An enhanced time-delay control with improved accuracy for hybrid mechanism under significant payload variations and uncertainties

Purpose To address the tracking control problem for (n-degree of freedom) n-DoF hybrid robot mechanisms in the presence of disturbances, including payload variation, friction, uncertainties and so on, a new enhanced time-delay control (TDC)-based model-free robust control with a sliding mode control method and an adaptive gain tuning is presented in this paper. Design/methodology/approach To handle the system disturbances and uncertainties, a time-delay estimation (TDE) is designed to estimate the nonlinear terms in the robot’s dynamics, including unmodeled dynamics, disturbance torque and varying payload. An adaptive law based on a neuro-fuzzy control algorithm and sliding mode variable is introduced to update the TDE gain and improve the trajectory tracking accuracy of the system under varying payload and disturbance. In addition, a gradient compensation error correction term is added for robustness. The proposed algorithm consists of four intuitive terms: 1) a TDE, an inherited part of TDC; 2) a desired-error dynamics injection term; 3) a gradient compensation term; and 4) an adaptive gain control term based on a neuro-fuzzy algorithm. Findings The neuro-fuzzy was introduced to realize adaptability to changes in the mechanism’s dynamics; this is essential because of the robot’s encounter with varying conditions and uncertainties. Finally, comparative simulations and experiments are conducted on a hybrid mechanism used for automobile electrocoating transportation, and the results show that the proposed control method can achieve better tracking performance. Originality/value This is a model-free enhanced TDC with neuro-fuzzy-based adaptive gain dynamics for improved control of robot mechanisms in the presence of payload variation and uncertainties.