PI-Based Set-Point Learning Control for Batch Processes With Unknown Dynamics and Nonrepetitive Uncertainties

• Published in: IEEE transactions on automatic control Vol. 70; no. 6; pp. 3649 - 3664
• Main Authors: Hao, Shoulin, Liu, Tao, Rogers, Eric, Wang, Youqing, Paszke, Wojciech
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive learning; Batch processes; Batch processes with unknown dynamics; Batch processing; Batch production systems; Closed loops; Control theory; Convergence; data-driven control (DDC); Dynamics; Feedback control; Initial conditions; iterative extended state observer (IESO); Iterative methods; Learning; Nonlinear dynamical systems; Optimization; PI control; Process control; proportional–integral (PI) controller tuning; Real time; robust convergence analysis; Robustness (mathematics); set-point learning control; State observers; Tracking errors; Tuning; Uncertainty
• ISSN: 0018-9286; 1558-2523
• DOI: 10.1109/TAC.2024.3512196

For industrial batch processes with unknown dynamics subject to nonrepetitive initial conditions and disturbances, this article develops a novel adaptive data-driven set-point learning control (ADDSPLC) scheme based on only the measured process input and output data, which has two loops, one for the dynamics within a batch and the other for the batch-to-batch dynamics. In the former case, a model-free tuning strategy is first presented for determining the closed-loop proportional-integral controller parameters. For the latter case, a set-point learning control law with adaptive set-point learning gain and gradient estimation is developed for batch run optimization. The robust convergence of the output tracking error is rigorously analyzed together with the boundedness of adaptive learning gain and real-time updated set-point command. Moreover, another iterative extended-state-observer-based ADDSPLC scheme is developed with rigorous convergence and boundedness analysis to enhance the robust tracking performance against nonrepetitive uncertainties. Finally, two illustrative examples from the literature are used to demonstrate the effectiveness and superiority of the new schemes over the recently developed data-driven learning control designs.