کنترل مکان یابی فضایی: ردیابی خروجی

This paper focuses on tracking spatially repeatable tasks. In addition, these tasks are not necessarily temporally repeatable in the sense that the finite length of the corresponding time interval may change with each repetition. Because of that, the standard Iterative Learning Control (ILC) framework is not directly applicable. Namely, the standing assumption that the finite length of the time interval is fixed with each repetition, is violated. Motivated by human motor learning, this paper proposes a Spatial ILC (SILC) framework which leverages the spatial repeatability. In particular, the concept of spatial projection, closely related to temporal rescaling, is proposed. This allows to spatially relate the relevant information from the past repetition to the present repetition. To demonstrate the proposed framework, a class of nonlinear time-varying systems with relative degree zero is selected. In particular, using contraction mapping technique, it is shown that under appropriate assumptions, the corresponding tracking error converges under the proposed SILC control law. Finally, simulation results support the obtained result.