Data-driven set-point learning control with ESO and RBFNN for nonlinear batch processes subject to nonrepetitive uncertainties - Ahmad, Naseem; Hao, Shoulin; Liu, Tao; Gong, Yihui; Wang, Qing-Guo (ISA transactions, 2024)
This paper proposes an extended state observer (ESO) based data-driven set-point learning control (DDSPLC) scheme for a class of nonlinear batch processes with a priori P-type feedback control structure subject to nonrepetitive uncertainties, by only using the process input and output data available in practice. Firstly, the unknown process dynamics is equivalently transformed into an iterative dynamic linearization data model (IDLDM) with a residual term. A radial basis function neural network is adopted to estimate the pseudo partial derivative information related to IDLDM, and meanwhile, a data-driven iterative ESO is constructed to estimate the unknown residual term along the batch direction. Then, an adaptive set-point learning control law is designed to merely regulate the set-point command of the closed-loop control structure for realizing batch optimization. Robust convergence of the output tracking error along the batch direction is rigorously analyzed by using the contraction mapping approach and mathematical induction. Finally, two illustrative examples from the literature are used to validate the effectiveness and advantage of the proposed design. •Novel extended state observer (ESO) based data-driven set-point learning control (DDSPLC).•Iterative dynamic linearized data model (IDLDM) for nonlinear batch processes.•Use the radial basis function neural network (RBFNN) to approximate pseudo partial derivative.•Set-point learning law with an adaptive learning gain for a priori P-type feedback structure.•Robust convergence of the resulting ILC system along the batch direction is rigorously analyzed.