This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Abdelatti M, Yuan C Z, Zeng W, et al. Cooperative deterministic learning control for a group of homogeneous nonlinear uncertain robot manipulators. Sci China Inf Sci, 2018, 61: 112201
Gao Y F, Sun X M, Wang W. Decentralized backstepping adaptive output tracking of large-scale stochastic nonlinear systems. Sci China Inf Sci, 2017, 60: 120207
Park G, Shim H. Guaranteeing almost fault-free tracking performance from transient to steady-state: a disturbance observer approach. Sci China Inf Sci, 2018, 61: 070224
Li Y M, Tong S C. Adaptive fuzzy output-feedback stabilization control for a class of switched nonstrict-feedback nonlinear systems. IEEE Trans Cybern, 2017, 47: 1007–1016
Liu Y J, Gao Y, Tong S C, et al. A unified approach to adaptive neural control for nonlinear discrete-time systems with nonlinear dead-zone input. IEEE Trans Neural Netw Learn Syst, 2016, 27: 139–150
Chen M, Shao S Y, Jiang B. Adaptive neural control of uncertain nonlinear systems using disturbance observer. IEEE Trans Cybern, 2017, 47: 3110–3123
Han J Q, Yuan L L. The discrete form of tracking-differentiator. J Syst Sci Math Sci, 1999, 19: 268–273
Zhang Y M, Chamseddine A, Rabbath C A, et al. Development of advanced FDD and FTC techniques with application to an unmanned quadrotor helicopter testbed. J Franklin Inst, 2013, 350: 2396–2422
This work was supported by National Natural Science Foundation of China (Grant No. 61573184) and Jiangsu Innovation Program for Graduate Education (Grant No. KYLX16_0375).
About this article
Cite this article
Shao, S., Chen, M. Sliding-mode-disturbance-observer-based adaptive neural control of uncertain discrete-time systems. Sci. China Inf. Sci. 63, 149204 (2020). https://doi.org/10.1007/s11432-018-9574-3