Generalized Norm Optimal Iterative Learning Control with Intermediate Point and Sub-interval Tracking

Abstract

Norm Optimal Iterative Learning Control (NOILC) has recently been applied to Iterative Learning Control (ILC)
problems in which tracking is only required at a subset of isolated time points along the trial duration. This problem addresses the practical needs of many applications, including industrial automation, crane control, satellite positioning and motion control within a medical stroke rehabilitation context. This paper provides a substantial generalization of this framework by providing a solution to the problem of convergence at intermediate points with simultaneous tracking of subsets of outputs to reference trajectories on subintervals. This formulation enables the NOILC paradigm to tackle tasks which mix ‘point to point’ movements with linear tracking requirements and hence substantially broadens the application domain to include automation tasks which include welding or cutting movements, or human motion control where the movement is restricted by the task to straight line and/or planar segments. A solution to the problem is presented in the framework of NOILC and inherits NOILC’s well-defined convergence properties. Design guidelines and supporting
experimental results are included.

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Identifiers

ORCID for Bing Chu: orcid.org/0000-0002-2711-8717

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