An enhanced PI controller based on adaptive iterative learning control
Huiping Liang, Chunhua Yang, Mingjie Lv, Bei Sun, Yonggang Li- Electrical and Electronic Engineering
- Industrial and Manufacturing Engineering
- Mechanical Engineering
- Aerospace Engineering
- Biomedical Engineering
- General Chemical Engineering
- Control and Systems Engineering
Summary
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.