Predictive functional control for challenging dynamic processes using a simple prestabilization strategy

• Published in: Advanced control for applications Vol. 4; no. 2
• Main Authors: Aftab, Muhammad Saleheen, Rossiter, John Anthony
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc 01-06-2022
• Subjects: Algorithms; coincidence horizon; constraint handling; modeling uncertainty; Perturbation; Predictive control; predictive functional control; prestabilization; Proportional integral derivative
• ISSN: 2578-0727; 2578-0727
• DOI: 10.1002/adc2.102

Predictive functional control (PFC) is a straightforward and cheap model‐based technique for systematic control of well‐damped open‐loop processes. Nevertheless, its oversimplified design characteristics are often the cause of diminished efficacy in more challenging applications; processes involving lightly damped and/or unstable dynamics have been particularly difficult to control with PFC. This paper presents a more sustainable solution for such applications by integrating the concept of prestabilization within the predictive functional control formulation. This is essentially a two‐stage synthesis wherein the undesirable open‐loop dynamics are first compensated, using a well‐understood classical approach such as proportional integral derivative (PID), before implementing predictive control in a cascade structure. The proposal, although comes with significant implications for tuning and constraint handling, is, nonetheless, straightforward and provides improved closed‐loop control in the presence of external perturbations compared to the standard PFC and the PID algorithms, as demonstrated with two industrial case studies.