Synchronization of multi-links systems with Lévy noise and application

ABSTRACT

In previous work, the synchronization of multi-links systems with Lévy noise (MLSLN) has not been fully investigated. Therefore, the synchronization of MLSLN is concerned via feedback discrete-time observations control. Therein, we provide a novel Lyapunov functional for MLSLN. Moreover, the upper bound of the state observations interval duration is obtained. By making use of Lyapunov functional method, some techniques of inequality and graph theory, we derive some sufficient conditions to guarantee the mean-squared asymptotical synchronization of MLSLN. Then, the theoretical results are employed to study the synchronization of single-link robot arms. Meantime, numerical simulations are given to demonstrate the effectiveness of the developed results.

Keywords:

• Lévy noise
• multi-links systems
• discrete-time state observations
• single-link robot arms

AMS Subject Classifications:

• 39A50
• 93E03
• 93C55
• 93D05

Acknowledgments

The authors really appreciate the editor's and reviewers' valuable comments. This work was supported by the Shandong Province Natural Science Foundation (Nos. ZR2018MA005, ZR2018MA020, ZR2017MA008); the Key Project of Science and Technology of Weihai (No. 2014DXGJMS08), the Innovation Technology Funding Project in Harbin Institute of Technology (No. HIT.NSRIF.201703), the Science and Technology Program of Shenzhen, China (No. JCYJ20170818091621856) and the National Science Foundation of China (No. 61872429).

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Shandong Province Natural Science Foundation [grant numbers ZR2018MA005, ZR2018MA020, ZR2017MA008]; the Key Project of Science and Technology of Weihai [grant number 2014DXGJMS08], the Innovation Technology Funding Project in Harbin Institute of Technology (No. HIT.NSRIF.201703), the Science and Technology Program of Shenzhen, China (No. JCYJ20170818091621856) and the National Science Foundation of China [grant number 61872429].