Process Control Overview

Principles

Definition

Process control is a field of engineering and applied science focused on regulating the behavior of dynamic systems over time. It involves monitoring system states and adjusting inputs to achieve desired performance objectives. This discipline investigates theoretical principles and practical techniques for designing, implementing, and analyzing control strategies, often employing mathematical models, sensor data, and actuator manipulation. Its significance lies in ensuring efficiency, stability, quality, and safety across diverse applications, from industrial manufacturing to biological systems.

Ontological type

Control System Design

Core Methods

Industrial Applications

Key developments

Key Figures

Foundations of Model-Based Control era

David A. Kenny [1], associated with Northwestern University [3] and the University of Connecticut [4] during this era, contributed to establishing the foundations of model-based process control. His key contribution is reflected in the Process Analysis [7] (1981) work, which advanced systematic analysis of process data and supported the move toward theory-driven regulator design in early model-based control. Shimon Y. Nof [2], affiliated with Purdue University West Lafayette [5] and the College of Industrial Engineering [6] during this era, helped pioneer integrated control and decision-support concepts for manufacturing systems. His 1980 paper Control and Decision Support in Automatic Manufacturing Systems [8] highlighted how real-time decision support could complement control algorithms, accelerating the adoption of model-based methods in automated plants.

Robust Predictive Multivariable Control era

John F. MacGregor [1] was active at McMaster University [3] and the University of Utah [4] during this era, contributing to robust multivariable process control. His work on monitoring batch processes using multiway principal component analysis [5] and on multivariate SPC methods for process and product monitoring [6] provided early, scalable tools for fault detection and process supervision, shaping how complex systems were analyzed under uncertainty. Theodora Kourti [2] conducted her research at McMaster University [3] during this period. Her contributions include applying latent variable methods to process control and multivariate statistical process control in industry [7], developing multivariate dynamic data modeling for analysis and statistical process control of batch processes, start-ups and grade transitions [8], and co-authoring the 1996 Multivariate SPC Methods for Process and Product Monitoring paper [6], advancing robust monitoring and data-driven design in this era.

Data-Driven Digital-Twin Control era

S. Joe Qin [1] is associated with Beijing University of Chemical Technology [3] and University of Hong Kong [4] in the Data-Driven Digital-Twin Control era (2010-2023). The key contribution highlighted by the paper Survey on data-driven industrial process monitoring and diagnosis [7] is the consolidation of data-driven monitoring and diagnosis techniques that anchored the data-centric approach central to this era. Magnus Egerstedt [2] is associated with MIT [5] and University of California, San Diego [6] during the Data-Driven Digital-Twin Control era. His key contribution is the Control Barrier Functions: Theory and Applications [8], which develops barrier-function methods that provide safety guarantees and constraint-handling capabilities crucial for reliable, data-driven industrial control in this era.