A noise suppression zeroing neural network for trajectory tracking with joint angle constraints of mobile manipulator

The trajectory tracking control (TTC) is an indispensable part in mobile manipulator (MM) application. The actual usage of the MM can be affected by factors, such as external noise interference and joint constraints. However, most of the current researches on the control of the MM only consider one of these factors. Herein, this paper presents a noise suppression zeroing neural network with joint angle constraints (NSZNN-JAC) model guided by theoretical analysis to solve the TTC problem of MM with both noise interference and joint angle constraints. The TTC problem with joint angle constraints can be transformed into time-varying nonlinear equations (TVNE) problem. The theoretical analyses verify that the NSZNN-JAC model is able to maintain convergence in the noise interference. The effectiveness and superiority of the NSZNN-JAC model are demonstrated by comparison in simulations. Moreover, the NSZNN-JAC model is applied to a physical platform which can substantiate that it is capable of performing the TTC task in the real platform.