Abstract
Abstract This paper proposes a particular type of nonlinear data-driven Model Predictive Control (NMPC) strategy, called Neural Network Model Predictive Control (NNMPC), applied to a simulated neuro-inspired quadruped robot. The locomotion control is realised using a central pattern generator (CPG) implemented through oscillators synchronized through environmental feedback. The NMPC provides a descending command to the robot for steering control. This is realized by regulating a parameter governing the dynamics of the CPG structure. In order to test the performance obtained applying the NMPC, the results are compared with those obtained using a linear MPC. Carrying out a comparative analysis, the differences between the two methods will be highlighted.
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