Contact-Force Distribution Optimization and Control for Quadruped Robots Using Both Gradient and Adaptive Neural Networks
- 18 December 2013
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Neural Networks and Learning Systems
- Vol. 25 (8), 1460-1473
- https://doi.org/10.1109/tnnls.2013.2293500
Abstract
This paper investigates optimal feet forces' distribution and control of quadruped robots under external disturbance forces. First, we formulate a constrained dynamics of quadruped robots and derive a reduced-order dynamical model of motion/force. Consider an external wrench on quadruped robots; the distribution of required forces and moments on the supporting legs of a quadruped robot is handled as a tip-point force distribution and used to equilibrate the external wrench. Then, a gradient neural network is adopted to deal with the optimized objective function formulated as to minimize this quadratic objective function subjected to linear equality and inequality constraints. For the obtained optimized tip-point force and the motion of legs, we propose the hybrid motion/force control based on an adaptive neural network to compensate for the perturbations in the environment and approximate feedforward force and impedance of the leg joints. The proposed control can confront the uncertainties including approximation error and external perturbation. The verification of the proposed control is conducted using a simulation.Keywords
Funding Information
- National Natural Science Foundation of China (61174045)
- International Science and Technology Cooperation Programme of China (2011DFA10950)
- National High Technology Research and Development Programme of China (2011AA040701)
- Fundamental Research Funds for the Central Universities (2013ZG0035)
- Program for New Century Excellent Talents in University (NCET-12-0195)
- Ph.D. Programs Foundation of Ministry of Education of China (20130172110026)
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