Locally Weighted Learning Control for Dynamic Restricted Manipulators

Authors

Wang Gang, 1. School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China; ;
Tairen Sun, 1. School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China; ;
Shengpei Ding, 2. Guangdong University of Technology, Guangzhou 510006, China;

Abstract

Abstract: This paper proposes a locally weighted learning control law for a manipulator with state and input constraints and modeling uncertainties. By visualizing the control input as an extended state, the control problem is converted into control design for a state-constraint uncertain nonlinear system. Barrier Lyapunov functions are introduced into a backstepping procedure and a locally weighted learning control is designed, which ensures the exponential convergence of the barrier functions to a small neighborhood of zero and then guarantees satisfaction of system constraints and the tracking error convergence. The control feasibility and effectiveness is validated by theoretical analysis and simulation results.

Recommended Citation

Gang, Wang; Sun, Tairen; and Ding, Shengpei (2019) "Locally Weighted Learning Control for Dynamic Restricted Manipulators," Journal of System Simulation: Vol. 31: Iss. 4, Article 17.
DOI: 10.16182/j.issn1004731x.joss.17-0133
Available at: https://dc-china-simulation.researchcommons.org/journal/vol31/iss4/17

First Page

733

Revised Date

2017-04-21

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.17-0133

Last Page

739

CLC

TP241

Recommended Citation

Wang Gang, Sun Tairen, Ding Shengpei. Locally Weighted Learning Control for Dynamic Restricted Manipulators[J]. Journal of System Simulation, 2019, 31(4): 733-739.

DOI

10.16182/j.issn1004731x.joss.17-0133