Experimental Validation of Robust Resonance Entrainment for CPG-Controlled Tensegrity Structures

Rhythmic motion employed in animal locomotion is ultimately controlled by neuronal circuits known as central pattern generators (CPGs). It appears that these controllers produce efficient oscillatory command signals by entraining to a resonant gait via sensory feedback. This property is of great interest in the control of autonomous vehicles. In this paper, we experimentally validate synthesized CPG control of tensegrity structures. The prestressed cables in a tensegrity structure provide a method of simultaneous actuation and sensing, analogous to the biological motor control mechanism of regulating muscle stiffness through motoneuron activation and sensing the resulting motion by stretch receptors. A three-cell class-two tensegrity structure is designed, built, and modeled to predict the structure's dynamic response. The models are experimentally validated using open-loop control tests. Next, a simple CPG, called a reciprocal inhibition oscillator (RIO), is designed and synthesized in real time. The RIOs outputs are used as actuation commands, while sensory signals from the tensegrity are fed back to the RIO. Multiple controller configurations are tested to validate an RIO design method developed and reported in a complementary study. Finally, the tensegrity dynamics are perturbed by altering the mass of the tensegrity, and the robustness of RIO control is demonstrated through its ability to entrain to the perturbed system.