Learning-based Balancing of Model-based and Feedback Control for Second-order Mechanical Systems

High-performance tracking control of mechanical systems typically requires model-based control as it enables to counteract undesirable dynamics in a timely fashion. The quality of the compensation depends on the accuracy of the underlying model. However, the dynamics are often (partially) infeasible for complex systems in a-priori unknown environments. Due to the favorable properties of model-based control, the goal is to apply feedback control for error compensation only when necessary. In this paper, we present an online learning-based balancing (OLBB) framework to adapt the feedback gains such that the controlled system with state-dependent uncertainties satisfies given performance specifications. For this purpose, an oracle predicts the unknown dynamics and an error model adapts the feedback gains. The framework can be easily applied to existing control approaches to improve the safety and performance of the closed-loop.