Support vector machine based optimal control for minimizing energy consumption of biped walking motions

A kind of support vector machine based optimal control (SVM-OC) approach for minimizing energy consumption of biped walking motions is proposed in this work. Different from existing learning controllers, a SVM controller is incorporated into an optimal controller for biped robots, which aims at minimize an energy-related cost function with three constraints of biped walking robots, including the system dynamics in the single support phase, the system dynamics in the impact phase, and the initial state of the biped. The controller is deduced under the condition of small sample sizes for the SVM. Main contributions of this paper include two aspects: First, a SVM-OC problem for minimizing energy consumption of biped walking motions is defined, which provides new clues to design a kind of optimal controller under the condition of unknown system dynamic model and small sample sizes. Secondly, derivation of the proposed SVM-OC problem is provided in detail, and simulation results demonstrate the advantage of the proposed method compared with conventional SVM control methods and neural network (NN) control methods.