Model-Free Robotic Architecture with Task-Multiplexer and Discrete-Time Adaptive Control

Abstract

Robotic systems equipped with a task-multiplexer unit are considered as a class of unknown non-linear discrete-time systems, where the input is a command voltage of the driver unit and the output is the feedback signal obtained by the multiplexer unit. With only the input and output data available, an equivalent identification is formulated by a multi-input fuzzy rule emulated network. An online-learning algorithm is proposed to tune all adjustable parameters by using convergence analysis. Using the equivalent model, a controller is developed when the convergence of the tracking error and internal signals can be guaranteed. An experimental system validates the performance of the proposed scheme. Furthermore, the comparative results are also included, to demonstrate the advantage of the proposed controller.