Modeling, Identification and Control of Robots

By W. Khalil, E. Dombre. 2003

1,149 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/66a0a78b58de108f5f7ee4015cd681fdc6812fd8

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Citations in the corpus

Feedback Control of Dynamic Bipedal Robot Locomotion (2007, methodology): “Other user-oriented sources on the use of Lagrange’s method for the derivation of equations of motions for rigid-body mechanical systems from a roboticist’s perspective are [60,71,164,206,218].”
Identification of consistent standard dynamic parameters of industrial robots (2013, methodology): “This is a strong result, which means that the essential parameters, which have significant identified values with respect to their small standard deviation (depending on measurement and modelling errors), are consistent because they lead to identify a set of standard essential consistent parameters.”
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].”
Rigid Body Dynamics Algorithms (2007, methodology): “If the system can be dismantled, then one can measure the inertias of the individual parts; otherwise, one can use parameter-identification techniques, such as those described in Khalil and Dombre (2002).”
Underwater manipulators: A review (2018, background): “However, PID based control laws provide poor dynamic accuracy when trajectory tracking comes into play and the dynamic performance of the manipulator varies according to its configuration (Khalil and Dombre, 2004).”
Robust dynamic experimental identification of robots with set membership uncertainty (2005, methodology): “The description of the geometry of the robot uses the modified Denavit and Hartenberg notation [7]–[9].”
Model learning for robot control: a survey (2011, background): “Estimating such parametric models from data has been popular for a long time [6,64] due to the applicability of well-known system identification techniques and adaptive control approaches [77].”
Reducing hardware experiments for model learning and policy optimization (2015, background): “Classical parameter identification techniques [2] partially solve this problem by fitting model parameters to experimental data, but they are still limited to factors that can actually be modeled.”