Gait Generation for a Hopping Robot Via Iterative Learning Control Based on Variational Symmetry
This paper proposes a novel framework to generate optimal gait trajectories for a one-legged hopping robot via iterative learning control. This method generates gait trajectories which are solutions of a class of optimal control problems without using precise knowledge of the plant model. It is expected to produce natural gait movements such as that of a passive walker. Some numerical examples demonstrate the effectiveness of the proposed method.
KeywordsCost Function Hamiltonian System Stance Phase Iterative Learning Control Variational Symmetry
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