Optimal Inversion-Based Iterative Learning Control for Overactuated Systems

An optimal inversion-based iterative learning control (Opt-In ILC) approach for overactuated systems is proposed. The Opt-In ILC update law is formulated as a constrained optimization problem using the plant model. Specifically, the ILC update law is designed to minimize control effort subject to a user-specified error convergence rate. To achieve robust monotonic convergence (MC), a frequency-domain optimization framework is adopted to determine the best plant model and robustness filter for Opt-In ILC. Simulations and experiments on an overactuated coarse-fine stage are used to demonstrate optimal control effort allocation and optimal selection of plant model and robustness filter via the proposed Opt-In ILC approach.