Model Predictive Control For Mobile Manipulators Based On Neural Dynamics(Extended version)

TL;DR

This paper introduces a neural dynamics-based model predictive control scheme for mobile manipulators that achieves precise, robust, and fast trajectory tracking with synchronized end-effector control.

Contribution

It develops a novel POMPTC scheme combined with neural dynamics and a non-singular fast terminal sliding mode for improved control performance.

Findings

Achieves finite-time convergence and high control accuracy.

Demonstrates robustness against disturbances and base motion effects.

Validates effectiveness through simulations and experiments.

Abstract

This article focuses on the trajectory tracking problem of mobile manipulators (MMs). Firstly, we construct a position and orientation model predictive tracking control (POMPTC) scheme for mobile manipulators. The proposed POMPTC scheme can simultaneously minimize the tracking error, joint velocity, and joint acceleration. Moreover, it can achieve synchronous control for the position and orientation of the end-effector. Secondly, a finite-time convergent neural dynamics (FTCND) model is constructed to find the optimal solution of the POMPTC scheme. Then, based on the proposed POMPTC scheme, a non-singular fast terminal sliding model (NFTSM) control method is presented, which considers the disturbances caused by the base motion on the manipulator at the dynamic level. It can achieve finite-time tracking performance and improve the anti-disturbances ability. Finally, simulation and experiments show that the proposed control method has the advantages of strong robustness, fast convergence, and high control accuracy.