scholarly journals System Identification Using Laguerre Basis Functions

2021 ◽  
Author(s):  
philip olivier
Keyword(s):  
System Identification ◽  
Discrete Time ◽  
New Product ◽  
Basis Functions ◽  
Model Order ◽  
Identification Process ◽  
Laguerre Expansion ◽  
Laguerre Basis ◽  
Expansion Coefficients ◽  
Using Data

This document describes how to use discrete time Laguerre basis functions, and their associated z-transforms, to construct a system identification process using experimentally determined Laguerre expansion coefficients of the input and output sequences. The process is derived using a new Product Propert} for the discrete time Laguerre basis functions. The system identification process is "linear in the parameters"; it does not require assumptions/knowledge of the poles locations of the system under test. An example is presented using data generated by a system that has appeared in the recent literature. The procedure naturally produces equations that can be used to determine if the chosen model order is correct or if its order needs to be increased. Constraints can easily be incorporated.

scholarly journals System Identification Using Laguerre Basis Functions

2021 ◽  
Author(s):  
philip olivier
Keyword(s):  
System Identification ◽  
Discrete Time ◽  
New Product ◽  
Basis Functions ◽  
Model Order ◽  
Identification Process ◽  
Laguerre Expansion ◽  
Laguerre Basis ◽  
Expansion Coefficients ◽  
Using Data

This document describes how to use discrete time Laguerre basis functions, and their associated z-transforms, to construct a system identification process using experimentally determined Laguerre expansion coefficients of the input and output sequences. The process is derived using a new Product Propert} for the discrete time Laguerre basis functions. The system identification process is "linear in the parameters"; it does not require assumptions/knowledge of the poles locations of the system under test. An example is presented using data generated by a system that has appeared in the recent literature. The procedure naturally produces equations that can be used to determine if the chosen model order is correct or if its order needs to be increased. Constraints can easily be incorporated.

Concurrent Engineering Assessment: A Proposed Framework

1996 ◽  
Vol 210 (4) ◽  
pp. 321-328 ◽  
Author(s):  
J Poolton ◽  
I Barclay
Keyword(s):  
Product Development ◽  
New Product Development ◽  
Concurrent Engineering ◽  
Research Study ◽  
New Product ◽  
Using Data ◽  
Past Experiences ◽  
Depth Interviews

There are few studies that have found an adequate means of assessing firms based on their specific needs for a concurrent engineering (CE) approach. Managers interested in introducing CE have little choice but to rely on their past experiences of introducing change. Using data gleaned from a nine month case study, a British-wide survey and a series of in-depth interviews, this paper summarizes the findings of a research study that examines how firms orientate themselves towards change and how they go about introducing CE to their operations. The data show that there are many benefits to introducing CE and that firms differ with respect to their needs for the CE approach. A tentative means to assess CE ‘needs’ is proposed which is based on the level of complexity of goods produced by firms. The method is currently being developed and extended to provide an applications-based framework to assist firms to improve their new product development performance.

Multiparameter Real-World System Identification using Iterative Residual Tuning

2021 ◽  
pp. 1-12
Author(s):  
Adam Allevato ◽  
Mitch W Pryor ◽  
Andrea L. Thomaz
Keyword(s):  
System Identification ◽  
Robot Control ◽  
Tracking Error ◽  
Nonlinear System Identification ◽  
Visual Observation ◽  
Free System ◽  
Proposed Model ◽  
Derivative Free ◽  
Identification Technique ◽  
Using Data

Abstract In this work we consider the problem of nonlinear system identification, using data to learn multiple and often coupled parameters that allow a simulator to more accurately model a physical system or mechanism and close the so-called reality gap for more accurate robot control. Our approach uses iterative residual tuning (IRT), a recently-developed derivative-free system identification technique that utilizes neural networks and visual observation to estimate parameter differences between a proposed model and a target model. We develop several modifications to the basic IRT approach and apply it to the system identification of a 5-parameter model of a marble rolling in a robot-controlled labyrinth game mechanism. We validate our technique both in simulation—where we outperform two baselines—and on a real system, where we achieve marble tracking error of 4% after just 5 optimization iterations.

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