Anthony Mario D'Amato

Minimum-norm input reconstruction for nonminimum-phase systems

Input reconstruction is a process where the inputs to a system are estimated using the measured system output and knowledge of the system model. One way to achieve this goal is to invert the system model and cascade delays to guarantee that the inverse is proper. A standing issue in input reconstruction lies in the inversion of nonminimum-phase systems, since the inverse model is unstable. In the present paper we assume that the unknown input is bounded and we present a novel technique that uses Markov parameters to determine the unknown state that minimizes the difference between the unknown input and the estimated input.

Transition threshold optimization for a rule based automotive cruise control

This paper presents a novel approach to fuel efficient vehicle speed control. Motivated by potential fuel economy improvements through periodic time-varying cruise control, a finite state machine structure is designed to control transitions between discrete states of acceleration, constant speed, and deceleration. The scheme accounts for changes in road grade, reduces fuel consumption, maintains drivability, and is easy to calibrate and implement. A method for optimizing the thresholds that control transitions between the states of the finite state machine is introduced and demonstrated. Results from simulation and vehicle testing are presented for a test route, demonstrating up to a 5.47% dynamic fuel consumption improvement potential in vehicle testing.