The enhanced performance of a robotic arm control based on neural oscillator networks

While the central pattern generator (CPG) controller based on biologically inspired neural oscillator networks is known to achieve repeatability and entrainment of rhythmic output motion, no systematic approach to implement the controller has been provided so far. In this study a series of schemes are introduced to tune the control parameters and to set up the network coupling of artificial neural oscillators combined with the virtual force control. Application of the schemes to the proposed controller for a three-link planar robot arm demonstrates that the optimized controller with proper neural oscillator networks can produce minimize the energy consumption with an enhanced-entrained motion with the change of a desired motion and interaction of unknown environments.