Precision coordination and motion control of multiple systems via iterative learning control

In this paper, we focus on improving the trajectory tracking and formation coordination performance of multiple systems through the use of iterative learning control. A Norm Optimal framework is used to design optimal learning filters based on varying design objectives. The general norm optimal framework is reformatted to enable separate weighting on individual system trajectory tracking, coupled system trajectory tracking, and coordinated system formation or shape tracking. A general approach for designing a norm optimal learning controller for this coupled system is included. The novel structure of the weighting matrices used in this approach enables one to focus on individual design objectives (e.g. trajectory tracking, formation tracking) and formation approaches (e.g. leader reference, formation center, and neighbor reference tracking) that affect the overall performance of the coupled systems within the same framework. The capabilities of the proposed controller are validated through simulation results.