This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).
This research aims to develop new fundamental theory and effective design methodologies to address practically important issues that existing control theories cannot effectively handle: specifically the proposal will develop methods for the design of fixed and low order controllers for multivariable systems which satisfy multiple design specifications and which are based on the measured data and not on models. This is a very realistic engineering problem that remains open despite significant progress in computer-aided design.
The objective of feedback control system design is to precisely regulate physical quantities such as position, velocity, temperature, pressure, flow-rate and level in dynamic systems despite significant uncertainty and knowledge of the behavior of the underlying processes. The present proposal approaches the control design problem with a view to overcoming several outstanding challenges. These are a) the need to develop design methods which use measured data directly since mathematical models are unavailable for most systems b) the design of low order or low complexity controllers since the existing methods yield high order controllers which are often fragile or acutely sensitive and therefore unimplementable and c) designs which satisfy multiple user defined specifications. Preliminary results on integral and first order controllers look promising. The research should have a significant impact on control applications ranging from chemical processes, manufacturing systems, disk drives, missile and aircraft control, internet congestion control and biological control systems including genetic networks. In each of these applications models are scarcely available and few effective design techniques exist for the effective design of simple controllers.
