Distributionally robust model predictive control for constrained robotic manipulators based on neural network modeling

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外文标题：Distributionally robust model predictive control for constrained robotic manipulators based on neural network modeling

外文摘要：A distributionally robust model predictive control(DRMPC)scheme is pro-posed based on neural network(NN)modeling to achieve the trajectory tracking control of robot manipulators with state and control torque constraints.First,an NN is used to fit the motion data of robot manipulators for data-driven dynamic modeling,convert-ing it into a linear prediction model through gradients.Then,by statistically analyzing the stochastic characteristics of the NN modeling errors,a distributionally robust model predictive controller is designed based on the chance constraints,and the optimization problem is transformed into a tractable quadratic programming(QP)problem under the distributionally robust optimization(DRO)framework.The recursive feasibility and con-vergence of the proposed algorithm are proven.Finally,the effectiveness of the proposed algorithm is verified through numerical simulation.

外文关键词：

robotic manipulatortrajectory tracking controlneural network(NN)distributionally robust optimization(DRO)model predictive control(MPC)

作者：

Yiheng YANG、Kai ZHANG、Zhihua CHEN、Bin LI

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作者单位：

College of Aeronautics and Astronautics,Sichuan University,Chengdu 610065,China

Robotic Satellite Laboratory of Sichuan Province,Sichuan University,Chengdu 610065,China

Beijing Institute of Control Engineering,Beijing 100190,China

关键词：

robotic manipulator trajectory tracking control neural network(NN) distributionally robust optimization(DRO) model predictive control(MPC)

出版年：

2024

DOI：

10.1007/s10483-024-3191-6

应用数学和力学(英文版)

上海大学

应用数学和力学(英文版)

影响因子：0.294

ISSN：0253-4827

年,卷(期)：2024.45(12)