Journal
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
Volume 49, Issue 12, Pages 2424-2437Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMC.2018.2827101
Keywords
Decentralized adaptive control; dynamic surface approximated inverse control; hysteresis; L-infinity performance; (1) large-scale system
Funding
- National Natural Science Foundation of China [61673101]
- Science and Technology Project of Jilin Province [20180201009SF, 20170414011GH, 20180201004SF, 20180101069JC]
- JSPS [C-15K06152, 14032011-000073]
- Grants-in-Aid for Scientific Research [15K06152] Funding Source: KAKEN
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This paper proposes a decentralized neural adaptive dynamic surface approximated inverse control (DNADSAIC) scheme for a class of large-scale time-delay systems with hysteresis nonlinearities as input. The decentralized control problem under the case only the outputs are measurable is solved by utilizing the radial basis function neural networks approximator and the hysteresis approximated inverse compensator. Also, with the help of finite covering lemma, the traditional Krasovskii functionals are dropped when coping with the delays, leading to the removal of the assumptions on the functions with time-delay states and the acquisition of the arbitrarily small L-infinity tracking performance of each hysteretic subsystem with time delays. The analysis of stabilities guarantees all the signals of the closed-loop systems are semiglobally uniformly ultimately bounded. Simulation results illustrate the efficiency of the proposed DNADSAIC scheme.
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