The method of “integrator backstep**” emerged around 1990 as a robust version of
feedback linearization for nonlinear systems with uncertainties. Backstep** was …

For dynamic distributed systems modeled by partial differential equations, existing methods
of sensor location in parameter estimation experiments are either limited to one-dimensional …

This book introduces a comprehensive methodology for adaptive control design of parabolic
partial differential equations with unknown functional parameters, including reaction …

Adaptive control of PDEs is a nonlinear feedback design problem for an infinite-dimensional
system. The author's early work for PDEs with unmatched uncertainties was limited to …

Motivated by recent progress in data assimilation, we develop an algorithm to dynamically
learn the parameters of a chaotic system from partial observations. Under reasonable …

In this paper a model reference-based adaptive parameter estimator for a wide class of
hyperbolic distributed parameter systems is considered. The proposed state and parameter …

This work establishes an abstract framework that considers the distributed filtering of
spatially varying processes using a sensor network. It is assumed that the sensor network …

A model reference adaptive control law is defined for nonlinear distributed parameter
systems. The reference model is assumed to be governed by a strongly coercive linear …

It is well-understood that the choice of experimental conditions for distributed systems has a
significant bearing upon the accuracy achievable in parameter-estimation experiments …

In this work, we address the problem of estimating parameters of diffusion phenomena via
autonomous wireless sensor networks. Diffusion phenomena, such as the propagation of a …