Model Reference Adaptive Control and Fuzzy Neural Network Synchronous Motion Compensator for Gantry Robots

Abstract

A model reference adaptive control and fuzzy neural network (FNN) synchronous motion compensator for a gantry robot is presented in this paper. This paper proposes the development and application of gantry robots with MRAC and FNN online compensators. First, we propose a model reference adaptive controller (MRAC) under the cascade control method to make the reference model close to the real model and reduce tracking errors for the single axis. Then, a fuzzy neural network compensator for the gantry robot is proposed to compensate for the synchronous errors between the dual servo motors to improve precise movement. In addition, an online parameter training method is proposed to adjust the parameters of the FNN. Finally, the experimental results show that the proposed method improves the synchronous errors of the gantry robot and demonstrates the methodology in this paper. This study also successfully integrates the hardware and successfully verifies the proposed methods.

Development analysis of intelligent robots in manufacturing industry

“…Gantry robot [5] Composed of two parallel beams and longitudinal support columns, the structure is similar to a gantry crane.…”

A fractional-order quantum neural network: dynamics, finite-time synchronization

“…The finite -time generalized synchronization of fractional order systems and its application was employed in [24]. The dynamics of multi-scroll fractional-order system and its finite-time synchronization was focused in [25], and other synchronization methods were also introduced in [26][27][28][29]. However, all the synchronization objects mentioned above are classical chaotic systems and general neural networks, there are few researches on synchronization for perturbed fractional-order quantum-dot cellular neural networks with uncertainties and external disturbances via SMC technique.…”

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