Dynamical Balance Optimization and Control of Quadruped Robots Under Perturbing External Force

This paper investigates the optimal feet forces distribution and control of quadruped robots under external disturbance forces. First, we formulate a constrained dynamic model 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 minimize the optimized objective function with linear equality and inequality constraints, and hybrid motion/force control based on adaptive neural network is used to compensate for the external perturbation in the environment and approximate feedforward force and impedance of the leg joints on line. The verification of the proposed control is conducted using the extensive simulations.