Adaptive dynamic control of a bipedal walking robot with radial basis function neural networks

The robustness of biped walking can be enhanced by the use of adaptive control and learning. The paper describes one such approach, radial basis function (RBF) neural network adaptive control (NNAC). The adaptive control mechanism is designed in a virtual space utilizing the virtual model control paradigm. The neural network is parameterized and trained in an unsupervised learning mode. There are two advantages to this approach. First, the NNAC can identify the unmodelled dynamics of the robot and ensure asymptotic system stability in a Lyapunov sense. Second, the controller can better accommodate unexpected external disturbances. The system's design is described and simulation results are presented.