scholarly journals Robotic Invention: Challenges and Perspectives for Model-Free Design Optimization of Dynamic Locomotion Robots

2017 ◽  
pp. 581-596 ◽  
Author(s):  
Luzius Brodbeck ◽  
Simon Hauser ◽  
Fumiya Iida
Keyword(s):  
Design Optimization ◽  
Model Free

scholarly journals Model-Free Design Optimization of a Hopping Robot and Its Comparison With a Human Designer

2018 ◽  
Vol 3 (2) ◽  
pp. 1245-1251 ◽  
Author(s):  
Kaur Aare Saar ◽  
Fabio Giardina ◽  
Fumiya Iida
Keyword(s):  
Design Optimization ◽  
Model Free ◽  
Hopping Robot ◽  
Human Designer

Representation, abstraction, and simple-minded sophisticates

2020 ◽  
Vol 43 ◽  
Author(s):  
Peter Dayan
Keyword(s):  
Decision Theory ◽  
Predictive Coding ◽  
Bayesian Decision Theory ◽  
Bayesian Decision ◽  
Model Based ◽  
Model Free

Abstract Bayesian decision theory provides a simple formal elucidation of some of the ways that representation and representational abstraction are involved with, and exploit, both prediction and its rather distant cousin, predictive coding. Both model-free and model-based methods are involved.

RFID Protocol Design, Optimization, and Security for the Internet of Things

2017 ◽  
Author(s):  
Alex X. Liu ◽  
Muhammad Shahzad ◽  
Xiulong Liu ◽  
Keqiu Li
Keyword(s):  
Internet Of Things ◽  
Design Optimization ◽  
Protocol Design ◽  
The Internet ◽  
The Internet Of Things ◽  
Rfid Protocol

Rubber Composition–Properties Relationships during Tire Numerical Simulation and Design Optimization

2017 ◽  
Vol 45 (1) ◽  
pp. 71-84 ◽  
Author(s):  
Alexey Mazin ◽  
Alexander Kapustin ◽  
Mikhail Soloviev ◽  
Alexander Karanets
Keyword(s):  
Numerical Simulation ◽  
Design Optimization ◽  
Strain Tensor ◽  
General Purpose ◽  
Orthogonal Functions ◽  
Element Analysis ◽  
Time Investment ◽  
Footprint Analysis ◽  
Composition Properties ◽  
Nonlinear Elastic

ABSTRACT Numerical simulation based on finite element analysis is now widely used during the design optimization of tires, thereby drastically reducing the time investment in the design process and improving tire performance because it is obtained from the optimized solution. Rubber material models that are used in numerical calculations of stress–strain distributions are nonlinear and may include several parameters. The relations of these parameters with rubber formulations are usually unknown, so the designer has no information on whether the optimal set of parameters is reachable by the rubber technological possibilities. The aim of this work was to develop such relations. The most common approach to derive the equation of the state of rubber is based on the expansion of the strain energy in a series of invariants of the strain tensor. Here, we show that this approach has several drawbacks, one of which is problems that arise when trying to build on its basis the quantitative relations between the rubber composition and its properties. An alternative is to use a series expansion in orthogonal functions, thereby ensuring the linear independence of the coefficients of elasticity in evaluation of the experimental data and the possibility of constructing continuous maps of “the composition to the property.” In the case of orthogonal Legendre polynomials, the technique for constructing such maps is considered, and a set of empirical functions is proposed to adequately describe the dependence of the parameters of nonlinear elastic properties of general-purpose rubbers on the content of the main ingredients. The calculated sets of parameters were used in numerical tire simulations including static loading, footprint analysis, braking/acceleration, and cornering and also in design optimization procedures.

Model-free Learning for Safety-critical Control Systems: A Reference Governor Approach

2020 ◽  
Author(s):  
Kaiwen Liu ◽  
Nan Li ◽  
Ilya Kolmanovsky ◽  
Denise Rizzo ◽  
Anouck Girard
Keyword(s):  
Control Systems ◽  
Safety Critical ◽  
Model Free ◽  
Reference Governor
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