Professor Pearson's research interests are in the area of advanced control systems. Specific focus areas include:

Control of Distributed Delay Dynamical Systems - Systems with distributed delay, like transport lag in chemical plants, are inherently difficult to control owing, in part, to the infinite dimensionality of the system when modeled by differential equations.

Parameter Identification for Continuous-Time Systems - Research is underway on a modulating function technique that converts the stochastic differential equation model to the frequency domain in a way that obviates dealing with unknown initial/end-point conditions for time-limited input/output data.

Professor Pearson's research interests are in the area of advanced control systems. Specific focus areas include - Control of Distributed Delay Dynamical Systems: Systems with distributed delay, like transport lag in chemical plants, are inherently difficult to control owing, in part, to the infinite dimensionality of the system when modeled by differential equations. Research is currently underway on a reducing transformation technique for linear differential distributed-delay systems which facilitates using finite dimensional methods for a variety of control problems. These include feedback stabilization, observers, tracking, approximate lumped parameter models, and optimal feedback control utilizing a separation principle. The basic aim is to devise a functional transformation on the state of the system such that the reduced order model inherits all the unstable and poorly damped modes of the system; Parameter Identification for Continuous-Time Systems: Most system identification techniques use discrete-time models for estimating parameters due to the difficulties of working with continuous-time white noise. Research is underway on a modulating function technique that converts the stochastic differential equation model to the frequency domain in a way that obviates dealing with unknown initial/end-point conditions for time-limited input/output data. In this process, the continuous-time white noise is converted to a discrete-frequency white noise sequence that is easier to deal with in parameter estimation. Applications include perturbation modeling for longitudinal and lateral aircraft dynamics, and non-linear modeling for unsteady aerodynamics with high angles of attack.