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High-order data-driven adaptive iterative learning control for nonlinear nonaffine systems
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DOI：10.1002/asjc.70162.png)
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Abstract
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This paper presents a novel high-order error-based data-driven adaptive iterative learning control (HOE-DDAILC) strategy for nonlinear nonaffine systems. Using iterative dynamic linearization (IDL), the nonlinear system is first reformulated into an iterative linear data model (iLDM), enabling data-driven control design. The control learning law is obtained by minimizing a performance index including high-order error terms, ensuring asymptotic tracking of the reference trajectory. In addition, for systems with input constraints, a constrained HOE-DDAILC is developed via the combination of an unconstrained solution and interval projection, which guarantees feasible control inputs while preserving tracking accuracy. Simulation results on nonlinear systems validate the effectiveness and superiority of the proposed methods.
Keywords:
adaptive iterative learning control
data-driven
high-order
input constraint
nonlinear nonaffine system
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