Abstract
This paper presents a two-step method for control-relevant model reduction of Volterra series models. First, using the nonlinear IMC design as a basis, an explicit expression relating the closed-loop performance to the open-loop modeling error is obtained. Secondly, an optimization problem that seeks to minimize the closed-loop error subject to the restriction of a reduced-order model is posed. By showing that model reduction of kernels with different degrees can be decoupled in the problem formulation, the optimization problem is simplified into a mathematically more convenient form which can be solved with significantly less computational effort. The effectiveness of the proposed method is illustrated on a polymerization reactor example where a second-order Volterra model with 85 parameters is reduced to a Hammerstein model with 3 parameters. Despite the lower 'open-loop' predictive ability of the control-relevant model, the closed-loop performance of the reduced-order control system closely mimics that of the full order model.
Original language | English (US) |
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Pages (from-to) | 79-88 |
Number of pages | 10 |
Journal | Journal of Process Control |
Volume | 8 |
Issue number | 2 |
DOIs | |
State | Published - Apr 1998 |
Keywords
- Control-relevant modeling
- Model reduction
- Volterra series
ASJC Scopus subject areas
- Control and Systems Engineering
- Modeling and Simulation
- Computer Science Applications
- Industrial and Manufacturing Engineering