Fuzzy Tracking Control for a Class of Uncertain MIMO Nonlinear Systems with State Constraints

Wei He; Linghuan Kong; Yiting Dong; Yao Yu; Chenguang Yang; Changyin Sun

doi:10.1109/TSMC.2017.2749124

Fuzzy Tracking Control for a Class of Uncertain MIMO Nonlinear Systems with State Constraints

Wei He
, Linghuan Kong
, Yiting Dong
, Yao Yu
, Chenguang Yang
, Changyin Sun

University of Science and Technology Beijing
Key Lab of the Ministry of Education for Process Control and Efficiency Egineering
University of Electronic Science and Technology of China
Texas Tech University
South China University of Technology
Southeast University, Nanjing

Research output: Contribution to journal › Article › peer-review

77 Scopus citations

Abstract

In this paper, an adaptive fuzzy neural network (FNN) control scheme is developed for a class of multiple-input and multiple-output (MIMO) nonlinear systems subject to unknown dynamics and state constraints. FNNs are used to approximate the unknown dynamics that comprises the effects of uncertain parameters and functions. Also, integral Lyapunov functions are introduced to address state constraints. A neural-network-based observer is designed to estimate the unmeasurable states. With state-feedback and output feedback tracking control, the stability of closed-loop system is guaranteed via Lyapunov's stability theory. Two cases of simulations for MIMO systems with state constraints are conducted to verify the effectiveness of the proposed control.

Original language	English
Article number	8094349
Pages (from-to)	543-554
Number of pages	12
Journal	IEEE Transactions on Systems, Man, and Cybernetics: Systems
Volume	49
Issue number	3
DOIs	https://doi.org/10.1109/TSMC.2017.2749124
State	Published - Mar 2019
Externally published	Yes

Keywords

Adaptive control
fuzzy control
multiple-input and multiple-output (MIMO) nonlinear systems
state constraints

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10.1109/TSMC.2017.2749124

Cite this

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abstract = "In this paper, an adaptive fuzzy neural network (FNN) control scheme is developed for a class of multiple-input and multiple-output (MIMO) nonlinear systems subject to unknown dynamics and state constraints. FNNs are used to approximate the unknown dynamics that comprises the effects of uncertain parameters and functions. Also, integral Lyapunov functions are introduced to address state constraints. A neural-network-based observer is designed to estimate the unmeasurable states. With state-feedback and output feedback tracking control, the stability of closed-loop system is guaranteed via Lyapunov's stability theory. Two cases of simulations for MIMO systems with state constraints are conducted to verify the effectiveness of the proposed control.",

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author = "Wei He and Linghuan Kong and Yiting Dong and Yao Yu and Chenguang Yang and Changyin Sun",

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doi = "10.1109/TSMC.2017.2749124",

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AU - He, Wei

AU - Kong, Linghuan

AU - Dong, Yiting

AU - Yu, Yao

AU - Yang, Chenguang

AU - Sun, Changyin

PY - 2019/3

Y1 - 2019/3

N2 - In this paper, an adaptive fuzzy neural network (FNN) control scheme is developed for a class of multiple-input and multiple-output (MIMO) nonlinear systems subject to unknown dynamics and state constraints. FNNs are used to approximate the unknown dynamics that comprises the effects of uncertain parameters and functions. Also, integral Lyapunov functions are introduced to address state constraints. A neural-network-based observer is designed to estimate the unmeasurable states. With state-feedback and output feedback tracking control, the stability of closed-loop system is guaranteed via Lyapunov's stability theory. Two cases of simulations for MIMO systems with state constraints are conducted to verify the effectiveness of the proposed control.

AB - In this paper, an adaptive fuzzy neural network (FNN) control scheme is developed for a class of multiple-input and multiple-output (MIMO) nonlinear systems subject to unknown dynamics and state constraints. FNNs are used to approximate the unknown dynamics that comprises the effects of uncertain parameters and functions. Also, integral Lyapunov functions are introduced to address state constraints. A neural-network-based observer is designed to estimate the unmeasurable states. With state-feedback and output feedback tracking control, the stability of closed-loop system is guaranteed via Lyapunov's stability theory. Two cases of simulations for MIMO systems with state constraints are conducted to verify the effectiveness of the proposed control.

KW - Adaptive control

KW - fuzzy control

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KW - state constraints

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SN - 2168-2216

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JO - IEEE Transactions on Systems, Man, and Cybernetics: Systems

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ER -

Fuzzy Tracking Control for a Class of Uncertain MIMO Nonlinear Systems with State Constraints

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Keywords

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