Conditioned Source Analysis, A Technique for Multiple Input System Identification with Application to Combustion Energy Separation in Piston Engines

1995 ◽  
Author(s):  
Michael F. Albright
Keyword(s):  
System Identification ◽  
Source Analysis ◽  
Energy Separation ◽  
Combustion Energy ◽  
Input System ◽  
Multiple Input

Output error MISO system identification using fractional models

2021 ◽  
Vol 24 (5) ◽  
pp. 1601-1618
Author(s):  
Abir Mayoufi ◽  
Stéphane Victor ◽  
Manel Chetoui ◽  
Rachid Malti ◽  
Mohamed Aoun
Keyword(s):  
System Identification ◽  
Optimization Algorithm ◽  
Simulation Analysis ◽  
Good Efficiency ◽  
Output Error ◽  
Single Output ◽  
Multiple Input ◽  
Fractional Models ◽  
Definition Of ◽  
Initialization Procedure

Abstract This paper deals with system identification for continuous-time multiple-input single-output (MISO) fractional differentiation models. An output error optimization algorithm is proposed for estimating all parameters, namely the coefficients and the differentiation orders. Given the high number of parameters to be estimated, the output error method can converge to a local minimum. Therefore, an initialization procedure is proposed to help the convergence to the optimum by using three variants of the algorithm. Moreover, a new definition of structured-commensurability (or S-commensurability) has been introduced to cope with the differentiation order estimation. First, a global S-commensurate order is estimated for all subsystems. Then, local S-commensurate orders are estimated (one for each subsystem). Finally the S-commensurability constraint being released, all differentiation orders are further adjusted. Estimating a global S-commensurate order greatly reduces the number of parameters and helps initializing the second variant, where local S-commensurate orders are estimated which, in turn, are used as a good initial hit for the last variant. It is known that such an initialization procedure progressively increases the number of parameters and provides good efficiency of the optimization algorithm. Monte Carlo simulation analysis are provided to evaluate the performances of this algorithm.

A Simple Algorithm for Pole Assignment in a Multiple Input Linear Time Invariant Dynamic System

1974 ◽  
Vol 96 (1) ◽  
pp. 13-18 ◽  
Author(s):  
M. R. Chidambara ◽  
R. B. Broen ◽  
J. Zaborszky
Keyword(s):  
Dynamic System ◽  
Linear Time ◽  
Simple Algorithm ◽  
Pole Assignment ◽  
Asymptotic State ◽  
Linear Time Invariant ◽  
Input System ◽  
Multiple Input ◽  
System Equations ◽  
Time Invariant

This paper is concerned with the development of a simple algorithm for solving the problem of pole assignment in a multiple input linear time-invariant dynamic system, by means of state variable feedback. Unlike other existing methods which solve the same problem, the proposed algorithm does not require the transformation of the system equations to a special canonical form or the reduction of the multiple input system to an equivalent single input system. Analogously, the dual problem of constructing an asymptotic state estimator for a multiple output system is solved, with the solution enjoying analogous advantages.

Modeling and Identification of a Nonlinear SDOF Moored Structure, Part 2—Comparisons and Sensitivity Study

2004 ◽  
Vol 126 (2) ◽  
pp. 183-190 ◽  
Author(s):  
S.C.S. Yim ◽  
S. Narayanan
Keyword(s):  
System Identification ◽  
Small Body ◽  
Ocean Waves ◽  
System Response ◽  
Mooring System ◽  
Nonlinear Structure ◽  
Single Output ◽  
Submerged Sphere ◽  
Multiple Input ◽  
Identification Technique

A system-identification technique based on the Reverse Multiple-Input/Single-Output (R-MI/SO) procedure is applied to identify the parameters of an experimental mooring system exhibiting nonlinear behavior. In Part 1, two nonlinear small-body hydrodynamic Morison type formulations: (A) with a relative-velocity (RV) model, and (B) with an independent-flow-field (IFF) model, are formulated. Their associated nonlinear system-identification algorithms based on the R-MI/SO system-identification technique: (A.1) nonlinear-structure linearly damped, and (A.2) nonlinear-structure coupled hydrodynamically damped for the RV model, and (B.1) nonlinear-structure nonlinearly damped for the IFF model, are developed for an experimental submerged-sphere nonlinear mooring system under ocean waves. The analytic models and the associated algorithms for parametric identification are described. In this companion paper (Part 2), we use the experimentally measured input wave and output system response data and apply the algorithms derived based on the multiple-input/single-output linear analysis of the reverse dynamic systems to identify the system parameters. The two nonlinear models are examined in detail and the most suitable physical representative model is selected for the mooring system considered. A sensitive analysis is conducted to investigate the coupled hydrodynamic forces modeled by the Morison equation, the nonlinear stiffness from mooring lines and the nonlinear response. The appropriateness of each model is discussed in detail.

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