An Adaptive Supervisory Control Approach to Dynamic Locomotion Under Parametric Uncertainty

This paper presents an adaptive control scheme for robotic systems that operate in the face of—potentially large—structured uncertainty. The proposed adaptive controller employs an on-line supervisor that utilizes logic-based switching among a finite set of controllers to identify uncertain parameters, and adapt the behavior of the system based on a current estimate of their value. To achieve this, the adaptive control approach in this paper combines on-line parameter estimation and feedback control while avoiding some of the inherent difficulties of classical adaptive control strategies. Furthermore, the proposed supervisory control architecture is modular as it relies on established "off-the-shelf" feedback control law and estimator design approaches, instead of cus-tomizing the overall design to the specific requirements of an adaptive control algorithm. We demonstrate the efficacy of the method on the problem of a dynamically-walking bipedal robot delivering a payload of unknown mass, and show that, by switching to the controller that is the "best" according to a current estimate of the uncertainty, the system maintains a low energy cost during its operation.