Dynamical balance optimization and control of biped robots in double-support phase under perturbing external forces - Wang, Liyang; Ge, Yongyong; Chen, Ming; Fan, Yongqing (Neural computing & applications, 2017)
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.