Dynamical balance optimization and control of biped robots in double-support phase under perturbing external forces

To realize the dynamic balance optimization and control of biped robots under the perturbing external forces in the double-support phase, a systematic scheme is proposed in this paper. First, a constrained dynamic model of biped robots and a reduced order dynamical model for the double-support phase...

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Vydáno v:Neural computing & applications Ročník 28; číslo 12; s. 4123 - 4137
Hlavní autoři: Wang, Liyang, Ge, Yongyong, Chen, Ming, Fan, Yongqing
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Springer London 01.12.2017
Springer Nature B.V
Témata:
Adaptive algorithms
Adaptive learning
Artificial Intelligence
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Computer simulation
Contact force
Data Mining and Knowledge Discovery
Energy conservation
Force distribution
Image Processing and Computer Vision
Machine learning
Optimization
Original Article
Probability and Statistics in Computer Science
Recurrent neural networks
Reduced order models
Robot control
Robots
Stress concentration
Biped robots
Balance optimization
Motion/force control
Dynamic force distribution
ISSN:0941-0643, 1433-3058
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Shrnutí:To realize the dynamic balance optimization and control of biped robots under the perturbing external forces in the double-support phase, a systematic scheme is proposed in this paper. First, a constrained dynamic model of biped robots and a reduced order dynamical model for the double-support phase are formulated. Considering the dynamic external wrench applied on biped robots, we present a dynamic force distribution approach based on quadratic objective function for computing the optimal contact forces to equilibrate the dynamic external wrench. As a result, the sum of the normal force components is minimized for enhancing safety and energy saving. Then, one primary recurrent neural network (RNN) is adopted to solve the optimization problem subject to both equality and inequality constraints. For the derived optimized contact force and motion, hybrid motion/force control is proposed based on another RNN to approximate unknown dynamic functions. Adaptive learning algorithms for learning the parameters of the RNN are provided as well. The proposed control can deal with the uncertainties including approximation errors and external disturbances. Extensive simulations are presented to demonstrate the effectiveness of the proposed optimization and control approach.
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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-016-2316-6