Observed-based adaptive finite-time tracking control for a class of nonstrict-feedback nonlinear systems with input saturation
This paper concentrates upon the problem of adaptive neural finite-time tracking control for uncertain nonstrict-feedback nonlinear systems with input saturation. The design difficulty of non-smooth input saturation nonlinearity is solved by applying a smooth non-affine function to approximate the s...
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| Published in | Journal of the Franklin Institute Vol. 357; no. 16; pp. 11518 - 11544 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elmsford
Elsevier Ltd
01.11.2020
Elsevier Science Ltd |
| Subjects | |
| Online Access | Get full text |
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| Abstract | This paper concentrates upon the problem of adaptive neural finite-time tracking control for uncertain nonstrict-feedback nonlinear systems with input saturation. The design difficulty of non-smooth input saturation nonlinearity is solved by applying a smooth non-affine function to approximate the saturation signal. Neural networks, as a kind of specialized function estimators, are used to estimate the uncertain function. Meanwhile, a neural network-based observer is constructed to observe the unavailable states, and thus an observer-based adaptive finite-time tracking control strategy is developed by combining dynamic surface control (DSC) technique and backstepping approach. Furthermore, the stability of the considered system is analyzed via semi-global practical finite-time stability theory. Under the proposed control method, all the signals in the closed-loop system are bounded, and the system output can almost surely track the desired trajectory within a specified bounded error in a finite time. In the end, two examples are adopted to illustrate the validity of our results. |
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| AbstractList | This paper concentrates upon the problem of adaptive neural finite-time tracking control for uncertain nonstrict-feedback nonlinear systems with input saturation. The design difficulty of non-smooth input saturation nonlinearity is solved by applying a smooth non-affine function to approximate the saturation signal. Neural networks, as a kind of specialized function estimators, are used to estimate the uncertain function. Meanwhile, a neural network-based observer is constructed to observe the unavailable states, and thus an observer-based adaptive finite-time tracking control strategy is developed by combining dynamic surface control (DSC) technique and backstepping approach. Furthermore, the stability of the considered system is analyzed via semi-global practical finite-time stability theory. Under the proposed control method, all the signals in the closed-loop system are bounded, and the system output can almost surely track the desired trajectory within a specified bounded error in a finite time. In the end, two examples are adopted to illustrate the validity of our results. |
| Author | Zhao, Xudong Huo, Xin Ma, Li Zong, Guangdeng |
| Author_xml | – sequence: 1 givenname: Li surname: Ma fullname: Ma, Li email: limario8369@gmail.com organization: College of Engineering, Bohai University, Jinzhou 121013, Liaoning, China – sequence: 2 givenname: Guangdeng surname: Zong fullname: Zong, Guangdeng email: lovelyletian@gmail.com organization: School of Engineering, Qufu Normal University, Rizhao 276826, Shandong, China – sequence: 3 givenname: Xudong surname: Zhao fullname: Zhao, Xudong email: xdzhaohit@gmail.com organization: College of Engineering, Bohai University, Jinzhou 121013, Liaoning, China – sequence: 4 givenname: Xin surname: Huo fullname: Huo, Xin email: aixiaoxin09@gmail.com organization: College of Engineering, Bohai University, Jinzhou 121013, Liaoning, China |
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| SubjectTerms | Adaptive control Control methods Control stability Dynamic stability Feedback control Feedback control systems Finite element analysis Neural networks Nonlinear control Nonlinear systems Nonlinearity Saturation Stability analysis Tracking control Tracking control systems |
| Title | Observed-based adaptive finite-time tracking control for a class of nonstrict-feedback nonlinear systems with input saturation |
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