Controle feedback de nível baseado em sensor de imagem aplicado ao equipamento misturador-decantador à inversão de fases (MDIF®)

The treatment of wastewaters contaminated with oil is of great practical interest and it is fundamental in environmental issues. A relevant process, which has been studied on continuous treatment of contaminated water with oil, is the equipment denominated MDIF® (a mixer-settler based on phase inversion). An important variable during the operation of MDIF® is the water-solvent interface level in the separation section. The control of this level is essential both to avoid the dragging of the solvent during the water removal and improve the extraction efficiency of the oil by the solvent. The measurement of oil-water interface level (in line) is still a hard task. There are few sensors able to measure oil-water interface level in a reliable way. In the case of lab scale systems, there are no interface sensors with compatible dimensions. The objective of this work was to implement a level control system to the organic solvent/water interface level on the equipment MDIF®. The detection of the interface level is based on the acquisition and treatment of images obtained dynamically through a standard camera (webcam). The control strategy was developed to operate in feedback mode, where the level measure obtained by image detection is compared to the desired level and an action is taken on a control valve according to an implemented PID law. A control and data acquisition program was developed in Fortran to accomplish the following tasks: image acquisition; water-solvent interface identification; to perform decisions and send control signals; and to record data in files. Some experimental runs in open-loop were carried out using the MDIF® and random pulse disturbances were applied on the input variable (water outlet flow). The responses of interface level permitted the process identification by transfer models. From these models, the parameters for a PID controller were tuned by direct synthesis and tests in closed-loop were performed. Preliminary results for the feedback loop demonstrated that the sensor and the control strategy developed in this work were suitable for the control of organic solvent-water interface level

Controle preditivo robusto baseado em desigualdades matriciais lineares aplicado a um sistema de tanques acoplados

This work deals with an on-line control strategy based on Robust Model Predictive Control (RMPC) technique applied in a real coupled tanks system. This process consists of two coupled tanks and a pump to feed the liquid to the system. The control objective (regulator problem) is to keep the tanks levels in the considered operation point even in the presence of disturbance. The RMPC is a technique that allows explicit incorporation of the plant uncertainty in the problem formulation. The goal is to design, at each time step, a state-feedback control law that minimizes a 'worst-case' infinite horizon objective function, subject to constraint in the control. The existence of a feedback control law satisfying the input constraints is reduced to a convex optimization over linear matrix inequalities (LMIs) problem. It is shown in this work that for the plant uncertainty described by the polytope, the feasible receding horizon state feedback control design is robustly stabilizing. The software implementation of the RMPC is made using Scilab, and its communication with Coupled Tanks Systems is done through the OLE for Process Control (OPC) industrial protocol

Uma proposta de um controlador adaptativo por posicionamento de pólos e estrutura variável

There are two main approaches for using in adaptive controllers. One is the so-called model reference adaptive control (MRAC), and the other is the so-called adaptive pole placement control (APPC). In MRAC, a reference model is chosen to generate the desired trajectory that the plant output has to follow, and it can require cancellation of the plant zeros. Due to its flexibility in choosing the controller design methodology (state feedback, compensator design, linear quadratic, etc.) and the adaptive law (least squares, gradient, etc.), the APPC is the most general type of adaptive control. Traditionally, it has been developed in an indirect approach and, as an advantage, it may be applied to non-minimum phase plants, because do not involve plant zero-pole cancellations. The integration to variable structure systems allows to aggregate fast transient and robustness to parametric uncertainties and disturbances, as well. In this work, a variable structure adaptive pole placement control (VS-APPC) is proposed. Therefore, new switching laws are proposed, instead of using the traditional integral adaptive laws. Additionally, simulation results for an unstable first order system and simulation and practical results for a three-phase induction motor are shown

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

Interação em sala de aula de língua inglesa: o feedback como estratégia do desempenho assistido

This dissertation aims at characterizing the practices as well as the effects of a teacher s feedback in oral conversation interaction with students in an English Language classroom at a Primary School, 6th Grade in Açu/RN, Brazil. Therefore, this study is based on Vygotsky s (1975) and Bruner´s (1976) researches, which state that the learning process is constructed through interaction between a more experienced individual (teacher, parents and friends) and a learner who plays an active role, a re-constructor of knowledge. It is also based on Ur´s (2006) and Brookhart s (2008) studies (among other authors in Applied Linguistic) who defend that the feedback process needs to be evaluative and formative since it sets interfaces with both students autonomy and learning improvement. Our study is based on qualitative, quantitative and interpretive researches, whose natural environment (the classroom) is a direct source of data generated in this research through field observations/note-taking as well as through the transcriptions of five English classes audio taped. This study shows the following results: the teacher still seems to accept the patterns of interaction in the classroom that correspond to the IRE process (Initiation, Response, Evaluation) in behaviorist patterns: (1) he speaks and determines the turns of speech; (2) the teacher asks more questions and directs the activities most of the time; (3) the teacher´s feedback presents the following types: questioning, modeling, repeated response, praise, depreciation, positive/negative and sarcasm feedback, whose functions are to assess students' performance based on the rightness and wrongness of their responses. Thus, this implies to state that the feedback does not seem to help students improvement in terms of acquiring knowledge because of its normative effects/roles. Therefore, it is the teacher´s role to give evaluative and formative feedback to a student so that he/she should advance in the learning of the language and in the construction of knowledge

A influência do feedback verbal na cooperação em crianças

Human cooperation is a hallmark of this species due to its wide extension to genetically unrelated individuals and complex division of labor. It is considered an evolutionary puzzle, because the theory of evolution by natural selection predicts that self-interested individuals tend to be selected. Different theories have been proposed to explain the evolution of cooperation, which the most important are kin selection and reciprocal altruism. Considering the evolutionary continuity between species, humans and other primates have several common traits that help to promote cooperation between individuals of these species. Two features, however, seem to be particularly humans: inequality aversion and preferences in relation to others. Although human cooperation is not necessarily related to morality, cooperative traits are the basis for moral tendencies. The development of human morality is a combination of early prosocial tendencies, cooperative skills displayed at different ages, social learning and cultural transmission of norms. The social stimulus seems to be particularly important in promoting cooperative behavior in children and adults. In order to study the influence of social stimuli, as verbal feedback, on children cooperation, a study was conducted with children in a public goods game. 407 children from public schools in Natal / RN, divided into 21 groups, between six and nine years, participated in eight rounds of this game. After each round, seven groups received praise for larger donations, seven groups have been criticized by smaller donations, and the other seven received no comment. Children cooperated more when criticized, without significant differences between sexes, although young children have cooperated more negative than older children. The results are likely related to the anticipation and avoidance of punishment associated with the feedback (although this did not occur), and greater sensitivity to the authority in younger children. Nevertheless, the cooperation decreased in all groups until the last day of play. The results suggest an early sensitivity to moral punishment, whose role in the maintenance of social relations must have been important in the evolution of cooperation in humans

Implementação de uma matriz de neurônios dinamicamente reconfigurável para descrição de topologias de redes neurais artificiais multilayer perceptrons

The Artificial Neural Networks (ANN), which is one of the branches of Artificial Intelligence (AI), are being employed as a solution to many complex problems existing in several areas. To solve these problems, it is essential that its implementation is done in hardware. Among the strategies to be adopted and met during the design phase and implementation of RNAs in hardware, connections between neurons are the ones that need more attention. Recently, are RNAs implemented both in application specific integrated circuits's (Application Specific Integrated Circuits - ASIC) and in integrated circuits configured by the user, like the Field Programmable Gate Array (FPGA), which have the ability to be partially rewritten, at runtime, forming thus a system Partially Reconfigurable (SPR), the use of which provides several advantages, such as flexibility in implementation and cost reduction. It has been noted a considerable increase in the use of FPGAs for implementing ANNs. Given the above, it is proposed to implement an array of reconfigurable neurons for topologies Description of artificial neural network multilayer perceptrons (MLPs) in FPGA, in order to encourage feedback and reuse of neural processors (perceptrons) used in the same area of the circuit. It is further proposed, a communication network capable of performing the reuse of artificial neurons. The architecture of the proposed system will configure various topologies MLPs networks through partial reconfiguration of the FPGA. To allow this flexibility RNAs settings, a set of digital components (datapath), and a controller were developed to execute instructions that define each topology for MLP neural network.

Implementação de uma matriz de neurônios dinamicamente reconfigurável para descrição de topologias de redes neurais artificiais multilayer perceptrons

The Artificial Neural Networks (ANN), which is one of the branches of Artificial Intelligence (AI), are being employed as a solution to many complex problems existing in several areas. To solve these problems, it is essential that its implementation is done in hardware. Among the strategies to be adopted and met during the design phase and implementation of RNAs in hardware, connections between neurons are the ones that need more attention. Recently, are RNAs implemented both in application specific integrated circuits's (Application Specific Integrated Circuits - ASIC) and in integrated circuits configured by the user, like the Field Programmable Gate Array (FPGA), which have the ability to be partially rewritten, at runtime, forming thus a system Partially Reconfigurable (SPR), the use of which provides several advantages, such as flexibility in implementation and cost reduction. It has been noted a considerable increase in the use of FPGAs for implementing ANNs. Given the above, it is proposed to implement an array of reconfigurable neurons for topologies Description of artificial neural network multilayer perceptrons (MLPs) in FPGA, in order to encourage feedback and reuse of neural processors (perceptrons) used in the same area of the circuit. It is further proposed, a communication network capable of performing the reuse of artificial neurons. The architecture of the proposed system will configure various topologies MLPs networks through partial reconfiguration of the FPGA. To allow this flexibility RNAs settings, a set of digital components (datapath), and a controller were developed to execute instructions that define each topology for MLP neural network.