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.