A two-step iterative method (1,2) f or a reduction in the order of linear continuous- time systems, given in the state equation or the transfer function, is extended to reduce discretetime systems. The method requires the optimization of the residues and eigenvalues (or poles) belonging to an object
A new two-step iterative method for optimal reduction of linear SISO systems
β Scribed by Chyi Hwang; Jyh-Haur Hwang
- Publisher
- Elsevier Science
- Year
- 1996
- Tongue
- English
- Weight
- 666 KB
- Volume
- 333
- Category
- Article
- ISSN
- 0016-0032
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β¦ Synopsis
~ new two-step iterative procedure is proposedJbr the optimal reduced-order modeling of linear time-invariant single-input single-output (SISO) systems. The performance index o[" optimal reduction is taken to be a quadratic function of the error between the time responses o[the original and reduced models. A t each iteration cycle, the numerator dynamics is first determined by solving a set of linear equations, and the denominator polynomial is then determined by a gradientbased search technique. The main features of the proposed procedure are that it searches the Routh stability parameters rather than the denominator polynomial coefficients of the reduced model, and computes the performance index and its gradients by a computationally efficient parametric algorithm. As a consequence, the need of stability monitoring in the step of searching optimal denominator polynomial for the reduced model is avoided, and the gradient vector evaluated exactly and efficiently for a gradient-based parameter search. Moreover, the constraint O[zero stead.v-state response error can be easily handled.
π SIMILAR VOLUMES
## This brief communication establishes a two-step iterative algorithm based on theorthogonalprojection forreducing orderof the high-ordersystem transferfunction orstate variableequations. A two-step iterativealgorithm which has been developed by the authors (1) consists of the residue and pole (or
A simple andjexible algorithm is presented tojnd stable reduced models, provided the original system has a set of dominant poles. The proposed technique applies to the multivariable case as well and provides a parameter to control the approximationfor small and large t.
A new two level method is developed for the optimization of non-linear dynamical systems with a quadratic cost function. This method used an expansion around the equilibrium point of the system to fix the second and higher order terms. These terms are compensated for iteratively at the second level