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Robust Adaptive Iterative Learning Control with Switching σ $$ \sigma $$ -Modification
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DOI：10.1002/rnc.70253.png)
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Abstract
En 中文
In this paper, the problem of robust adaptive iterative learning control (RAILC) with switching σ $$ \sigma $$ -modifications is addressed for a class of nonlinear systems with unrepeatable uncertainties and initial errors. Different from the published learning algorithms, switching σ $$ \sigma $$ learning is introduced to ensure the robustness of the parametric estimation. The causal contradiction caused by the switching σ $$ \sigma $$ -modification is solved by applying an open-loop learning law. Sufficient conditions for initial rectifying functions (IRFs) are given for constructing prespecified tracking error trajectories, which are adopted in RAILC algorithms to cope with initial errors. S-class function with a series convergence sequence is utilized in the controller design to guarantee the perfect tracking performance. Simulations are given to compare the switching- σ $$ \sigma $$ and the saturated learning that demonstrate the effectiveness of the proposed learning control scheme.
Keywords:
initial rectifying function
robust adaptive iterative learning control
saturated learning
switching σ$$ \sigma $$-modification
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