scholarly journals Phase retrieval with transverse translation diversity: a nonlinear optimization approach

2008 ◽  
Vol 16 (10) ◽  
pp. 7264 ◽  
Author(s):  
Manuel Guizar-Sicairos ◽  
James R. Fienup
Keyword(s):  
Nonlinear Optimization ◽  
Phase Retrieval ◽  
Optimization Approach

Sensitivity of Muscle Force Estimations to Changes in Muscle Input Parameters Using Nonlinear Optimization Approaches

1992 ◽  
Vol 114 (2) ◽  
pp. 267-268 ◽  
Author(s):  
Walter Herzog
Keyword(s):  
Nonlinear Optimization ◽  
Muscle Force ◽  
Anatomical Variations ◽  
Degree Of Freedom ◽  
Optimization Approach ◽  
Force Sharing ◽  
Input Parameters ◽  
Synergistic Muscles

The purpose of this study was to analyze the sensitivity of muscle force calculations to changes in muscle input parameters. Force sharing between two synergistic muscles was derived analytically for a one-degree-of-freedom system using three nonlinear optimization approaches. Changes in input parameters that are within normal anatomical variations often caused changes in muscular forces exceeding 100 percent. These results indicate that errors in muscle force calculations may depend as much on inadequate muscle input parameters as they may on the choice of the objective and constraint functions of the optimization approach.

Parameter Identification of a Friction Model for Robotic Joints

2010 ◽  
Author(s):  
Mohammad Vakil ◽  
Reza Fotouhi ◽  
Peter N. Nikiforuk
Keyword(s):  
Nonlinear Optimization ◽  
Controller Design ◽  
Static Friction ◽  
Friction Model ◽  
New Method ◽  
Optimization Approach ◽  
Mass Moment ◽  
Optimization Parameters ◽  
Robotic Joints ◽  
Friction Parameters

In this paper a new identification method to obtain the friction parameters in the joints of robotic manipulators is presented. These parameters are coulomb friction, static friction, Stribeck velocity constant and viscous damping coefficient. The available methods to find these parameters either require the design of a controller or the precise value of system parameters such as mass moment of inertia. In contrast, the new method proposed here finds these parameters by a nonlinear optimization approach which requires neither any knowledge of system’s parameters nor any controller design. The corresponding nonlinear optimization problem is solved using an efficient technique which does not require iteration or any initial estimate of optimization parameters. The new method proposed in this paper was experimentally verified on a robotic manipulator available in the robotics laboratory at the University of Saskatchewan.

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