the performance evaluation and a methodology for tuning the initial MFs parameters of output of a function based Takagi-Sugeno-Kang Fuzzy-PI controller to neutralize the pH in a stirred-tank reactor. The controller is designed to perform pH neutralization of industrial plants, mainly in units found in oil refineries where it is strongly required to mitigate uncertainties and nonlinearities. In addition, it adjusts the changes in pH regulating process, avoiding or reducing the need for retuning to maintain the desired performance. Based on the Hammerstein model, the system emulates a real plant that fits the changes in pH neutralization process of avoiding or reducing the need to retune. The controller performance is evaluated by overshoots, stabilization times, indices Integral of the Absolute Error (IAE) and Integral of the Absolute Value of the Error-weighted Time (ITAE), and using a metric developed by that takes into account both the error information and the control signal. The Fuzzy-PI controller is compared with PI and gain schedule PI controllers previously used in the testing plant, whose results can be found in the literature.

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This work presents a proposal to detect interface in atmospheric oil tanks by installing a differential pressure level transmitter to infer the oil-water interface. The main goal of this project is to maximize the quantity of free water that is delivered to the drainage line by controlling the interface. A Fuzzy Controller has been implemented by using the interface transmitter as the Process Variable. Two ladder routine was generated to perform the control. One routine was developed to calculate the error and error variation. The other was generate to develop the fuzzy controller itself. By using rules, the fuzzy controller uses these variables to set the output. The output is the position variation of the drainage valve. Although the ladder routine was implemented into an Allen Bradley PLC, Control Logix family it can be implemented into any brand of PLCs

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From their early days, Electrical Submergible Pumping (ESP) units have excelled in lifting much greater liquid rates than most of the other types of artificial lift and developed by good performance in wells with high BSW, in onshore and offshore environments. For all artificial lift system, the lifetime and frequency of interventions are of paramount importance, given the high costs of rigs and equipment, plus the losses coming from a halt in production. In search of a better life of the system comes the need to work with the same efficiency and security within the limits of their equipment, this implies the need for periodic adjustments, monitoring and control. How is increasing the prospect of minimizing direct human actions, these adjustments should be made increasingly via automation. The automated system not only provides a longer life, but also greater control over the production of the well. The controller is the brain of most automation systems, it is inserted the logic and strategies in the work process in order to get you to work efficiently. So great is the importance of controlling for any automation system is expected that, with better understanding of ESP system and the development of research, many controllers will be proposed for this method of artificial lift. Once a controller is proposed, it must be tested and validated before they take it as efficient and functional. The use of a producing well or a test well could favor the completion of testing, but with the serious risk that flaws in the design of the controller were to cause damage to oil well equipment, many of them expensive. Given this reality, the main objective of the present work is to present an environment for evaluation of fuzzy controllers for wells equipped with ESP system, using a computer simulator representing a virtual oil well, a software design fuzzy controllers and a PLC. The use of the proposed environment will enable a reduction in time required for testing and adjustments to the controller and evaluated a rapid diagnosis of their efficiency and effectiveness. The control algorithms are implemented in both high-level language, through the controller design software, such as specific language for programming PLCs, Ladder Diagram language.

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The progressing cavity pump artificial lift system, PCP, is a main lift system used in oil production industry. As this artificial lift application grows the knowledge of it s dynamics behavior, the application of automatic control and the developing of equipment selection design specialist systems are more useful. This work presents tools for dynamic analysis, control technics and a specialist system for selecting lift equipments for this artificial lift technology. The PCP artificial lift system consists of a progressing cavity pump installed downhole in the production tubing edge. The pump consists of two parts, a stator and a rotor, and is set in motion by the rotation of the rotor transmitted through a rod string installed in the tubing. The surface equipment generates and transmits the rotation to the rod string. First, is presented the developing of a complete mathematical dynamic model of PCP system. This model is simplified for use in several conditions, including steady state for sizing PCP equipments, like pump, rod string and drive head. This model is used to implement a computer simulator able to help in system analysis and to operates as a well with a controller and allows testing and developing of control algorithms. The next developing applies control technics to PCP system to optimize pumping velocity to achieve productivity and durability of downhole components. The mathematical model is linearized to apply conventional control technics including observability and controllability of the system and develop design rules for PI controller. Stability conditions are stated for operation point of the system. A fuzzy rule-based control system are developed from a PI controller using a inference machine based on Mandami operators. The fuzzy logic is applied to develop a specialist system that selects PCP equipments too. The developed technics to simulate and the linearized model was used in an actual well where a control system is installed. This control system consists of a pump intake pressure sensor, an industrial controller and a variable speed drive. The PI control was applied and fuzzy controller was applied to optimize simulated and actual well operation and the results was compared. The simulated and actual open loop response was compared to validate simulation. A case study was accomplished to validate equipment selection specialist system

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Conventional control strategies used in shunt active power filters (SAPF) employs real-time instantaneous harmonic detection schemes which is usually implements with digital filters. This increase the number of current sensors on the filter structure which results in high costs. Furthermore, these detection schemes introduce time delays which can deteriorate the harmonic compensation performance. Differently from the conventional control schemes, this paper proposes a non-standard control strategy which indirectly regulates the phase currents of the power mains. The reference currents of system are generated by the dc-link voltage controller and is based on the active power balance of SAPF system. The reference currents are aligned to the phase angle of the power mains voltage vector which is obtained by using a dq phase locked loop (PLL) system. The current control strategy is implemented by an adaptive pole placement control strategy integrated to a variable structure control scheme (VS-APPC). In the VS-APPC, the internal model principle (IMP) of reference currents is used for achieving the zero steady state tracking error of the power system currents. This forces the phase current of the system mains to be sinusoidal with low harmonics content. Moreover, the current controllers are implemented on the stationary reference frame to avoid transformations to the mains voltage vector reference coordinates. This proposed current control strategy enhance the performance of SAPF with fast transient response and robustness to parametric uncertainties. Experimental results are showing for determining the effectiveness of SAPF proposed control system

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On this paper, it is made a comparative analysis among a controller fuzzy coupled to a PID neural adjusted by an AGwith several traditional control techniques, all of them applied in a system of tanks (I model of 2nd order non lineal). With the objective of making possible the techniques involved in the comparative analysis and to validate the control to be compared, simulations were accomplished of some control techniques (conventional PID adjusted by GA, Neural PID (PIDN) adjusted by GA, Fuzzy PI, two Fuzzy attached to a PID Neural adjusted by GA and Fuzzy MISO (3 inputs) attached to a PIDN adjusted by GA) to have some comparative effects with the considered controller. After doing, all the tests, some control structures were elected from all the tested techniques on the simulating stage (conventional PID adjusted by GA, Fuzzy PI, two Fuzzy attached to a PIDN adjusted by GA and Fuzzy MISO (3 inputs) attached to a PIDN adjusted by GA), to be implemented at the real system of tanks. These two kinds of operation, both the simulated and the real, were very important to achieve a solid basement in order to establish the comparisons and the possible validations show by the results

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In this work, the Markov chain will be the tool used in the modeling and analysis of convergence of the genetic algorithm, both the standard version as for the other versions that allows the genetic algorithm. In addition, we intend to compare the performance of the standard version with the fuzzy version, believing that this version gives the genetic algorithm a great ability to find a global optimum, own the global optimization algorithms. The choice of this algorithm is due to the fact that it has become, over the past thirty yares, one of the more importan tool used to find a solution of de optimization problem. This choice is due to its effectiveness in finding a good quality solution to the problem, considering that the knowledge of a good quality solution becomes acceptable given that there may not be another algorithm able to get the optimal solution for many of these problems. However, this algorithm can be set, taking into account, that it is not only dependent on how the problem is represented as but also some of the operators are defined, to the standard version of this, when the parameters are kept fixed, to their versions with variables parameters. Therefore to achieve good performance with the aforementioned algorithm is necessary that it has an adequate criterion in the choice of its parameters, especially the rate of mutation and crossover rate or even the size of the population. It is important to remember that those implementations in which parameters are kept fixed throughout the execution, the modeling algorithm by Markov chain results in a homogeneous chain and when it allows the variation of parameters during the execution, the Markov chain that models becomes be non - homogeneous. Therefore, in an attempt to improve the algorithm performance, few studies have tried to make the setting of the parameters through strategies that capture the intrinsic characteristics of the problem. These characteristics are extracted from the present state of execution, in order to identify and preserve a pattern related to a solution of good quality and at the same time that standard discarding of low quality. Strategies for feature extraction can either use precise techniques as fuzzy techniques, in the latter case being made through a fuzzy controller. A Markov chain is used for modeling and convergence analysis of the algorithm, both in its standard version as for the other. In order to evaluate the performance of a non-homogeneous algorithm tests will be applied to compare the standard fuzzy algorithm with the genetic algorithm, and the rate of change adjusted by a fuzzy controller. To do so, pick up optimization problems whose number of solutions varies exponentially with the number of variables

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Conventional control strategies used in shunt active power filters (SAPF) employs real-time instantaneous harmonic detection schemes which is usually implements with digital filters. This increase the number of current sensors on the filter structure which results in high costs. Furthermore, these detection schemes introduce time delays which can deteriorate the harmonic compensation performance. Differently from the conventional control schemes, this paper proposes a non-standard control strategy which indirectly regulates the phase currents of the power mains. The reference currents of system are generated by the dc-link voltage controller and is based on the active power balance of SAPF system. The reference currents are aligned to the phase angle of the power mains voltage vector which is obtained by using a dq phase locked loop (PLL) system. The current control strategy is implemented by an adaptive pole placement control strategy integrated to a variable structure control scheme (VS¡APPC). In the VS¡APPC, the internal model principle (IMP) of reference currents is used for achieving the zero steady state tracking error of the power system currents. This forces the phase current of the system mains to be sinusoidal with low harmonics content. Moreover, the current controllers are implemented on the stationary reference frame to avoid transformations to the mains voltage vector reference coordinates. This proposed current control strategy enhance the performance of SAPF with fast transient response and robustness to parametric uncertainties. Experimental results are showing for determining the effectiveness of SAPF proposed control system

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Foundation Fieldbus Industrial networks are the high standard technology which allows users to create complex control logic and totally decentralized. Although being so advanced, they still have some limitations imposed by their own technology. Attempting to solve one of these limitations, this paper describes how to design a Fuzzy controller in a Foundation Fieldbus network using their basic elements of programming, the functional blocks, so that the network remains fully independent of other devices other than the same instruments that constitute it. Moreover, in this work was developed a tool that aids this process of building the Fuzzy controller, setting the internal parameters of functional blocks and informing how many and which blocks should be used for a given structure. The biggest challenge in creating this controller is exactly the choice of blocks and how to arrange them in order to effectuate the same functions of a Fuzzy controller implemented in other kind of environment. The methodology adopted was to divide each one of the phases of a traditional Fuzzy controller and then create simple structures with the functional blocks to implement them. At the end of the work, the developed controller is compared with a Fuzzy controller implemented in a mathematical program that it has a proper tool for the development and implementation of Fuzzy controllers, obtaining comparatives graphics of performance between both

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Fuzzy intelligent systems are present in a variety of equipment ranging from household appliances to Fuzzy intelligent systems are present in a variety of equipment ranging from household appliances to small devices such as digital cameras and cell phones being used primarily for dealing with the uncertainties in the modeling of real systems. However, commercial implementations of Fuzzy systems are not general purpose and do not have portability to different hardware platforms. Thinking about these issues this work presents the implementation of an open source development environment that consists of a desktop system capable of generate Graphically a general purpose Fuzzy controller and export these parameters for an embedded system with a Fuzzy controller written in Java Platform Micro Edition To (J2ME), whose modular design makes it portable to any mobile device that supports J2ME. Thus, the proposed development platform is capable of generating all the parameters of a Fuzzy controller and export it in XML file, and the code responsible for the control logic that is embedded in the mobile device is able to read this file and start the controller. All the parameters of a Fuzzy controller are configurable using the desktop system, since the membership functions and rule base, even the universe of discourse of the linguistic terms of output variables. This system generates Fuzzy controllers for the interpolation model of Takagi-Sugeno. As the validation process and testing of the proposed solution the Fuzzy controller was embedded on the mobile device Sun SPOT ® and used to control a plant-level Quanser®, and to compare the Fuzzy controller generated by the system with other types of controllers was implemented and embedded in sun spot a PID controller to control the same level plant of Quanser®

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Every day, water scarcity becomes a more serious problem and, directly affects global society. Studies are directed in order to raise awareness of the rational use of this natural asset that is essential to our survival. Only 0.007% of the water available in the world have easy access and can be consumed by humans, it can be found in rivers, lakes, etc... To better take advantage of the water used in homes and small businesses, reuse projects are often implemented, resulting in savings for customers of water utilities. The reuse projects involve several areas of engineering, like Environmental, Chemical, Electrical and Computer Engineering. The last two are responsible for the control of the process, which aims to make gray water (soapy water), and clear blue water (rain water), ideal for consumption, or for use in watering gardens, flushing, among others applications. Water has several features that should be taken into consideration when it comes to working its reuse. Some of the features are, turbidity, temperature, electrical conductivity and, pH. In this document there is a proposal to control the pH (potential Hydrogen) through a microcontroller, using the fuzzy logic as strategy of control. The controller was developed in the fuzzy toolbox of Matlab®

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The main objective of work is to show procedures to implement intelligent control strategies. This strategies are based on fuzzy scheduling of PID controllers, by using only standard function blocks of this technology. Then, the standardization of Foundation Fieldbus is kept. It was developed an environment to do the necessary tests, it validates the propose. This environment is hybrid, it has a real module (the fieldbus) and a simulated module (the process), although the control signals and measurement are real. Then, it is possible to develop controllers projects. In this work, a fuzzy supervisor was developed to schedule a network of PID controller for a non-linear plant. Analyzing its performance results to the control and regulation problem

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Traditional irrigation projects do not locally determine the water availability in the soil. Then, irregular irrigation cycles may occur: some with insufficient amount that leads to water deficit, other with excessive watering that causes lack of oxygen in plants. Due to the nonlinear nature of this problem and the multivariable context of irrigation processes, fuzzy logic is suggested to replace commercial ON-OFF irrigation system with predefined timing. Other limitation of commercial solutions is that irrigation processes either consider the different watering needs throughout plant growth cycles or the climate changes. In order to fulfill location based agricultural needs, it is indicated to monitor environmental data using wireless sensors connected to an intelligent control system. This is more evident in applications as precision agriculture. This work presents the theoretical and experimental development of a fuzzy system to implement a spatially differentiated control of an irrigation system, based on soil moisture measurement with wireless sensor nodes. The control system architecture is modular: a fuzzy supervisor determines the soil moisture set point of each sensor node area (according to the soil-plant set) and another fuzzy system, embedded in the sensor node, does the local control and actuates in the irrigation system. The fuzzy control system was simulated with SIMULINK® programming tool and was experimentally built embedded in mobile device SunSPOTTM operating in ZigBee. Controller models were designed and evaluated in different combinations of input variables and inference rules base

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In this work, we propose a two-stage algorithm for real-time fault detection and identification of industrial plants. Our proposal is based on the analysis of selected features using recursive density estimation and a new evolving classifier algorithm. More specifically, the proposed approach for the detection stage is based on the concept of density in the data space, which is not the same as probability density function, but is a very useful measure for abnormality/outliers detection. This density can be expressed by a Cauchy function and can be calculated recursively, which makes it memory and computational power efficient and, therefore, suitable for on-line applications. The identification/diagnosis stage is based on a self-developing (evolving) fuzzy rule-based classifier system proposed in this work, called AutoClass. An important property of AutoClass is that it can start learning from scratch". Not only do the fuzzy rules not need to be prespecified, but neither do the number of classes for AutoClass (the number may grow, with new class labels being added by the on-line learning process), in a fully unsupervised manner. In the event that an initial rule base exists, AutoClass can evolve/develop it further based on the newly arrived faulty state data. In order to validate our proposal, we present experimental results from a level control didactic process, where control and error signals are used as features for the fault detection and identification systems, but the approach is generic and the number of features can be significant due to the computationally lean methodology, since covariance or more complex calculations, as well as storage of old data, are not required. The obtained results are significantly better than the traditional approaches used for comparison