Abstract
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.
| Original language | English |
|---|---|
| Journal | Neurocomputing |
| DOIs | |
| Publication status | Published - 2019 Jan 1 |
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Keywords
- Dead zone
- Fixed-time control
- Prescribed performance control
- Recurrent neural network control
- Uncertain nonlinear system
ASJC Scopus subject areas
- Computer Science Applications
- Cognitive Neuroscience
- Artificial Intelligence
Cite this
Prescribed performance fixed-time recurrent neural network control for uncertain nonlinear systems. / Ni, Junkang; Ahn, Choon Ki; Liu, Ling; Liu, Chongxin.
In: Neurocomputing, 01.01.2019.Research output: Contribution to journal › Article
}
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
PY - 2019/1/1
Y1 - 2019/1/1
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
UR - http://www.scopus.com/inward/record.url?scp=85069704341&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85069704341&partnerID=8YFLogxK
U2 - 10.1016/j.neucom.2019.07.053
DO - 10.1016/j.neucom.2019.07.053
M3 - Article
AN - SCOPUS:85069704341
JO - Neurocomputing
JF - Neurocomputing
SN - 0925-2312
ER -