Krishan M. Nagpal

A time-domain approach to model validation

In this paper we offer a novel approach to control-oriented model validation problems. The problem is to decide whether a postulated nominal model with bounded uncertainty is consistent with measured input-output data. Our approach directly uses time-domain input-output data to validate uncertainty models. The algorithms we develop are computationally tractable and reduce to (generally nondifferentiable) convex feasibility problems or to linear programming problems. In special cases, we give analytical solutions to these problems. >

H/sub infinity / control and filtering for sampled-data systems

H/sub infinity / control and filtering problems for sampled-data systems are studied. Necessary and sufficient conditions are obtained for the existence of controllers and filters that satisfy a specified H/sub infinity / performance bound. When these conditions hold, explicit formulas for a controller and a filter satisfying the H/sub infinity / performance bound are also given. >

H ∞ control of linear time-varying systems: a state-space approach

In this paper the standard problem of

H ∞ control with transients

H^\infty

A Time-Domain Approach to Model Validation

control theory for finite-dimensional linear time-varying continuous-time plants is considered. The problem is: given a real number

Robust regulation with H 2 performance

\gamma > 0

Does robust regulation compromise H/sub 2/ performance?

, find (if one exists) an internally stabilizing controller such that the closed-loop operator norm is less than

The H/sup infinity / control problem for linear time-varying systems

\gamma

Digital control systems: H/sub infinity / controller design with a zero-order hold function

. Under rather weak assumptions on the plant model, it is shown that a solution to this problem exists if and only if a pair of matrix Riccati differential equations admits positive semidefinite stabilizing solutions. State-space formulae for one solution to the problem are also given.

Robust performance of discrete-time linear systems under partial statistical information for exogenous signals

In