Robust Adaptive Neural Tracking Control for a Class of Perturbed Uncertain Nonlinear Systems With State Constraints
In this paper, we deal with the problem of tracking control for a class of uncertain nonlinear systems in strictfeedback form subject to completely unknown system nonlinearities, hard constraints on full states, and unknown time-varying bounded disturbances. Integral barrier Lyapunov functionals are...
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| Published in | IEEE transactions on systems, man, and cybernetics. Systems Vol. 46; no. 12; pp. 1618 - 1629 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
IEEE
01.12.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2168-2216 2168-2232 |
| DOI | 10.1109/TSMC.2015.2508962 |
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| Abstract | In this paper, we deal with the problem of tracking control for a class of uncertain nonlinear systems in strictfeedback form subject to completely unknown system nonlinearities, hard constraints on full states, and unknown time-varying bounded disturbances. Integral barrier Lyapunov functionals are constructed to handle the unknown affine control gains (g(·)) with state constraints simultaneously. This removes the need on the knowledge of control gains for control design and avoids the conservative step of transforming original state constraints into new bounds on tracking errors. Neural networks (NNs) are used to approximate the unknown continuous packaged functions. To enhance the robustness, adapting parameters are developed to compensate the unknown bounds on NNs approximations and external disturbances. Design parameters-dependent feasibility conditions are formulated as sufficient conditions for the existence of feasible design parameters to guarantee the state constraints, and an offline constrained optimization step is proposed to obtain the optimal design parameters prior to the implementation of the proposed control. It is proved that the proposed control can guarantee the semiglobal uniform ultimate boundedness of all signals in closed-loop system, all states are ensured to remain in the predefined constrained state space, and tracking error converges to an adjustable neighborhood of the origin by choosing appropriate design parameters. Simulations are performed to validate the proposed control. |
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| AbstractList | In this paper, we deal with the problem of tracking control for a class of uncertain nonlinear systems in strict-feedback form subject to completely unknown system nonlinearities, hard constraints on full states, and unknown time-varying bounded disturbances. Integral barrier Lyapunov functionals are constructed to handle the unknown affine control gains ([Formula Omitted]) with state constraints simultaneously. This removes the need on the knowledge of control gains for control design and avoids the conservative step of transforming original state constraints into new bounds on tracking errors. Neural networks (NNs) are used to approximate the unknown continuous packaged functions. To enhance the robustness, adapting parameters are developed to compensate the unknown bounds on NNs approximations and external disturbances. Design parameters-dependent feasibility conditions are formulated as sufficient conditions for the existence of feasible design parameters to guarantee the state constraints, and an offline constrained optimization step is proposed to obtain the optimal design parameters prior to the implementation of the proposed control. It is proved that the proposed control can guarantee the semiglobal uniform ultimate boundedness of all signals in closed-loop system, all states are ensured to remain in the predefined constrained state space, and tracking error converges to an adjustable neighborhood of the origin by choosing appropriate design parameters. Simulations are performed to validate the proposed control. In this paper, we deal with the problem of tracking control for a class of uncertain nonlinear systems in strictfeedback form subject to completely unknown system nonlinearities, hard constraints on full states, and unknown time-varying bounded disturbances. Integral barrier Lyapunov functionals are constructed to handle the unknown affine control gains (g(·)) with state constraints simultaneously. This removes the need on the knowledge of control gains for control design and avoids the conservative step of transforming original state constraints into new bounds on tracking errors. Neural networks (NNs) are used to approximate the unknown continuous packaged functions. To enhance the robustness, adapting parameters are developed to compensate the unknown bounds on NNs approximations and external disturbances. Design parameters-dependent feasibility conditions are formulated as sufficient conditions for the existence of feasible design parameters to guarantee the state constraints, and an offline constrained optimization step is proposed to obtain the optimal design parameters prior to the implementation of the proposed control. It is proved that the proposed control can guarantee the semiglobal uniform ultimate boundedness of all signals in closed-loop system, all states are ensured to remain in the predefined constrained state space, and tracking error converges to an adjustable neighborhood of the origin by choosing appropriate design parameters. Simulations are performed to validate the proposed control. |
| Author | Shuzhi Sam Ge Keng Peng Tee Zhong-Liang Tang Wei He |
| Author_xml | – sequence: 1 givenname: Zhong-Liang surname: Tang fullname: Tang, Zhong-Liang – sequence: 2 givenname: Shuzhi Sam surname: Ge fullname: Ge, Shuzhi Sam – sequence: 3 givenname: Keng Peng surname: Tee fullname: Tee, Keng Peng – sequence: 4 givenname: Wei surname: He fullname: He, Wei |
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| Snippet | In this paper, we deal with the problem of tracking control for a class of uncertain nonlinear systems in strictfeedback form subject to completely unknown... In this paper, we deal with the problem of tracking control for a class of uncertain nonlinear systems in strict-feedback form subject to completely unknown... |
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| StartPage | 1618 |
| SubjectTerms | Adaptive systems Approximation methods Artificial neural networks Backstepping design constrained states Control design Feedback neural network (NN) Neural networks Nonlinear systems Robust control Tracking control Trajectory Uncertainty unknown disturbance |
| Title | Robust Adaptive Neural Tracking Control for a Class of Perturbed Uncertain Nonlinear Systems With State Constraints |
| URI | https://ieeexplore.ieee.org/document/7373677 https://www.proquest.com/docview/1842213352 |
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