Utilização da lógica difusa no controle inteligente de sistemas eletro-hidráulicos

Electro-hydraulic servo-systems are widely employed in industrial applications such as robotic manipulators, active suspensions, precision machine tools and aerospace systems. They provide many advantages over electric motors, including high force to weight ratio, fast response time and compact size. However, precise control of electro-hydraulic systems, due to their inherent nonlinear characteristics, cannot be easily obtained with conventional linear controllers. Most flow control valves can also exhibit some hard nonlinearities such as deadzone due to valve spool overlap on the passage´s orifice of the fluid. This work describes the development of a nonlinear controller based on the feedback linearization method and including a fuzzy compensation scheme for an electro-hydraulic actuated system with unknown dead-band. Numerical results are presented in order to demonstrate the control system performance

Controle inteligente de sistemas eletroidráulicos utilizando redes neurais artificiais

This work describes the development of a nonlinear control strategy for an electro-hydraulic actuated system. The system to be controlled is represented by a third order ordinary differential equation subject to a dead-zone input. The control strategy is based on a nonlinear control scheme, combined with an artificial intelligence algorithm, namely, the method of feedback linearization and an artificial neural network. It is shown that, when such a hard nonlinearity and modeling inaccuracies are considered, the nonlinear technique alone is not enough to ensure a good performance of the controller. Therefore, a compensation strategy based on artificial neural networks, which have been notoriously used in systems that require the simulation of the process of human inference, is used. The multilayer perceptron network and the radial basis functions network as well are adopted and mathematically implemented within the control law. On this basis, the compensation ability considering both networks is compared. Furthermore, the application of new intelligent control strategies for nonlinear and uncertain mechanical systems are proposed, showing that the combination of a nonlinear control methodology and artificial neural networks improves the overall control system performance. Numerical results are presented to demonstrate the efficacy of the proposed control system

Otimização do sistema de supervisão e controle de uma planta de tratamento de resíduos utilizando plasma

A major and growing problems faced by modern society is the high production of waste and related effects they produce, such as environmental degradation and pollution of various ecosystems, with direct effects on quality of life. The thermal treatment technologies have been widely used in the treatment of these wastes and thermal plasma is gaining importance in processing blanketing. This work is focused on developing an optimized system of supervision and control applied to a processing plant and petrochemical waste effluents using thermal plasma. The system is basically composed of a inductive plasma torch reactors washing system / exhaust gases and RF power used to generate plasma. The process of supervision and control of the plant is of paramount importance in the development of the ultimate goal. For this reason, various subsidies were created in the search for greater efficiency in the process, generating events, graphics / distribution and storage of data for each subsystem of the plant, process execution, control and 3D visualization of each subsystem of the plant between others. A communication platform between the virtual 3D plant architecture and a real control structure (hardware) was created. The goal is to use the concepts of mixed reality and develop strategies for different types of controls that allow manipulating 3D plant without restrictions and schedules, optimize the actual process. Studies have shown that one of the best ways to implement the control of generation inductively coupled plasma techniques is to use intelligent control, both for their efficiency in the results is low for its implementation, without requiring a specific model. The control strategy using Fuzzy Logic (Fuzzy-PI) was developed and implemented, and the results showed satisfactory condition on response time and viability

Processamento Inteligente de Sinais de Pressão e Temperatura Adquiridos Através de Sensores Permanentes em Poços de Petróleo

Originally aimed at operational objectives, the continuous measurement of well bottomhole pressure and temperature, recorded by permanent downhole gauges (PDG), finds vast applicability in reservoir management. It contributes for the monitoring of well performance and makes it possible to estimate reservoir parameters on the long term. However, notwithstanding its unquestionable value, data from PDG is characterized by a large noise content. Moreover, the presence of outliers within valid signal measurements seems to be a major problem as well. In this work, the initial treatment of PDG signals is addressed, based on curve smoothing, self-organizing maps and the discrete wavelet transform. Additionally, a system based on the coupling of fuzzy clustering with feed-forward neural networks is proposed for transient detection. The obtained results were considered quite satisfactory for offshore wells and matched real requisites for utilization