Combined Online and Offline Inverse Dynamics Learning for a Robot Manipulator

Due to the approximation errors in dynamic model, changing payloads and dynamic disturbances acting on the system, the model based tracking is not satisfactory. Hence the application of real-time machine learning techniques in inverse dynamics learning has gained prominence for collaborative human robot interaction. In this paper, we propose a novel combined online and offline Neural Network based learning technique in conjunction with an acceleration tracker for inverse dynamics learning of a robot manipulator. This eliminates the need for explicit reliance on the approximate analytical robot model while controlling the robotic systems. The proposed approach can even capture the system dynamics accurately at higher acceleration where non-linear forces such as non-linear friction and damping play a prominent role. The performance of the proposed inverse dynamic model has been verified using extensive simulations on control of a 6 DOF UR5 robot manipulator in an accurate physics based Pybullet Simulator. The efficacy of the proposed method has been validated by comparing the control performance using model based backstepping controller. The results show that the inverse dynamics learning based controller outperforms significantly its counterpart in real world scenarios where uncertainties are ubiquitous.