Low-Cost Approximation-Based Adaptive Tracking Control of Output-Constrained Nonlinear Systems

Kai ZHAO; Yongduan SONG; Wenchao MENG; C. L. Philip CHEN; Long CHEN

doi:10.1109/TNNLS.2020.3026078

Low-Cost Approximation-Based Adaptive Tracking Control of Output-Constrained Nonlinear Systems

Kai ZHAO
, Yongduan SONG^*
, Wenchao MENG
, C. L. Philip CHEN
, Long CHEN

^*Corresponding author for this work

Research output: Journal Publications › Journal Article (refereed) › peer-review

94 Citations (Scopus)

Abstract

For pure-feedback nonlinear systems under asymmetric output constraint, we present a low-cost neuroadaptive tracking control solution with salient features benefited from two design steps. In the first step, a novel output-dependent universal barrier function (ODUBF) is constructed such that not only the restrictive condition on constraining boundaries/functions is removed but also both constrained and unconstrained cases can be handled uniformly without the need for changing the control structure. In the second step, to reduce the computational burden caused by the neural network (NN)-based approximators, a single parameter estimator is developed so that the number of adaptive law is independent of the system order and the dimension of system parameters, making the control design inexpensive in computation. Furthermore, it is shown that all signals in the closed-loop system are semiglobally uniformly ultimately bounded, the tracking error converges to an adjustable neighborhood of the origin, and the violation of output constraint is prevented. The effectiveness of the proposed method can be validated via numerical simulation.

Original language	English
Pages (from-to)	4890-4900
Number of pages	11
Journal	IEEE Transactions on Neural Networks and Learning Systems
Volume	32
Issue number	11
Early online date	14 Oct 2020
DOIs	https://doi.org/10.1109/TNNLS.2020.3026078
Publication status	Published - Nov 2021
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 2012 IEEE.

Funding

This work was supported in part by the Open Research Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University, China, under Grant ICT20099; in part by the National Natural Science Foundation of China under Grant 61860206008, Grant 61773081, Grant 61933012, Grant 61833013, Grant 61991403, and Grant 61803053; in part by the National Key Research and Development Program of China under Grant 2019YFB1703600; and in part by the Science and Technology Development Fund, Macau under Grant 079/2017/A2, Grant 0119/2018/A3, and Grant 196/2017/A3.

Keywords

Asymmetric output constraint
neural adaptive control
nonlinear systems
universal barrier function

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10.1109/TNNLS.2020.3026078

Cite this

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title = "Low-Cost Approximation-Based Adaptive Tracking Control of Output-Constrained Nonlinear Systems",

abstract = "For pure-feedback nonlinear systems under asymmetric output constraint, we present a low-cost neuroadaptive tracking control solution with salient features benefited from two design steps. In the first step, a novel output-dependent universal barrier function (ODUBF) is constructed such that not only the restrictive condition on constraining boundaries/functions is removed but also both constrained and unconstrained cases can be handled uniformly without the need for changing the control structure. In the second step, to reduce the computational burden caused by the neural network (NN)-based approximators, a single parameter estimator is developed so that the number of adaptive law is independent of the system order and the dimension of system parameters, making the control design inexpensive in computation. Furthermore, it is shown that all signals in the closed-loop system are semiglobally uniformly ultimately bounded, the tracking error converges to an adjustable neighborhood of the origin, and the violation of output constraint is prevented. The effectiveness of the proposed method can be validated via numerical simulation.",

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AU - MENG, Wenchao

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AU - CHEN, Long

PY - 2021/11

Y1 - 2021/11

N2 - For pure-feedback nonlinear systems under asymmetric output constraint, we present a low-cost neuroadaptive tracking control solution with salient features benefited from two design steps. In the first step, a novel output-dependent universal barrier function (ODUBF) is constructed such that not only the restrictive condition on constraining boundaries/functions is removed but also both constrained and unconstrained cases can be handled uniformly without the need for changing the control structure. In the second step, to reduce the computational burden caused by the neural network (NN)-based approximators, a single parameter estimator is developed so that the number of adaptive law is independent of the system order and the dimension of system parameters, making the control design inexpensive in computation. Furthermore, it is shown that all signals in the closed-loop system are semiglobally uniformly ultimately bounded, the tracking error converges to an adjustable neighborhood of the origin, and the violation of output constraint is prevented. The effectiveness of the proposed method can be validated via numerical simulation.

AB - For pure-feedback nonlinear systems under asymmetric output constraint, we present a low-cost neuroadaptive tracking control solution with salient features benefited from two design steps. In the first step, a novel output-dependent universal barrier function (ODUBF) is constructed such that not only the restrictive condition on constraining boundaries/functions is removed but also both constrained and unconstrained cases can be handled uniformly without the need for changing the control structure. In the second step, to reduce the computational burden caused by the neural network (NN)-based approximators, a single parameter estimator is developed so that the number of adaptive law is independent of the system order and the dimension of system parameters, making the control design inexpensive in computation. Furthermore, it is shown that all signals in the closed-loop system are semiglobally uniformly ultimately bounded, the tracking error converges to an adjustable neighborhood of the origin, and the violation of output constraint is prevented. The effectiveness of the proposed method can be validated via numerical simulation.

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ER -

Low-Cost Approximation-Based Adaptive Tracking Control of Output-Constrained Nonlinear Systems

Abstract

Bibliographical note

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Keywords

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