Global stabilization of a chain of integrators with input saturation and disturbances: A new approach

Automatica ◽  
2012 ◽  
Vol 48 (7) ◽  
pp. 1389-1396 ◽  
Author(s):  
Shreekant Gayaka ◽  
Lu Lu ◽  
Bin Yao
1996 ◽  
Vol 61 (2) ◽  
pp. 288-297 ◽  
Author(s):  
Vladimír Pouzar ◽  
Ivan Černý

New approach to the preparation of steroids with connecting bridge, based on an O-carboxymethyloxime (CMO) structure, and with terminal hydroxy group, is presented. 17-CMO derivatives of 3β-acetoxy- and 3β-methoxymethoxyandrost-5-en-17-one were condensed with α,ω-amino alcohols to give derivatives with a chain of seven to nine atoms. After THP-protection, these compounds were converted to 3-keto-4-ene derivatives. An alternative synthesis consisted in transformation of 17-CMO derivatives with bonded amino acids by reduction of the terminal carboxyl. The resulting compounds were designed as building blocks for the preparation of bis-haptens for sandwich immunoassays.


Author(s):  
Hiromasa Kato ◽  
Ken-ichi Funazaki

A new approach for adaptively sampling a design parameter space using an error estimate through the reconstruction of flow field by a combination of proper orthogonal decomposition (POD) and radial basis function network (RBFN) is presented. It differs from other similar approaches in that it does not use the reconstructed flow field by POD for the evaluation of objective functions, and thus it can be a subset of the flow field. Advantages of this approach include the ease of constructing a chain of simulation codes as well as the flexibility of choosing where and what to reconstruct within the solution domain. An improvement in achieving a good prediction quality, with respect to other adaptive sampling methods, has been demonstrated using supersonic impulse turbine optimization as the test case. A posteriori validation of the surrogate models were also carried out using a set of separately-evaluated samples, which showed a similar trend as the Leave-One-Out (LOO) cross-validation. The progressively enriched surrogate model was then used to achieve the more uniformly populated Pareto front with fewer number of function evaluations.


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