Learning-Gain-Adaptive Iterative Learning Control to Linear Discrete-Time-Invariant Systems
Release Time:2025-04-30
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- Date:
- 2025-04-30
- Title of Paper:
- Learning-Gain-Adaptive Iterative Learning Control to Linear Discrete-Time-Invariant Systems
- Journal:
- IEEE Access
- Summary:
- For a class of repetitive linear discrete-time-invariant systems with the unit relative degree, a learning-gain-adaptive iterative learning control (LGAILC) mechanism is exploited, for which the iteration-wise performance index is to maximize the declining quantity of tracking-error energies at two adjacent operations without considering control input and any parameters, and the argument is the iterationtime-variable learning-gain vector. By taking advantage of rows/columns exchanging transformations and matrix theory, an explicit learning-gain vector is solved, which exhibits that the learning-gain vector is not only dependent upon the system Markov parameters but also relevant to the iteration-time-wise tracking errors. Benefited from the orthogonality of the rows/columns exchanging transformation, it is derived that the LGAILC scheme is non-conditionally strictly monotonically convergent. For the sake of ensuring the LGAILC to be robust to the system parameters' uncertainties, a pseudo-LGAILC strategy is developed whose system Markov parameter-based learning-gain vector involves the system parameters' uncertainties. Rigorous induction delivers that the pseudo strategy is strictly monotonically convergent with a wider uncertainty degree, which implies that the pseudo strategy is robust to the system parameters' uncertainties in a wider range. The numerical simulations demonstrate the validity and effectiveness.
- Co-author:
- Xiaohui Li， Xiaoe Ruan， Yan Liu
- Volume:
- 7（1）
- Page Number:
- 98934-98945
- Translation or Not:
- No
- Date of Publication:
- 2019-07-15