Adaptive neural dynamic surface control of output constrained non-linear systems with unknown control direction

This study investigates the adaptive neural dynamic surface control (DSC) of output constrained non-linear systems, subject to unknown system dynamics and uncertain control direction. A Nussbaum-type dynamic gain algorithm is used to handle the effect of unknown control direction. Integral barrier L...

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Published inIET control theory & applications Vol. 11; no. 17; pp. 2994 - 3003
Main Authors Zhang, Sainan, Tang, Zhongliang, Ge, Shuzhi Sam, He, Wei
Format Journal Article
LanguageEnglish
Published The Institution of Engineering and Technology 24.11.2017
Subjects
adaptive control
adaptive neural dynamic surface control
closed loop systems
closed‐loop system
constraint range
control system synthesis
DSC technology
iBLF
initial output signals
Integral barrier Lyapunov functions
Lyapunov methods
Lyapunov synthesis
neurocontrollers
nonlinear control systems
Nussbaum‐type dynamic gain algorithm
output constrained nonlinear systems
output constraint
parametric uncertainties
pure tracking error‐based barrier Lyapunov function
Research Article
semiglobal uniformly ultimately bounded control
uncertain control direction
uncertain systems
unknown system dynamics
Nussbaum-type dynamic gain algorithm
iBLF
uncertain systems
semiglobal uniformly ultimately bounded control
parametric uncertainties
adaptive control
control system synthesis
constraint range
Lyapunov synthesis
Integral barrier Lyapunov functions
closed loop systems
adaptive neural dynamic surface control
initial output signals
neurocontrollers
uncertain control direction
output constrained nonlinear systems
nonlinear control systems
DSC technology
pure tracking error-based barrier Lyapunov function
output constraint
unknown system dynamics
closed-loop system
Lyapunov methods
Online AccessGet full text
ISSN1751-8644
1751-8652
DOI10.1049/iet-cta.2017.0556

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Abstract This study investigates the adaptive neural dynamic surface control (DSC) of output constrained non-linear systems, subject to unknown system dynamics and uncertain control direction. A Nussbaum-type dynamic gain algorithm is used to handle the effect of unknown control direction. Integral barrier Lyapunov functions (iBLFs) are directly utilised to tackle the effect of output constraint. The prominent feature of iBLFs is that the feasible initial output signals are relaxed to the whole constraint range compared with pure tracking errors-based barrier Lyapunov function. Also, the DSC technology is developed to avoid the explosion of complexity in traditional control design, and adaptive neural networks are adopted to estimate the uncertainty that comprises the unknown system dynamics and parametric uncertainties. By utilising Lyapunov synthesis, it is proven that the proposed control is able to guarantee semi-global uniformly ultimately bounded of all signals in the closed-loop system. Simulation results are provided to verify the effectiveness of the proposed approach.
AbstractList This study investigates the adaptive neural dynamic surface control (DSC) of output constrained non‐linear systems, subject to unknown system dynamics and uncertain control direction. A Nussbaum‐type dynamic gain algorithm is used to handle the effect of unknown control direction. Integral barrier Lyapunov functions (iBLFs) are directly utilised to tackle the effect of output constraint. The prominent feature of iBLFs is that the feasible initial output signals are relaxed to the whole constraint range compared with pure tracking errors‐based barrier Lyapunov function. Also, the DSC technology is developed to avoid the explosion of complexity in traditional control design, and adaptive neural networks are adopted to estimate the uncertainty that comprises the unknown system dynamics and parametric uncertainties. By utilising Lyapunov synthesis, it is proven that the proposed control is able to guarantee semi‐global uniformly ultimately bounded of all signals in the closed‐loop system. Simulation results are provided to verify the effectiveness of the proposed approach.
Author Zhang, Sainan
He, Wei
Tang, Zhongliang
Ge, Shuzhi Sam
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  givenname: Wei
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  organization: 4School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, People's Republic of China
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Issue 17
Keywords Nussbaum-type dynamic gain algorithm
iBLF
uncertain systems
semiglobal uniformly ultimately bounded control
parametric uncertainties
adaptive control
control system synthesis
constraint range
Lyapunov synthesis
Integral barrier Lyapunov functions
closed loop systems
adaptive neural dynamic surface control
initial output signals
neurocontrollers
uncertain control direction
output constrained nonlinear systems
nonlinear control systems
DSC technology
pure tracking error-based barrier Lyapunov function
output constraint
unknown system dynamics
closed-loop system
Lyapunov methods
Language English
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SSID ssj0055645
Score 2.3263497
Snippet This study investigates the adaptive neural dynamic surface control (DSC) of output constrained non-linear systems, subject to unknown system dynamics and...
This study investigates the adaptive neural dynamic surface control (DSC) of output constrained non‐linear systems, subject to unknown system dynamics and...
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wiley
iet
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StartPage 2994
SubjectTerms adaptive control
adaptive neural dynamic surface control
closed loop systems
closed‐loop system
constraint range
control system synthesis
DSC technology
iBLF
initial output signals
Integral barrier Lyapunov functions
Lyapunov methods
Lyapunov synthesis
neurocontrollers
nonlinear control systems
Nussbaum‐type dynamic gain algorithm
output constrained nonlinear systems
output constraint
parametric uncertainties
pure tracking error‐based barrier Lyapunov function
Research Article
semiglobal uniformly ultimately bounded control
uncertain control direction
uncertain systems
unknown system dynamics
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Title Adaptive neural dynamic surface control of output constrained non-linear systems with unknown control direction
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Volume 11
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