MPC-based control strategy of a neuro-inspired quadruped robot

This paper proposes the application of the Model Predictive Control (MPC) strategy to the locomotion of a quadrupedal robot endowed with a Central Pattern Generator (CPG) neural locomotion architecture. The neural structure is adaptive based on the proprioceptive information and the exteroceptive signals acquired through ground contact sensors. The MPC generates the high-level descending commands used by the CPG, controlling the robot navigation. Using its capability to provide optimized output guaranteeing, at the same time, the state and input constraints of the system, the MPC allows a robust heading control of the robot suitably interacting with the neural locomotion paradigm. The obtained results are analyzed and compared with those obtained in the same robotic architecture using a standard PID controller.