An Application of Nonlinear Parametric Identification and Control of a Minimum Phase Acrobot

2006 ◽  
Author(s):  
Klaus Nji ◽  
Mehran Mehrandezh
Keyword(s):  
Parametric Identification ◽  
Minimum Phase ◽  
And Control ◽  
Nonlinear Parametric Identification

Nonlinear Parametric Identification Using Chaotic Data

1995 ◽  
Vol 1 (3) ◽  
pp. 291-305 ◽  
Author(s):  
N. Van de Wouw ◽  
G. Verbeek ◽  
D.H. Van Campen
Keyword(s):  
Time Series ◽  
Numerical Computation ◽  
Periodic Orbits ◽  
Nonlinear Dynamical System ◽  
Parametric Identification ◽  
Chaotic Time Series ◽  
Unstable Periodic Orbits ◽  
Estimation Process ◽  
Identification Method ◽  
Nonlinear Parametric Identification

The subject of this paper is the development of a nonlinear parametric identification method using chaotic data. In former research, the main problem in using chaotic data in parameter estimation appeared to be the numerical computation of the chaotic trajectories. This computational problem is due to the highly unstable character of the chaotic orbits. The method proposed in this paper is based on assumed physical models and has two important components. First, the chaotic time series is characterized by a "skeleton" of unstable periodic orbits. Second, these unstable periodic orbits are used as the input information for a nonlinear parametric identification method using periodic data. As a consequence, problems concerning the numerical computation of chaotic trajectories are avoided. The identifiability of the system is optimized by using the structure of the phase space instead of a single physical trajectory in the estimation process. Furthermore, before starting the estimation process, a huge data reduction has been accomplished by extracting the unstable periodic orbits from the long chaotic time series. The method is validated by application to a parametrically excited pendulum, which is an experimental nonlinear dynamical system in which transient chaos occurs.

System-Level Modeling and Parametric Identification of Electric Impact Wrench

2016 ◽  
Vol 138 (11) ◽  
Author(s):  
Shengli Zhang ◽  
J. Tang
Keyword(s):  
Experimental Studies ◽  
Parametric Identification ◽  
System Level ◽  
Multi Objective Optimization ◽  
Dynamic Events ◽  
Spindle Shaft ◽  
Optimization And Control ◽  
Level Model ◽  
System Level Model ◽  
And Control

Electric impact wrench is an important tool used in manufacturing and maintenance services. It has complex mechanism and its operation involves dynamic events occurring at vastly different time scales, which poses challenges for efficient and accurate modeling to facilitate design optimization and control. This investigation establishes a first principle-based, system-level model of a representative impact wrench. The model explicitly incorporates the dynamic flexibility of gear transmission, spindle shaft, and impacting components into the kinematic relations that connect them together. The nonlinear impact and contact events, coupled with the rotational and translational motions of all components, are explicitly analyzed, and systematic parametric identification is performed based on a multi-objective optimization (MOO) approach. The model prediction is correlated with experimental studies.

scholarly journals Nonlinear parametric identification using periodic equilibrium states

1995 ◽  
Vol 7 (4) ◽  
pp. 499-515 ◽  
Author(s):  
G. Verbeek ◽  
A. De Kraker ◽  
D. H. Van Campen
Keyword(s):  
Parametric Identification ◽  
Equilibrium States ◽  
Nonlinear Parametric Identification
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