Analysis of exit probability for a trajectory tracking robot in case of a rare event

被引：0

作者：

Rana, Rohit ^{[1
]}

Gaur, Prerna ^{[1
]}

Agarwal, Vijyant ^{[2
]}

Parthasarathy, Harish ^{[3
]}

机构：

[1] Netaji Subhas Univ Technol, Instrumentat & Control Engn Dept, New Delhi, India

[2] Netaji Subhas Univ Technol, Mech Engn Dept, New Delhi, India

[3] Netaji Subhas Univ Technol, Elect & Commun Engn Dept, New Delhi, India

来源：

ROBOTICA | 2022年 / 40卷 / 04期

关键词：

nonlinear dynamics; large deviation theory; Poisson process; Gaussian noise; robot; probability; robotics; LARGE DEVIATION PRINCIPLE; STATE OBSERVER; SYSTEMS;

D O I：

10.1017/S0263574721000916

中图分类号：

TP24 [机器人技术];

学科分类号：

080202 ; 1405 ;

摘要：

In this paper, a novel statistical application of large deviation principle (LDP) to the robot trajectory tracking problem is presented. The exit probability of the trajectory from stability zone is evaluated, in the presence of small-amplitude Gaussian and Poisson noise. Afterward, the limit of the partition function for the average tracking error energy is derived by solving a fourth-order system of Euler-Lagrange equations. Stability and computational complexity of the proposed approach is investigated to show the superiority over the Lyapunov method. Finally, the proposed algorithm is validated by Monte Carlo simulations and on the commercially available Omni bundle(TM) robot.

引用

页码：907 / 932

页数：26

共 27 条

[1] Disturbance estimator as a state observer with extended Kalman filter for robotic manipulator [J].