Adaptive Neural Dynamic Surface Control for Nonstrict-Feedback Systems With Output Dead Zone
This paper focuses on the problem of adaptive output-constrained neural tracking control for uncertain nonstrict-feedback systems in the presence of unknown symmetric output dead zone and input saturation. A Nussbaum-type function-based dead-zone model is introduced such that the dynamic surface con...
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| Published in | IEEE transaction on neural networks and learning systems Vol. 29; no. 11; pp. 5200 - 5213 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
IEEE
01.11.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
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| Abstract | This paper focuses on the problem of adaptive output-constrained neural tracking control for uncertain nonstrict-feedback systems in the presence of unknown symmetric output dead zone and input saturation. A Nussbaum-type function-based dead-zone model is introduced such that the dynamic surface control approach can be used for controller design. The variable separation technique is employed to decompose the unknown function of entire states in each subsystem into a series of smooth functions. Radial basis function neural networks are utilized to approximate the unknown black-box functions derived from Young's inequality. With the help of auxiliary first-order filters, the dimensions of neural network input are reduced in each recursive design. A main advantage of the proposed method is that for an <inline-formula> <tex-math notation="LaTeX">n </tex-math></inline-formula>-order nonlinear system, only one adaptation parameter needs to be tuned online. It is rigorously shown that the proposed output-constrained controller guarantees that all the closed-loop signals are semiglobal uniformly ultimately bounded and the tracking error never violates the output constraint. |
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| AbstractList | This paper focuses on the problem of adaptive output-constrained neural tracking control for uncertain nonstrict-feedback systems in the presence of unknown symmetric output dead zone and input saturation. A Nussbaum-type function-based dead-zone model is introduced such that the dynamic surface control approach can be used for controller design. The variable separation technique is employed to decompose the unknown function of entire states in each subsystem into a series of smooth functions. Radial basis function neural networks are utilized to approximate the unknown black-box functions derived from Young's inequality. With the help of auxiliary first-order filters, the dimensions of neural network input are reduced in each recursive design. A main advantage of the proposed method is that for an <inline-formula> <tex-math notation="LaTeX">n </tex-math></inline-formula>-order nonlinear system, only one adaptation parameter needs to be tuned online. It is rigorously shown that the proposed output-constrained controller guarantees that all the closed-loop signals are semiglobal uniformly ultimately bounded and the tracking error never violates the output constraint. This paper focuses on the problem of adaptive output-constrained neural tracking control for uncertain nonstrict-feedback systems in the presence of unknown symmetric output dead zone and input saturation. A Nussbaum-type function-based dead-zone model is introduced such that the dynamic surface control approach can be used for controller design. The variable separation technique is employed to decompose the unknown function of entire states in each subsystem into a series of smooth functions. Radial basis function neural networks are utilized to approximate the unknown black-box functions derived from Young's inequality. With the help of auxiliary first-order filters, the dimensions of neural network input are reduced in each recursive design. A main advantage of the proposed method is that for an n-order nonlinear system, only one adaptation parameter needs to be tuned online. It is rigorously shown that the proposed output-constrained controller guarantees that all the closed-loop signals are semiglobal uniformly ultimately bounded and the tracking error never violates the output constraint. This paper focuses on the problem of adaptive output-constrained neural tracking control for uncertain nonstrict-feedback systems in the presence of unknown symmetric output dead zone and input saturation. A Nussbaum-type function-based dead-zone model is introduced such that the dynamic surface control approach can be used for controller design. The variable separation technique is employed to decompose the unknown function of entire states in each subsystem into a series of smooth functions. Radial basis function neural networks are utilized to approximate the unknown black-box functions derived from Young's inequality. With the help of auxiliary first-order filters, the dimensions of neural network input are reduced in each recursive design. A main advantage of the proposed method is that for an -order nonlinear system, only one adaptation parameter needs to be tuned online. It is rigorously shown that the proposed output-constrained controller guarantees that all the closed-loop signals are semiglobal uniformly ultimately bounded and the tracking error never violates the output constraint. |
| Author | Shi, Peng Xu, Shengyuan Shi, Xiaocheng Lim, Cheng-Chew |
| Author_xml | – sequence: 1 givenname: Xiaocheng orcidid: 0000-0003-4211-640X surname: Shi fullname: Shi, Xiaocheng email: nihaoshixiaocheng@gmail.com organization: School of Automation, Nanjing University of Science and Technology, Nanjing, China – sequence: 2 givenname: Cheng-Chew orcidid: 0000-0002-2463-9760 surname: Lim fullname: Lim, Cheng-Chew email: cheng.lim@adelaide.edu.au organization: School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, SA, Australia – sequence: 3 givenname: Peng orcidid: 0000-0001-8218-586X surname: Shi fullname: Shi, Peng email: Peng.Shi@adelaide.edu.au organization: School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, SA, Australia – sequence: 4 givenname: Shengyuan orcidid: 0000-0002-3015-0662 surname: Xu fullname: Xu, Shengyuan email: syxu@njust.edu.cn organization: School of Automation, Nanjing University of Science and Technology, Nanjing, China |
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| SubjectTerms | Adaptation models Adaptive control Adaptive output-constrained control Adaptive systems Artificial neural networks Backstepping Basis functions Control systems Control systems design dynamic surface control (DSC) Feedback Neural networks neural networks (NNs) Nonlinear systems nonstrict-feedback nonlinear systems output dead zone Radial basis function Recursive methods Tracking control |
| Title | Adaptive Neural Dynamic Surface Control for Nonstrict-Feedback Systems With Output Dead Zone |
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