Data-Driven Adaptive Modelling and Control for a Class of Discrete-Time Robotic Systems Based on a Generalized Jacobian Matrix Initialization

Abstract

Data technology advances have increased in recent years, especially for robotic systems, in order to apply data-driven modelling and control computations by only considering the input and output signals’ relationship. For a data-driven modelling and control approach, the system is considered unknown. Thus, the initialization values of the system play an important role to obtain a suitable estimation. This paper presents a methodology to initialize a data-driven model using the pseudo-Jacobian matrix algorithm to estimate the model of a mobile manipulator robot. Once the model is obtained, a control law is proposed for the robot end-effector position tasks. To this end, a novel neuro-fuzzy network is proposed as a control law, which only needs to update one parameter to minimize the control error and avoids the chattering phenomenon. In addition, a general stability analysis guarantees the convergence of the estimation and control errors and the tuning of the closed-loop control design parameters. The simulations results validate the performance of the data-driven model and control.