The past decade has witnessed a resurgence of interest and activity in process control. While economic plant optimization translated into highly complex and integrated process designs, tighter product specifications and narrower profit margins increased the performance requirements of the control system. Work in our group studies the interacting tasks of process control, optimization and model identification, and focuses on applications from the oil, chemical and, most recently, the semiconductor manufacturing industry. Model Predictive Control MPC algorithms use a linear or nonlinear process model to predict future values of the process outputs. The fact that modeling error is always present creates the need for designing controllers which are robust with respect to model-plant mismatch.
Optimization is used in real-time to obtain appropriate input values.
The presence of constraints as physical bounds as well as performance and safety specifications result in complex control laws. We study the design of MPC algorithms and the use of CAD tools for tuning the nonlinear control system for stability and performance in the presence of modeling error. The degree of accuracy required of a process model used for designing a control system depends on the closed-loop performance and stability specifications for the controller. Our work also examines the use of control requirements in reducing the size of first principles process models that will be used for on-line control, focusing on rapid thermal chemical vapor deposition. Optimal Operation of Batch Processes Many polymerization and biochemical processes are operated in a batch or semi-batch manner.