2 research outputs found
Output Synchronization of Nonlinear Systems under Input Disturbances
We study synchronization of nonlinear systems that satisfy an incremental
passivity property. We consider the case where the control input is subject to
a class of disturbances, including constant and sinusoidal disturbances with
unknown phases and magnitudes and known frequencies. We design a distributed
control law that recovers the synchronization of the nonlinear systems in the
presence of the disturbances. Simulation results of Goodwin oscillators
illustrate the effectiveness of the control law. Finally, we highlight the
connection of the proposed control law to the dynamic average consensus
estimator developed in [1].Comment: 8 pages, 9 figure
Extension of Full and Reduced Order Observers for Image-based Depth Estimation using Concurrent Learning
In this paper concurrent learning (CL)-based full and reduced order observers
for a perspective dynamical system (PDS) are developed. The PDS is a widely
used model for estimating the depth of a feature point from a sequence of
camera images. Building on the current progress of CL for parameter estimation
in adaptive control, a state observer is developed for the PDS model where the
inverse depth appears as a time-varying parameter in the dynamics. The data
recorded over a sliding time window in the near past is used in the CL term to
design the full and the reduced order state observers. A Lyapunov-based
stability analysis is carried out to prove the uniformly ultimately bounded
(UUB) stability of the developed observers. Simulation results are presented to
validate the accuracy and convergence of the developed observers in terms of
convergence time, root mean square error (RMSE) and mean absolute percentage
error (MAPE) metrics. Real world depth estimation experiments are performed to
demonstrate the performance of the observers using aforementioned metrics on a
7-DoF manipulator with an eye-in-hand configuration