Optimal Synthesis of a Robomech: Procedure and Application

Author(s):  
Stephen Canfield ◽  
Giridhar Kolanupaka ◽  
Ahmad Smaili

Abstract This paper presents and compares two optimal synthesis techniques for direct application in creating a robomech-II, the second manipulator presented in a new class of linkage arms called Robomcchs. The first optimal synthesis approach will solve the problem as a nonlinear optimization, with a subset of the device parameters described in a linear system and solved directly in a least squares sense. The second approach will employ a least squares optimization using Lagrange Multipliers to contend with nonlinear constraints. In this paper, each optimal synthesis procedure is developed for the general case and then applied to robomcch-II through an example.

Author(s):  
John Locker ◽  
P. M. Prenter

AbstractLet L, T, S, and R be closed densely defined linear operators from a Hubert space X into X where L can be factored as L = TS + R. The equation Lu = f is equivalent to the linear system Tv + Ru = f and Su = v. If Lu = f is a two-point boundary value problem, numerical solution of the split system admits cruder approximations than the unsplit equations. This paper develops the theory of such splittings together with the theory of the Methods of Least Squares and of Collocation for the split system. Error estimates in both L2 and L∞ norms are obtained for both methods.


1999 ◽  
Vol 1 (2) ◽  
pp. 115-126 ◽  
Author(s):  
J. W. Davidson ◽  
D. Savic ◽  
G. A. Walters

The paper describes a new regression method for creating polynomial models. The method combines numerical and symbolic regression. Genetic programming finds the form of polynomial expressions, and least squares optimization finds the values for the constants in the expressions. The incorporation of least squares optimization within symbolic regression is made possible by a rule-based component that algebraically transforms expressions to equivalent forms that are suitable for least squares optimization. The paper describes new operators of crossover and mutation that improve performance, and a new method for creating starting solutions that avoids the problem of under-determined functions. An example application demonstrates the trade-off between model complexity and accuracy of a set of approximator functions created for the Colebrook–White formula.


2019 ◽  
Vol 57 (3) ◽  
pp. 1265-1277 ◽  
Author(s):  
Yexian Ren ◽  
Jie Yang ◽  
Lingli Zhao ◽  
Pingxiang Li ◽  
Zhiqu Liu ◽  
...  

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