Controle preditivo não linear baseado no modelo de Hammerstein com prova de estabilidade

The Predictive Controller has been receiving plenty attention in the last decades, because the need to understand, to analyze, to predict and to control real systems has been quickly growing with the technological and industrial progress. The objective of this thesis is to present a contribution for the development and implementation of Nonlinear Predictive Controllers based on Hammerstein model, as well as to its make properties evaluation. In this case, in the Nonlinear Predictive Controller development the time-step linearization method is used and a compensation term is introduced in order to improve the controller performance. The main motivation of this thesis is the study and stability guarantee for the Nonlinear Predictive Controller based on Hammerstein model. In this case, was used the concepts of sections and Popov Theorem. Simulation results with literature models shows that the proposed approaches are able to control with good performance and to guarantee the systems stability

Contribuições à análise de robustez de sistemas de controle usando redes neurais

This work develops a robustness analysis with respect to the modeling errors, being applied to the strategies of indirect control using Artificial Neural Networks - ANN s, belong to the multilayer feedforward perceptron class with on-line training based on gradient method (backpropagation). The presented schemes are called Indirect Hybrid Control and Indirect Neural Control. They are presented two Robustness Theorems, being one for each proposed indirect control scheme, which allow the computation of the maximum steady-state control error that will occur due to the modeling error what is caused by the neural identifier, either for the closed loop configuration having a conventional controller - Indirect Hybrid Control, or for the closed loop configuration having a neural controller - Indirect Neural Control. Considering that the robustness analysis is restrict only to the steady-state plant behavior, this work also includes a stability analysis transcription that is suitable for multilayer perceptron class of ANN s trained with backpropagation algorithm, to assure the convergence and stability of the used neural systems. By other side, the boundness of the initial transient behavior is assured by the assumption that the plant is BIBO (Bounded Input, Bounded Output) stable. The Robustness Theorems were tested on the proposed indirect control strategies, while applied to regulation control of simulated examples using nonlinear plants, and its results are presented

Identificação de uma planta de corrente de um motor de indução utilizando redes de base radial

The present work describes the use of a mathematical tool to solve problems arising from control theory, including the identification, analysis of the phase portrait and stability, as well as the temporal evolution of the plant s current induction motor. The system identification is an area of mathematical modeling that has as its objective the study of techniques which can determine a dynamic model in representing a real system. The tool used in the identification and analysis of nonlinear dynamical system is the Radial Basis Function (RBF). The process or plant that is used has a mathematical model unknown, but belongs to a particular class that contains an internal dynamics that can be modeled.Will be presented as contributions to the analysis of asymptotic stability of the RBF. The identification using radial basis function is demonstrated through computer simulations from a real data set obtained from the plant

Nanotechnological delivery systems for the oral administration of active molecules: Polymeric microparticles and microemulsions applied to anti-inflammatory and anti-infectious drugs

This thesis was devoted to the development of innovative oral delivery systems for two different molecules. In the first part, microparticles (MPs) based on xylan and Eudragit® S- 100 were produced and used to encapsulate 5-aminosalicylic acid for colon delivery. Xylan was extracted from corn cobs and characterized in terms of its physicochemical, rheological and toxicological properties. The polymeric MPs were prepared by interfacial cross-linking polymerization and spray-drying and characterized for their morphology, mean size and distribution, thermal stability, crystallinity, entrapment efficiency and in vitro drug release. MPs with suitable physical characteristics and satisfactory yields were prepared by both methods, although the spray-dried systems showed higher thermal stability. In general, spraydried MPs would be preferable systems due to their thermal stability and absence of toxic agents used in their preparation. However, drug loading and release need to be optimized. In the second part of this thesis, oil-in-water microemulsions (O/W MEs) based on mediumchain triglycerides were formulated as drug carriers and solubility enhancers for amphotericin B (AmB). Phase diagrams were constructed using surfactant blends with hydrophiliclipophilic balance values between 9.7 and 14.4. The drug-free and drug-loaded MEs presented spherical non-aggregated droplets around 80 and 120 nm, respectively, and a low polydispersity index. The incorporation of AmB was high and depended on the volume fraction of the disperse phase. These MEs did not reduce the viability of J774.A1 macrophage-like cells for concentrations up to 25 μg/mL of AmB. Therefore, O/W MEs based on propylene glycol esters of caprylic acid may be considered as suitable delivery systems for AmB