J. A. M. Felippe de Souza

Exact linearization and fuzzy logic applied to the control of a Magnetic Levitation System

In recent years the area of nonlinear control systems has been the subject of many studies. Computational developments have enabled more complex applications to provide solutions to nonlinear problems. The purpose of this paper is to use a combination of two techniques to control a nonlinear system: the Magnetic Levitation System. First, the exact linearization technique with state feedback is applied to obtain a linear system. Second, the linearization is made via direct cancellation of nonlinear functions, which represent the phenomenological model of the system. Finally, to deal with the presence of uncertainty in the system model, an adaptive controller is used. The controller is based on fuzzy logic to estimate the functions that contain the nonlinearities of the system. The fuzzy system is a zero-order Takagi-Sugeno-Kang structure and the adaptive controller is implemented in a simulated environment (Matlab Simulink ©). The methodology guarantees the convergence of the estimates to their optimal values, and in turn the overall stability of the system. The results show the controller output signal tracks a reference input signal. For future work this adaptive controller should be implemented in a real physical system.

An architecture of sensor fusion for spatial location of objects in mobile robotics

Each part of a mobile robot has particular aspects of its own, which must be integrated in order to successfully conclude a specific task. Among these parts, sensing enables to construct a representation of landmarks of the surroundings with the goal of supplying relevant information for the robot’s navigation. The present work describes the architecture of a perception system based on data fusion from a CMOS camera and distance sensors. The aim of the proposed architecture is the spatial location of objects on a soccer field. An SVM is used for both recognition and object location and the process of fusion is made by means of a fuzzy system, using a TSK model.

Optimal medication in HIV seropositive patient treatment using fuzzy cost function

This work concerns the optimization of medication doses using fuzzy relations to represent the compromise between the side-effects and therapeutic effects in the clinical treatment of HIV (human immunodeficiency virus) seropositive patients. The optimization process is carried out using a mathematical model which relates immunological variables such as the number of CD4+T helper cells (immune cells) and the viral load. The control variables used in the optimization process are the doses of reverse transcriptase inhibitor and protease inhibitor. The cost function uses fuzzy logic rules because clinical states are well expressed by linguistic variables by the medical staff. The results are illustrated with a model fitted to the actual data clinical data of an HIV seropositive patient.

Analysis of a sub-optimal scheme of drug dosage in the aids treatment

Here the results for CD4T cells count and the viral load obtained from HIV sero-positive patients are compared with results from numerical simulations by computer. Also, the standard scheme of administration of drugs anti HIV (HAART schemes) which uses constant doses is compared with an alternative sub-optimal treatment scheme which uses variable drug dosage according to the evolution of a quantitative measure of the side effects. The quantitative analysis done here shows that it is possible to obtain, using the alternative scheme, the same performance of actual data but using variable dosage and having fewer side effects. Optimal control theory is used to solve and also to provide a prognosis related to the strategies for control of viraemia.

Optimization of the therapy of viral infections using fuzzy techniques

This work concerns the optimization of treatment schemes for viral infections using drugs that stimulate the humoral immune response. The dynamics of the disease is based on Marchuks formulation of the behavior of immune systems. The model is extended to include the effect of treatment and to allow a representation of the pathological consequences of the infection and the side effects of the drugs on the patient. The performance index uses the concept of fuzzy sets to model non-exact subjective quantities such as patient well-being, that should be traded off against quantities that can be objectively measured, such as cost of medication. The admissible controls are assumed to belong to the class of piecewise constant functions. A numerical example using influenza dynamics show a considerable improvement of the optimized drug administration policy, when compared to non-optimized policies, in terms of reduction in the achieved performance index.

Therapy Optimization for Viral Infections using Fuzzy Performance Index

Abstract This Work concerns the application of optimization theory in the planning of pharmacological treatment of viral infections. The dynamics of the disease is modeled using Marchuks formulation the model being extended to include the effect of treatment and to allow representation of the pathological consequences of the infection on the patient The proposed performance index is based on fuzzy sets to reflect a compromise between non-exaet subjective quantities such as patient well being as compared with quantities such as cost of medication that can be objectively measured The admissible controls are assumed to belong to the class of piecewise constant functions. The numerical results show a considerable improvement of the optimized drug administration policy, when compared to non-optimized policies, in terms of reduction in the achieved performance index.

Model Reference Adaptive Control Applied to the Improvement of the Operational Conditions of a Sucker Rod Pump System

Abstract Several studies have shown that the satisfactory operation of oil wells with sucker rod pumps is attributed to the techniques and methods able to control the performance of the well. The Polytechnic School of Federal University of Bahia, through the Artificial Lift Lab, has nowadays a reduced plant of a sucker rod pump with an artificial well of 32m height fully instrumented, with full access and visible. Among some studies already performed with this sucker rod pump, there is the use of a dynamic model to control the fluid level in annular well. The presence of uncertainties is related to the fluid characteristics in the well, associated with either the electrical or the mechanical assembly, may jeopardize the desired performance of the PID controller. These uncertainties usually do not contribute to good results and may put in risk the wells productivity. The aim of this paper is to show a model reference adaptive controller to deal with some uncertainties in the system model to control the level of the annular well. The results show that the adaptive controller is able to deal satisfactorily with changes, such as: in the pumped fluid composition (parts of water, oil and gas), which is a very common situation in real production fields.

A NEW MATHEMATICAL INDEX FOR THE OPTIMAL CONTROL OF DRUG DOSES

Abstract A new quantitative index is proposed to assign a numerical value that reflects the compromise between the therapeutic and side effects during treatment of AIDS using drugs that are active against HIV. The model considers that the clinical conditions are related to the values of CD4 + T cell counts and viral load. The therapeutic effect is associated with increase in the CD4 + T cell counts, while the side effects are assumed related to the drug doses. The proposed index might be useful in computer programs for numerical evaluation and optimization of different types of drug administration schemes.