Adaptive Neural Network Control of Biped Robots
针对双足机器人的平衡与姿态控制，设计了基于径向基函数的神经网络控制策略，用神经网络逼近未知模型，并证明了闭环系统轨迹的半全局一致有界性。
In this paper, neural network control strategies based on radial basis functions are designed for biped robots, which includes balancing and posture control. To deal with system uncertainties, neural networks are used to approximate the unknown model of the robot. Both full state feedback control and output feedback control are considered in this paper. With the proposed control, the trajectories of the closed-loop system are semiglobally uniformly bounded which can be proved via Lyapunov stability theorem. Simulations are also carried out to illustrate the effectiveness of the proposed control.