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An enhanced PI controller based on adaptive iterative learning control
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DOI：10.1002/rnc.6940.png)
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Abstract
En 中文
This paper presents a PI-type adaptive iterative learning control (PI-AILC) method for nonlinear processes, which targets enhancing system tracking capabilities by adapting setpoints of the PI controller. First, the proposed method employs compact form dynamic linearization technology to obtain a local linear representation of unknown nonlinear systems. Subsequently, the iterative learning controller gain is adaptively updated using the local linear expression to ensure the optimality of the setpoints. Finally, a pre-learning mechanism for offline data is introduced to augment the efficiency of the iterative mechanism further. The proof of strict convergence for PI-AILC is established. Experimental results substantiate the efficacy of PI-AILC.
Keywords:
adaptive learning
iterative learning control
PI controller
trace control
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