On Finding Exciting Trajectories for Identification Experiments Involving Systems with Nonlinear Dynamics

By B. Armstrong. 1989

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Model-based reinforcement learning with parametrized physical models and optimism-driven exploration (2015, background): “citation trajectories for model identification [5], [14], [25], [28], [31], [32].”
Adaptive Reactionless Motion and Parameter Identification in Postcapture of Space Debris (2013, background): “As iswell known, convergence of the identification algorithm to accurate parameter values can only be guaranteed if the input motion of the arm is sufficiently or persistently exciting [30].”
Robot excitation trajectories for dynamic parameter estimation using optimized B-splines (2012, methodology): “Formulation of the Dynamic Model For the parameter identification different models were used in the literature [1], [5], [7], [8], [9], [10], [11], [12], [13] and [14].”
Optimal robot excitation and identification (1997, background): “Armstrong [16] describes an approach in which the degrees of freedom are the points of a sequence of joint accelerations.”