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Model predictive control of a humanoid robot

Andrei Herdt 1 
1 BIPOP - Modelling, Simulation, Control and Optimization of Non-Smooth Dynamical Systems
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology
Abstract : The range of motions that humanoid robots are able to realize is strongly limited by inherent dynamical constraints so that any control law that does not consider these limitations, in one way or another, will fail to avoid falling. The Model Predictive Control (MPC) technique is capable of handling constraints on the state and the control explicitly, which makes it highly apt for the control of walking robots.We begin by unveiling the specific structure of these constraints, stressing especially the impor- tance of the supports on the ground. We give thereupon a sufficient condition for keeping balance and formulate an MPC law that complies with it. This formulation serves us then for the design of practicable controllers capable of more efficient and more robust control of humanoid robots.
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Submitted on : Wednesday, July 23, 2014 - 10:31:57 AM
Last modification on : Thursday, January 20, 2022 - 5:28:11 PM
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  • HAL Id : pastel-01038025, version 1



Andrei Herdt. Model predictive control of a humanoid robot. Other. Ecole Nationale Supérieure des Mines de Paris, 2012. English. ⟨NNT : 2012ENMP0113⟩. ⟨pastel-01038025⟩



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