A Fixed-Time Robust ZNN Model With Adaptive Parameters for Redundancy Resolution of Manipulators

Abstract

Due to the excellent time-varying problem-solving capability of zeroing neural network (ZNN), many redundancy resolution schemes based on ZNN have been proposed for robots. The work proposes a fixed-time robust ZNN (FTRZNN) model with adaptive parameters to effectively address redundancy resolution problems of robots in the presence of noises. Differing from existing ZNN models, the FTRZNN possesses a fixed-time activation function and two adaptive parameters, which greatly improve its performance on convergence speed and robustness. The establishment of the FTRZNN for handling redundancy resolution problems consists of two steps: 1) converting the target practical problem into nonlinear equations firstly; and 2) deriving an FTRZNN for solving the equations. For providing a convincible evidence of the significant advantages of the FTRZNN over existing ZNN models, theoretical analysis in convergence and robustness of the FTRZNN is given, and the performance of the FTRZNN model is compared with existing ZNN models when performing path tracking tasks using a 6R manipulator under different noise disturbances. Finally, the FTRZNN model is employed to control two robot manipulators (UR5 and Jaco) to track desired paths under noise interference, which is simulated on a robotic simulation platform (i.e.,CoppeliaSim). Simulation results indicate the effectiveness and potential practical value of the FTRZNN model.