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EAAI
2007

Nearly optimal neural network stabilization of bipedal standing using genetic algorithm

9 years 2 months ago
Nearly optimal neural network stabilization of bipedal standing using genetic algorithm
In this work, stability control of bipedal standing is investigated. The biped is simplified as an inverted pendulum with a foot-link. The controller consists of a general regression neural network (GRNN) feedback control, which stabilizes the inverted pendulum in a region around the upright position, and a PID feedback control, which keeps the pendulum at the upright position. The GRNN controller is also designed to minimize an energy-related cost function while satisfying the constraints between the foot-link and the ground. The optimization has been carried out using the genetic algorithm (GA) and the GRNN is directly trained during optimization iteration process to provide the closed loop feedback optimal controller. The stability of the controlled system is analyzed using the concept of Lyapunov exponents, and a stability region is determined. Simulation results show that the controller can keep the inverted pendulum at the upright position while nearly minimizing an energy-rela...
Reza Ghorbani, Qiong Wu, G. Gary Wang
Added 13 Dec 2010
Updated 13 Dec 2010
Type Journal
Year 2007
Where EAAI
Authors Reza Ghorbani, Qiong Wu, G. Gary Wang
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