scholarly journals MPC for Large-Scale Systems via Model Reduction and Multiparametric Quadratic Programming

2006 ◽  
Author(s):  
S. Hovland ◽  
K. Willcox ◽  
J. T. Gravdahl
Keyword(s):  
Quadratic Programming ◽  
Model Reduction ◽  
Large Scale ◽  
Large Scale Systems

Shift-Independent Model Reduction of Large-Scale Second-Order Mechanical Structures

2016 ◽  
Vol 138 (4) ◽  
Author(s):  
Masih Mahmoodi ◽  
Kamran Behdinan
Keyword(s):  
Model Reduction ◽  
Large Scale ◽  
Second Order ◽  
Computational Time ◽  
Original Structure ◽  
Fe Model ◽  
Krylov Methods ◽  
Convergence Criteria ◽  
Large Scale Systems ◽  
Reduction Techniques

Nonmodal model order reduction (MOR) techniques present accurate and efficient ways to approximate input–output behavior of large-scale mechanical structures. In this regard, Krylov-based model reduction techniques for second-order mechanical structures are typically known to require a priori knowledge of the original system parameters, such as expansion points (or eigenfrequencies). The calculation of the eigenfrequencies of the original finite-element (FE) model can be significantly time-consuming for large-scale structures. Existing iterative rational Krylov algorithm (IRKA) addresses this issue by iteratively updating the expansion points for first-order formulations until convergence criteria are achieved. Motivated by preserving the model properties of second-order systems, this paper extends the IRKA method to second-order formulations, typically encountered in mechanical structures. The proposed second-order IRKA method is implemented on a large-scale system as an example and compared with the standard Krylov and Craig-Bampton reduction techniques. The results show that the second-order IRKA method provides tangibly reduced error for a multi-input-multi-output (MIMO) mechanical structure compared to the Craig-Bampton. In addition, unlike the standard Krylov methods, the second-order IRKA does not require the information on expansion points, which eliminates the need to perform a modal analysis on the original structure. This can be especially advantageous for large-scale systems where calculations of the eigenfrequencies of the original structure can be computationally expensive. For such large-scale systems, the proposed MOR technique can lead to significant reductions of the computational time.

Model Reduction of Large-Scale Discrete Plants With Specified Frequency Domain Balanced Structure

2004 ◽  
Vol 127 (3) ◽  
pp. 486-498 ◽  
Author(s):  
Abbas H. Zadegan ◽  
Ali Zilouchian
Keyword(s):  
Model Reduction ◽  
Frequency Domain ◽  
Large Scale ◽  
Linear Time ◽  
Model Simulation ◽  
Large Scale Systems ◽  
Linear Time Invariant ◽  
Balanced Structure ◽  
Linear Time Invariant Systems ◽  
Model Reduction Technique

A new model reduction technique for linear time-invariant systems is proposed. A new method that reduces the order of large-scale systems by integrating singular perturbation with specified frequency domain balanced structure is proposed. Considering a frequency range at which the system actually operates guarantees a good approximation of the original full order model. Simulation experiments for model reduction of several large-scale systems demonstrate the effectiveness of the proposed technique.

scholarly journals Model reduction of large-scale systems by least squares

2006 ◽  
Vol 415 (2-3) ◽  
pp. 290-321 ◽  
Author(s):  
Serkan Gugercin ◽  
Athanasios C. Antoulas
Keyword(s):  
Least Squares ◽  
Model Reduction ◽  
Large Scale ◽  
Large Scale Systems
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