Adaptive Control of Underactuated Planar Pronking Hexapod
This addresses the problem of stable gait control for underactuated legged robots in the presence of modeling errors, which is incremental as it builds on existing adaptive control methods for specific robotic applications.
The paper tackles the challenge of controlling underactuated legged robots with uncertain or changing parameters by developing an online, model-based adaptive control approach for a planar hexapod robot's pronking behavior, showing through simulations that it is robust to high levels of parameter uncertainties compared to a non-adaptive controller.
Underactuated legged robots depict highly nonlinear and complex dynamical behaviors that create significant challenges in accurately modeling system dynamics using both first principles and system identification approaches. Hence, it makes a more substantial challenge to design stabilizing controllers. If physical parameters on mathematical models have miscalibrations due to uncertainty in identifying and modeling processes, designed controllers could perform poorly or even result in unstable responses. Moreover, these parameters can certainly change-over-time due to operation and environmental conditions. In that respect, analogous to a living organism modifying its behavior in response to novel conditions, adapting/updating system parameters, such as spring constant, to compensate for modeling errors could provide the advantage of constructing a stable gait level controller without needing "exact" dynamical parameter values. This paper presents an online, model-based adaptive control approach for an underactuated planar hexapod robot's pronking behavior adopted from antelope species. We show through systematic simulation studies that the adaptive control policy is robust to high levels of parameter uncertainties compared to a non-adaptive model-based dead-beat controller.