Iterative learning control for path tracking of service robot in perspective dynamic system with uncertainties

Wang Yugang,Yin Lei,Zhao Yang,Zhou Fengyu,Li Ming

doi:10.1177/1729881420968528

Wang Yugang, Yin Lei + Show 3 more

Open Access

https://doi.org/10.1177/1729881420968528

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Abstract

A novel iterative learning control (ILC) for perspective dynamic system (PDS) is designed and illustrated in detail in this article to overcome the uncertainties in path tracking of mobile service robots. PDS, which transmits the motion information of mobile service robots to image planes (such as a camera), provides a good control theoretical framework to estimate the robot motion problem. The proposed ILC algorithm is applied in accordance with the observed motion information to increase the robustness of the system in path tracking. The convergence of the presented learning algorithm is derived as the number of iterations tends to infinity under a specified condition. Simulation results show that the designed framework performs efficiently and satisfies the requirements of trajectory precision for path tracking of mobile service robots.

Highlights

With the rapid development of techniques for mobile service robots and the urgent demand from society, mobile service mobile robots have a wide range of applications in diverse areas.[1]
Iterative learning control (ILC) for path tracking of mobile service robots with multiple uncertainties, which hinders the applications of service robots in practice, has gained increasing attention in recent years.[25,26]
In contrast to earlier studies, the current study adopts perspective dynamic system (PDS) and ILC to enhance the performance of path tracking of mobile service robots

Summary

Introduction

With the rapid development of techniques for mobile service robots and the urgent demand from society, mobile service mobile robots have a wide range of applications in diverse areas.[1]. ILC for path tracking of mobile service robots with multiple uncertainties, which hinders the applications of service robots in practice, has gained increasing attention in recent years.[25,26] To solve these obstacles, Li et al.[27] presented adaptive ILC law for a class of discrete-time systems with iteration-varying trajectory and random initial condition. Adaptive ILC and data-driven adaptive ILC were applied by Chi et al.[31,32] for a class of nonlinear discrete-time systems with random initial states and iteration-varying target trajectory All of these works provided a theoretical foundation for solving the multiple uncertainties in path tracking of mobile service robots and applying such robots in practice. Some conclusions and future works are provided in the sixth section

Background

Each trajectory is considered for a fixed finite time

À eÀlT ð19Þ þ b2 l k u0ðtÞ À ukðtÞkl

Conclusions and future work

Declaration of conflicting interests