Model reference adaptive control based time delay estimation with RBF neural network for robot manipulators

In this paper, model-reference adaptive control (MRAC) with neural network (NN) and time delay estimation (TDE) is proposed for controlling a robotic manipulator. With more than two degrees of freedom (DoF) of the robot, the formulation of a known regression matrix is tedious and also difficult to compute for the different robotic systems. Therefore, this work introduces MRAC based on TDE with NN (MRAC-NNTDE) to achieve high-control performance without prior knowledge of the regression matrix and offers a model free scheme. Firstly, MRAC is applied to adjust the control gains, then TDE is implemented to estimate the unknown dynamical robotic system, and NN is employed to deal with the TDE estimation error. The overall stability of the robotic dynamics is investigated using the Lyapunov theorem. In the end, computer simulations are compared to validate the effectiveness of the proposed scheme.