西安交通大学教师主页管理系统阮小娥 Iterative learning controllers with time-varying gains for large-scale industrial processes to track trajectories with different magnitudes Home

Iterative learning controllers with time-varying gains for large-scale industrial processes to track trajectories with different magnitudes

Release Time:2025-04-30

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Title of Paper:: Iterative learning controllers with time-varying gains for large-scale industrial processes to track trajectories with different magnitudes

Summary:: In this paper, a set of decentralized open-loop and closed-loop iterative learning controllers are embedded into the procedure of steady-state hierarchical optimization utilizing feedback information for large-scale industrial processes. The task of the learning controllers is to generate a sequence of upgraded control inputs iteratively to take responsibilities of a sequential step function-type control decisions each of which is determined by the steady-state optimization layer and then imposed to the real system for feedback information. In the learning control scheme, the learning gains are designated to be time-varying which are adjusted by virtue of expertise experiences-based IF-THEN rules, and the magnitudes of the learning control inputs are amplified by the sequential step function-type control decisions. The aim of learning schemes is to further effectively improve the transient performance. The convergence of the updating laws is deduced in the sense of Lebesgue 1-norm by taking advantage of the Hausdorff-Young inequality of convolution integral and the Hoelder inequality of Lebesgue norm. Numerical simulations manifest that both the open-loop and the closed-loop time-varying learning gain-based schemes can effectively decrease the overshoot, accelerate the rising speed and shorten the settling time, etc.