Output Feedback Control of Nonlinear Distributed Parameter Systems with Unknown Parameters Using a Two-Tier Adaptive Identification Method
- Type: Conference Presentation
- Conference Type: AIChE Annual Meeting
- Presentation Date: November 8, 2021
- Duration: 19 minutes
- Skill Level: Intermediate
- PDHs: 0.50
A standard approach to obtain the approximated model is applying a weighted residual method to discretize the governing DPDEs. The most challenging part of such an approach is the computation of the optimal basis functions required by the weighted residual method, especially in the presence of unknown transport-reaction parameters. To resolve this fundamental issue, a learning-based approach is used to recursively compute the basis functions from the spatiotemporal data of the system's key states. As the number and shape of the resulting basis functions change by appearing new dynamics, the dimension and form of the approximated model change adaptively to capture the dominant dynamics. The resulting adaptive approximate reduced-order model is then used for an adaptive output feedback control design consisting of a dynamic observer to estimate the states of the reduced-order model, an adaptation law to estimate the unknown parameters, and a Lyapunov-based nonlinear controller to stabilize the dominant modes of the system dynamics. The temperature regulation problem in a catalytic reactor with unknown transport-reaction parameters is used as a case study to illustrate the application and computational advantages of the proposed two-tier adaptive identification and output feedback control strategy.
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