Applied Mathematics and Mechanics >
Distributionally robust model predictive control for constrained robotic manipulators based on neural network modeling
Received date: 2024-07-12
Online published: 2024-11-30
Supported by
the National Natural Science Foundation of China(62273245);the National Natural Science Foundation of China(62173033);the Sichuan Science and Technology Program of China(2024NSFSC1486);the Opening Project of Robotic Satellite Key Laboratory of Sichuan Province of China;Project supported by the National Natural Science Foundation of China (Nos. 62273245 and 62173033), the Sichuan Science and Technology Program of China (No. 2024NSFSC1486), and the Opening Project of Robotic Satellite Key Laboratory of Sichuan Province of China
Copyright
A distributionally robust model predictive control (DRMPC) scheme is proposed 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, converting 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 convergence of the proposed algorithm are proven. Finally, the effectiveness of the proposed algorithm is verified through numerical simulation.
Yiheng YANG, Kai ZHANG, Zhihua CHEN, Bin LI . Distributionally robust model predictive control for constrained robotic manipulators based on neural network modeling[J]. Applied Mathematics and Mechanics, 2024 , 45(12) : 2183 -2202 . DOI: 10.1007/s10483-024-3191-6
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