Model-Free Primitive-Based Iterative Learning Control Approach to Trajectory Tracking of MIMO Systems With Experimental Validation

摘要: This paper proposes a novel model-free trajectory tracking of multiple-input multiple-output (MIMO) systems by the combination iterative learning control (ILC) and primitives. The optimal solution is obtained in terms previously learned solutions to simple tasks called library primitives that are stored memory consists pairs reference input/controlled output signals. input optimized ILC framework without using knowledge controlled process. guaranteed convergence scheme built upon virtual feedback tuning design decoupling controller. Each new complex be tracked decomposed into regarded as basis functions. for system track desired next recomposed from advantageous because computed straightforward need learn repeated executions task. In addition, optimization problem specific square MIMO set problems assigned each separate single-input single-output channel ensures convenient decoupling. primitive-based approach capable planning, reasoning, learning. A case study dealing with nonlinear aerodynamic included validate approach. experimental results given.