Experimental study on a novel simultaneous control and identification of a 3-DOF delta robot using model reference adaptive control

Abstract

Delta robot is one of the most widely used parallel robots in the robotics industry. Therefore, it is vital to employ appropriate methods to control Delta robot in order to perform a specific task. Modeling parallel robots is a complex and time consuming task where model-based methods which are mainly dependent to accurate modeling, lose their performance in confronting with model uncertainties and disturbances. In this paper, a novel simultaneous control-and-identification structure is proposed for Multi-Input Multi-Output (MIMO) time-varying systems with unknown parameters which guarantees closed-loop stability and simultaneous identification in presence of model uncertainties and disturbances. Therefore, the unknown parameters of system can be acquired without any prior knowledge from the outset; thus, the suggested method becomes more attractive for robotics system having sophisticated models. For the purpose of this paper, the proposed algorithm is practically implemented on a 3 Degrees-of-Freedom (DOF) Delta parallel robot. The result of simulation and practical implementation are compared with three well known methods which have been previously implemented on the robot, namely PID, adaptive method, and sliding mode control algorithm. In this regard, the results both in simulation and practical implementation reveal remarkable performance for the suggested method in compare with other aforementioned algorithms.