A mixed-kernel-based SVR controller for biped robots

Aiming at the stable walking control problem in the dynamic environments for biped robots, this paper puts forward a method of gait control based on support vector machine(SVM), which provides a solution for the learning control issue based on small sample sizes. Using ankle trajectory and hip trajectory as inputs, and the corresponding trunk trajectory, which guarantees the ZMP criterion as outputs, the SVM is trained based on small sample sizes to learn the dynamic kinematics relationships between the legs and the trunk of the biped robots. The trained SVM is incorporated into the control system of the robots. Robustness of the gait control is enhanced, which is propitious to realize the stable biped walking. Simulation results demonstrate the superiority of the proposed methods.