2022, the year when I defended my Thesis, “Multi-body modeling of robot dynamics and system identification during MPC”, in Scientific Computing. The updated translation of the poem can be found here.

Due to external influences over parameters that characterize dynamical systems, an online parameter estimation must be added as part of model predictive control strategies. In this thesis, we show how continuous parameters estimation, using inverse dynamics, can be used for identifying the inertial parameters (mass, inertia, and center of mass) of multi-body systems as part of an adaptive control strategy. For this, a Featherstone spatial algebra equivalent model, based on screw theory was used. The system identification was done using a linear least squares approach using the Recursive Newton-Euler Algorithm as a way of implementing a generic solution. The process is for open-loop robots and is tested using an optimal control algorithm based on multiple shooting.

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