Multilayered hybrid time-varying problem solving based on integrated-enhanced zeroing neural network for robust manipulator control

Abstract

Robustness is a significant research direction in manipulator control owing to their complicated and uncertain external environment, abrasion, and other factors. The ability to implement multitasking is also necessary for manipulator control because of the physical limitations and complex requirements. However, the existing research has mainly focused on the control of a single task and robustness analysis of single-task control. Although some research on multi-task control has been conducted recently, its robustness has not yet been studied. Because of the excellent performance of the integrated-enhanced zeroing neural network in terms of robustness for time-varying problem solving, it was employed in this study to solve robust multi-task control. First, the multi-task control was formulated as a two-layered time-varying problem, including nonlinear and hybrid linear equations describing the tracking task and additional tasks, respectively. Second, an integrated-enhanced zeroing neural network was employed for the multilayered time-varying problem solving and a robust multi-task control algorithm was obtained, which can suppress different types of noises. Theoretical analyses demonstrated its effectiveness in multitasking and superior robustness compared with conventional algorithms. Finally, simulation results verified the theoretical results.