The synthesis of a model-based control structure for general linear dissipative distributed parameter systems (DPSs) is explored in this manuscript. Discrete-time distributed state measurements (called process snapshots) are used by a continuous-time regulator to stabilize the process. The main objective of this article is to identify a criterion to minimize the communication bandwidth between sensors and controller (snapshots acquisition frequency) using linear systems analysis and still achieve closed-loop stability. This objective is addressed by adding a modeling layer to the regulator. Theoretically, DPSs can be well described by low dimensional ordinary differential equation models when represented in functional spaces; practically, the model accuracy hinges on finding basis functions for these spaces. Adaptive proper orthogonal decomposition is used to identify statistically important basis functions and establish locally accurate reduced order models which are then used in controller design. The proposed approach is successfully applied toward thermal regulation in a tubular chemical reactor.
- Adaptive model reduction
- Adaptive proper orthogonal decomposition
- Distributed parameter systems
- Hybrid systems
- Process control