Identification and adaptive control of bipedal robot motion with artificial neural networks

Abstract

Artificial Neural Networks (ANNs) is one of the most popular fields of machine learning thanks to the critical improvements in the last decade, including their applications in the field of robotics and control. The important usage of neural networks in robotics makes it possible for robots to act and interact similar to humans. In this manner, legged robots are important platforms to mimick human locomotion. However, there are significant difficulties to apply system identification and control schemes for these hybrid dynamical structures. With this purpose, this thesis focuses on using artificial neural network-based novel techniques on these problems, for reaching to an efficient walking ability for bipedal robot systems like their counterparts in the nature. In this thesis, our work to find and apply our novel techniques is mainly divided into two parts. In the first part, inspired by a class of activation functions frequently used in deep learning literature, we propose a novel activation function and investigate its performance in various segmentation and classification tasks by using different well-known datasets. In the second part of the thesis, biped robot locomotion is chosen as the main topic. Separate datasets are created for three experiment configurations. For 2D and 3D simulations, locomotion control, system identification and adaptive control are applied with neural networks for successful periodical walking with low errors, having approximations of robot models and preparing for the adaptive learning using both control and identification blocks, respectively. For 2D physical robot system, system identification is completed for a walking dataset generated with varying speed levels. For all cases, proposed novel activation function DELU (ExtendeD Exponential Linear Unit) and its tuned functions are tried together with other activation functions in comparison, to reach better performances.