A Generalized Design Method for Learning-Based Disturbance Observer

This article presents a generalized disturbance observer (DOB) design framework that is applicable to both multi-input multi-output (MIMO) and/or nonminimum phase systems. The design framework removes conventional DOB's structure constraint, which allows minimizing the H-infinity norm of the dynamics from disturbance to its estimation error over a larger feasible set. The design procedure does not require explicit plant inverse, which is usually challenging to obtain for MIMO or nonminimum phase systems. Furthermore, the generalized DOB is augmented by a learning scheme, which is motivated by iterative learning control, to further enhance the estimation and suppression of the disturbance when it has repetitive components. Both numerical and experimental studies are performed to validate the proposed learning-based DOB design framework.