Neural Control of a Fully Actuated Biped Robot

According to the fact that humans and animals show marvelous abilities in walking on irregular terrain, there is a strong need for adaptive algorithms in walking of biped robots to behave like them. Since the stance leg can easily rise from the ground and it can easily rotate about the toe or the heel, the problem of controlling the biped robots is difficult. In this paper, according to the adaptive locomotion patterns of animals, coordination and control of body links have been done with central pattern generator (CPG) in spinal cord and feedback network from musculoskeletal system. A one layer feedforward neural network that its inputs are the scaled joint variables and the touch sensors of the feet, has been used for modeling the feedback network. Also according to the dynamics of the muscles, PI controllers are used at joints for tracking. Finally, for tuning the parameters of the CPG network and the feedback network, due to the stability of zero moment point (ZMP) and the suitable walking pattern, genetic algorithm (GA) has been used