A forgetting-factor based data-driven optimal terminal iterative learning control with applications to product concentration control of ethanol fermentation processes

Abstract

Ethanol fermentation process (EFP) is characterized as a repetitive batch process with strong nonlinear behavior, changing operational conditions and exogenous disturbances which causes huge cost and hard difficulties in modeling an EFP. In this work, a forgetting-factor based data-driven optimal terminal iterative learning control (FF-DDOTILC) is proposed for the product concentration control of an EFP, which is regarded as an unknown nonlinear and nonaffine discrete-time system in general. An iterative dynamic linearization method is introduced to transfer the nonlinear system equivalently into a linear parametric incremental input-output form. The learning control law is derived by iteratively optimizing the proposed new objective function with a forgetting-factor. Meanwhile, a project parameter updating law is designed to estimate the unknown parameters in the linear input-output data model. By introducing a forgetting-factor, the proposed method becomes more flexible and efficient with a better control performance. The proposed FF-DDOTILC only depends on the I/O data for the design and analysis where the convergence of tracking error is guaranteed mathematically. The proposed method is applicable and effective in the product concentration control of the ethanol fermentation process verified through detail simulations.

Iterative learning control realized using an iteration-varying forgetting factor based on optimal gains

“…However, they focused only on algorithm convergence, and an in-depth analysis of the forgetting factor effect on system output characteristics was not performed. In recent years, ILCs with forgetting factor, without enough supporting theory, have been applied to several engineering domains (Cao et al, 2015; Dong et al, 2019; Lan et al, 2017; Lin et al, 2019). Most reports indicated that the forgetting factor prevented the accumulation of initial resetting errors and interference.…”

“…This continues to restrict its application to a certain extent and motivates our research. In Lin et al (2019), a forgetting-factor based data-driven optimal terminal ILC (FF-DDOTILC) was proposed for the product concentration control of an ethanol fermentation process (EFP). Although an index function of control input was designed with weighting and forgetting factors, the selection principles were not provided.…”

Iterative learning control for nonlinear nonaffine networked systems with stochastic noise in communication channels

“…However, many practical plants operate repetitively within a finite time interval. For example, the central air-conditioning systems (Lu et al, 2018), ethanol fermentation processes (Lin et al, 2019), computer numerical control machine tools (Zhang and Jiang, 2020), formation control systems (Chi et al, 2019), permanent magnet synchronous motors (Zhu et al, 2020), and so forth. For the repetitive systems, an iterative learning control (ILC) method (Arimoto et al, 1984) is proposed to enhance the tracking performance via learning from the obtained control knowledge of previous operations.…”

A data-driven approach for trajectory-based aircraft operation with controlled time of arrival and along-track wind effects

“…This concept was first proposed in Uchiyama (1978). Nowadays, it has become an important branch of intelligence control and it is widely used in practical systems, such as ethanol fermentation processes (Lin et al, 2019), robot manipulator (Bouakrif and Zasadzinski, 2018), autonomous aerial refueling (Dai et al, 2018) and spacecraft attitude control (Hu et al, 2018).…”