A complex-valued neural dynamical optimization approach and its stability analysis

2015 ◽  
Vol 61 ◽  
pp. 59-67 ◽  
Author(s):  
Songchuan Zhang ◽  
Youshen Xia ◽  
Weixing Zheng
Keyword(s):  
Stability Analysis ◽  
Optimization Approach ◽  
Dynamical Optimization ◽  
Complex Valued

Dissipativity and stability analysis of fractional-order complex-valued neural networks with time delay

2017 ◽  
Vol 86 ◽  
pp. 42-53 ◽  
Author(s):  
G. Velmurugan ◽  
R. Rakkiyappan ◽  
V. Vembarasan ◽  
Jinde Cao ◽  
Ahmed Alsaedi
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Time Delay ◽  
Fractional Order ◽  
Order Complex ◽  
Complex Valued

Maximizing Walking Step Length for a Near Omni-Directional Hexapod Robot

2004 ◽  
Author(s):  
James P. Schmiedeler ◽  
Nathan J. Bradley ◽  
Brett Kennedy
Keyword(s):  
Stability Analysis ◽  
Step Length ◽  
Static Stability ◽  
Optimization Approach ◽  
Computationally Efficient ◽  
Constant Height ◽  
Planning Algorithm ◽  
Two Phases ◽  
The Stability ◽  
Path Planning Algorithm

A foot path planning algorithm is presented for a robot with six limbs symmetrically located on the faces of its hexagonal body, enabling it to walk at a constant height with an alternating tripod gait. The symmetry results in near omni-directional locomotion capability, so the algorithm is formulated for walking in any direction and at any height. The approach is to determine the maximum length foot path through each limb’s workspace and then modify those foot paths based upon static stability analysis. The stability analysis is conducted in two phases to ensure stability without excessively reducing step length. Compared to an optimization approach, the algorithm yields foot paths within 9.1% of the maximal foot paths for all directions and heights. Unlike the optimization approach, the developed algorithm is computationally efficient enough to be implemented in realtime.

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