Hybrid Dynamic Modelling and Bioinspired Control Based on Central Pattern Generator of Biped Robotic Gait

Abstract

This chapter presents the theoretical foundations and methodology to develop a bioinspired hybrid control architecture for a biped robotic device that reproduces gait and human motor control strategies with the ability to adapt the trajectory to environmental conditions. The objective is to design robotic devices (such as exoskeletons), through the functional integration of hybrid dynamic system modeling (event-driven and continuous dynamics) with efficient and robust conventional control techniques and bioinspired control algorithms, with a near-natural human gait pattern. The human gait cycle is modeled as a hybrid dynamic using a finite state machine (FSM). The gait trajectories are to be generated in such a way that they will be capable of adapting to disturbances in the path followed by the robotic device; this will be achieved using a neuronal oscillator that simulates the behavior of a central pattern generator (CPG).