Pade type model order reduction for multivariable systems using routh approximation

By Rajendra Prasad. 2000

104 citations recorded by the source corpus.

Abstract A method of obtaining reduced order models for multivariable systems is described. It is shown that the method has several advantages, e.g. the reduced order models retain the steady-state value and stability of the original system. Irrespective of whether the original multivariable system is described in state space form or in the transfer matrix form, the proposed method yields the reduced order models in state space form. In this method the Routh approximation is used to formulate the common denominator polynomial of a reduced order model. This is used to describe the structure of A r matrix. The matrices B r and C r are chosen appropriately and some of the elements of B r / C r matrices are specified in such a way that after matching time moments/Markov parameters, the resulting equations are linear in the unknown elements of B r and C r matrices. The procedure is illustrated via a numerical example.

Subfields: Control and Systems Engineering, Statistical and Nonlinear Physics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/2fcdb6e858d6adfac4356b57ce8e8bf2cbcd597d

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In Control and Systems Engineering

1. Arvind Kumar Prajapati
2. Rajendra Prasad
3. C. B. Vishwakarma
4. Rajendra Prasad
5. Afzal Sikander
6. Amit Narwal

In Statistical and Nonlinear Physics

1. Arvind Kumar Prajapati
2. Rajendra Prasad
3. C. B. Vishwakarma
4. Rajendra Prasad
5. Afzal Sikander
6. Amit Narwal

In Statistics and Probability

1. Arvind Kumar Prajapati
2. C. B. Vishwakarma
3. Rajendra Prasad
4. Rajendra Prasad

Citations in the corpus

• Clustering Method for Reducing Order of Linear System using Pade Approximation (2008, methodology): “Several methods [16-20] are available for arriving at stable reduced order Pade approximants.”
• Order Reduction of Linear Dynamic Systems by Improved Routh Approximation Method (2019, unknown): “Routh–Pad e approximation [45,77] 0:1783sþ 0:1957 s2 þ 1:8089sþ 0:9352 9.”
• Model Order Reduction by Using the Balanced Truncation and Factor Division Methods (2018, methodology): “Routh approximation and Padé approximation [11,14] 0.”
• Model Reduction Using the Balanced Truncation Method and the Padé Approximation Method (2020, methodology): “1106 Routh and Padé approximations [22,25] 3.”