The thesis considers the problems of designing a dynamic output feedback controller to discrete time systems with polytopic uncertainty so that the closed-loop systems are DR stable with their transfer matrices having H2 norm and H¡Û norm bounded by a prescribed value ru. The formar part of the thesis provides less conservative LMI conditions for H2 and H¡Û analysis and the output feedback control of discrete system than those appeared in the current research. While the latter part of the thesis extend the current research to DR stable with H2 and H¡Û design. Finally, numerical examples are illustrated to show improvement of the propered result.

Made available in DSpace on 2014-12-17T14:55:47Z (GMT). No. of bitstreams: 1 JoseSBL_DISSERT.pdf: 1769944 bytes, checksum: 43863b3b32771c922314a0fa73be8bf8 (MD5) Previous issue date: 2011-02-14 / 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 / Este trabalho tem como objetivo desenvolver uma estrat?gia de controle on-line baseado no Controlador Preditivo Robusto (RMPC, acr?nimo do ingl?s Robust Model Predictive Control) aplicado a um sistema real de tanques acoplados. Este processo consiste em sistema de dois tanques conectados, cujo liquido ? enviado aos mesmos por uma bomba. O objetivo do controle (problema regulat?rio) ? deixar os n?veis dos tanques no ponto de opera??o considerado, mesmo na presen?a de perturba??es. A s?ntese da t?cnica RMPC consiste em incorporar de forma explicita as incertezas da planta na formula??o do problema. O objetivo do projeto, a cada per?odo de amostragem, ? encontrar uma realimenta??o de estados que minimiza o pior caso de uma fun??o objetivo com horizonte infinito, sujeita a restri??es no sinal de controle. O problema original, do tipo Min-max, ? reduzido em a problema de otimiza??o convexa expresso em desigualdades matriciais lineares (LMI, Linear Matriz Inequalities). Mostram-se, neste trabalho, a descri??o da incerteza da planta na forma polit?pica e as condi??es de factibilidade do problema de otimiza??o. A implementa??o do algoritmo RMPC foi feita utilizando o software Scilab e a sua comunica??o com o sistema de tanques acoplados foi feita atrav?s do protocolo OPC (do ingl?s OLE for Process Control)