TY - JOUR
T1 - Prescribed performance fixed-time recurrent neural network control for uncertain nonlinear systems
AU - Ni, Junkang
AU - Ahn, Choon Ki
AU - Liu, Ling
AU - Liu, Chongxin
N1 - Funding Information:
This work was supported by the Fundamental Research Funds for the Central Universities under grant 31020180QD076 and the Natural Science Basic Research Plan in Shaanxi Province of China under grant 2019JQ-035 .
PY - 2019/10/21
Y1 - 2019/10/21
N2 - This paper investigates fixed-time prescribed performance control problem for uncertain strict-feedback nonlinear systems with unknown dead zone. First, a novel prescribed performance function (PPF) is proposed and a coordinate transformation is employed to transform the prescribed performance constrained system into an unconstrained one. Next, recurrent neural network is introduced to estimate the uncertain dynamics and fixed-time differentiator is utilized to obtain the derivative of virtual control. Then, a fixed-time dynamic surface control is developed to deal with dead zone and guarantee the convergence of the tracking error within a fixed time. Lyapunov stability analysis shows that the presented control scheme can achieve the fixed-time convergence of the error variables, while the other closed-loop system signals are bounded. Finally, numerical simulation validates the effectiveness of the presented control scheme.
AB - This paper investigates fixed-time prescribed performance control problem for uncertain strict-feedback nonlinear systems with unknown dead zone. First, a novel prescribed performance function (PPF) is proposed and a coordinate transformation is employed to transform the prescribed performance constrained system into an unconstrained one. Next, recurrent neural network is introduced to estimate the uncertain dynamics and fixed-time differentiator is utilized to obtain the derivative of virtual control. Then, a fixed-time dynamic surface control is developed to deal with dead zone and guarantee the convergence of the tracking error within a fixed time. Lyapunov stability analysis shows that the presented control scheme can achieve the fixed-time convergence of the error variables, while the other closed-loop system signals are bounded. Finally, numerical simulation validates the effectiveness of the presented control scheme.
KW - Dead zone
KW - Fixed-time control
KW - Prescribed performance control
KW - Recurrent neural network control
KW - Uncertain nonlinear system
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U2 - 10.1016/j.neucom.2019.07.053
DO - 10.1016/j.neucom.2019.07.053
M3 - Article
AN - SCOPUS:85069704341
VL - 363
SP - 351
EP - 365
JO - Neurocomputing
JF - Neurocomputing
SN - 0925-2312
ER -