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Second level adaptation using multiple models for the adaptive control of linear systems with unknown parameters was introduced in [1], and its robustness properties were discussed in [2]. In both cases, the plant, the identification models, and the reference model were assumed to be described by state equations in companion form, with all state variables accessible.
This paper proposes a robust iterative learning control based on a two-degree-of-freedom (2DOF) framework for linear-time-invariant (LTI) systems with uncertainties. It is motivated by the unavoidable model plant mismatches and the presence of the uncertainties in practical process, which hinder the application of the precise model-based iterative learning control (ILC) techniques in practice. In...
We present schemes for both continuous and discrete time adaptive control of linear time invariant systems. which are partially known, along with analysis of their convergence properties.
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