Robust adaptive input-output control for a class of modular robotic systems via inverse optimality theory

Abstract

This paper introduces a model-based robust adaptive input-output control framework for a family of robotic systems that include under-actuation, nonholonomic, and constrained properties. The proposed control framework can provide fast and effective controller generation for modular robotic systems (MRS) with interchangeable subsystems. The controller was first derived based on the general properties of nonholonomic robotic dynamic models while considering under-actuation and constraints. Then the adaptive control technique is introduced to overcome the effects such as inertia and force uncertainties. Robust augmentation is implemented via inverse optimality theory, which is verified with respect to a meaningful cost function. A simulation study on an aerial manipulator system with model uncertainty and disturbance was provided to demonstrate the characteristics and effectiveness of the proposed controller.