Bioprocess Modeling and Control
Most biological and processes are nonlinear systems with time delays. Effective process modeling and control strategies are needed. We are developing new methodologies for time delay system identification and predictive control. This involves theoretical development, computer simulation, and experimental validation. Applications include vision systems and food processes.
Objectives & Activities:
This research consists of multiple components. The objectives and activities of these components include:
- Visual system modeling (ERG signal processing, visual system state estimation, and development of diagnostic tools)
- Method development (time-delay system identification, predictive control, and neural network-based statistical process control)
- Food process control (process identification, sensory-based process control, fuzzy set applications).