Control Systems: New Approaches to Analysis and Design

This dissertation deals with two open problems in control theory. The first problem concerns the synthesis of fixed structure controllers for Linear Time Invariant (LTI) systems. The problem of synthesizing fixed structure/order controllers has practical importance when simplicity, hardware limitations, or reliability in the implementation of a controller dictates a low order of stabilization. A new method is proposed to simplify the calculation of the set of fixed structure stabilizing controllers for any given plant. The method makes use of computational algebraic geometry techniques and sign-definite decomposition method. Although designing a stabilizing controller of a fixed structure is important, in many practical applications it is also desirable to control the transient response of the closed loop system. This dissertation proposes a novel approach to approximate the set of stabilizing Proportional-Integral-Derivative (PID) controllers guaranteeing transient response specifications. Such desirable set of PID controllers can be constructed upon an application of Widder's theorem and Markov-Lukacs representation of non-negative polynomials. The second problem explored in this dissertation handles the design and control of linear systems without requiring the knowledge of the mathematical model of the system and directly from a small set of measurements, processed appropriately. The traditional approach to deal with the analysis and control of complex systems has been to describe them mathematically with sets of algebraic or differential equations. The objective of the proposed approach is to determine the design variables directly from a small set of measurements. In particular, it will be shown that the functional dependency of any system variable on any set of system design parameters can be determined by a small number of measurements. Once the functional dependency is obtained, it can be used to extract the values of the design parameters

Dynamical modeling of uncertain interaction-based genomic networks

BACKGROUND: Most dynamical models for genomic networks are built upon two current methodologies, one process-based and the other based on Boolean-type networks. Both are problematic when it comes to experimental design purposes in the laboratory. The first approach requires a comprehensive knowledge of the parameters involved in all biological processes a priori, whereas the results from the second method may not have a biological correspondence and thus cannot be tested in the laboratory. Moreover, the current methods cannot readily utilize existing curated knowledge databases and do not consider uncertainty in the knowledge. Therefore, a new methodology is needed that can generate a dynamical model based on available biological data, assuming uncertainty, while the results from experimental design can be examined in the laboratory. RESULTS: We propose a new methodology for dynamical modeling of genomic networks that can utilize the interaction knowledge provided in public databases. The model assigns discrete states for physical entities, sets priorities among interactions based on information provided in the database, and updates each interaction based on associated node states. Whenever uncertainty in dynamics arises, it explores all possible outcomes. By using the proposed model, biologists can study regulation networks that are too complex for manual analysis. CONCLUSIONS: The proposed approach can be effectively used for constructing dynamical models of interaction-based genomic networks without requiring a complete knowledge of all parameters affecting the network dynamics, and thus based on a small set of available data