Adaptive Finite-Time Fuzzy Control of Nonlinear Active Suspension Systems With Input Delay
This paper presents a new adaptive fuzzy control scheme for active suspension systems subject to control input time delay and unknown nonlinear dynamics. First, a predictor-based compensation scheme is constructed to address the effect of input delay in the closed-loop system. Then, a fuzzy logic sy...
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| Published in | IEEE transactions on cybernetics Vol. 50; no. 6; pp. 2639 - 2650 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
IEEE
01.06.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2168-2267 2168-2275 2168-2275 |
| DOI | 10.1109/TCYB.2019.2894724 |
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| Abstract | This paper presents a new adaptive fuzzy control scheme for active suspension systems subject to control input time delay and unknown nonlinear dynamics. First, a predictor-based compensation scheme is constructed to address the effect of input delay in the closed-loop system. Then, a fuzzy logic system (FLS) is employed as the function approximator to address the unknown nonlinearities. Finally, to enhance the transient suspension response, a novel parameter estimation error-based finite-time (FT) adaptive algorithm is developed to online update the unknown FLS weights, which differs from traditional estimation methods, for example, gradient algorithm with <inline-formula> <tex-math notation="LaTeX">{e} </tex-math></inline-formula>-modification or <inline-formula> <tex-math notation="LaTeX">{\sigma } </tex-math></inline-formula>-modification. In this framework, both the suspension and estimation errors can achieve convergence in FT. A Lyapunov-Krasovskii functional is constructed to prove the closed-loop system stability. Comparative simulation results based on a dynamic simulator built in a professional vehicle simulation software, Carsim, are provided to demonstrate the validity of the proposed control approach, and show its effectiveness to operate active suspension systems safely and reliably in various road conditions. |
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| AbstractList | This paper presents a new adaptive fuzzy control scheme for active suspension systems subject to control input time delay and unknown nonlinear dynamics. First, a predictor-based compensation scheme is constructed to address the effect of input delay in the closed-loop system. Then, a fuzzy logic system (FLS) is employed as the function approximator to address the unknown nonlinearities. Finally, to enhance the transient suspension response, a novel parameter estimation error-based finite-time (FT) adaptive algorithm is developed to online update the unknown FLS weights, which differs from traditional estimation methods, for example, gradient algorithm with e -modification or σ -modification. In this framework, both the suspension and estimation errors can achieve convergence in FT. A Lyapunov-Krasovskii functional is constructed to prove the closed-loop system stability. Comparative simulation results based on a dynamic simulator built in a professional vehicle simulation software, Carsim, are provided to demonstrate the validity of the proposed control approach, and show its effectiveness to operate active suspension systems safely and reliably in various road conditions. This paper presents a new adaptive fuzzy control scheme for active suspension systems subject to control input time delay and unknown nonlinear dynamics. First, a predictor-based compensation scheme is constructed to address the effect of input delay in the closed-loop system. Then, a fuzzy logic system (FLS) is employed as the function approximator to address the unknown nonlinearities. Finally, to enhance the transient suspension response, a novel parameter estimation error-based finite-time (FT) adaptive algorithm is developed to online update the unknown FLS weights, which differs from traditional estimation methods, for example, gradient algorithm with [Formula Omitted]-modification or [Formula Omitted]-modification. In this framework, both the suspension and estimation errors can achieve convergence in FT. A Lyapunov–Krasovskii functional is constructed to prove the closed-loop system stability. Comparative simulation results based on a dynamic simulator built in a professional vehicle simulation software, Carsim, are provided to demonstrate the validity of the proposed control approach, and show its effectiveness to operate active suspension systems safely and reliably in various road conditions. This paper presents a new adaptive fuzzy control scheme for active suspension systems subject to control input time delay and unknown nonlinear dynamics. First, a predictor-based compensation scheme is constructed to address the effect of input delay in the closed-loop system. Then, a fuzzy logic system (FLS) is employed as the function approximator to address the unknown nonlinearities. Finally, to enhance the transient suspension response, a novel parameter estimation error-based finite-time (FT) adaptive algorithm is developed to online update the unknown FLS weights, which differs from traditional estimation methods, for example, gradient algorithm with e -modification or σ -modification. In this framework, both the suspension and estimation errors can achieve convergence in FT. A Lyapunov-Krasovskii functional is constructed to prove the closed-loop system stability. Comparative simulation results based on a dynamic simulator built in a professional vehicle simulation software, Carsim, are provided to demonstrate the validity of the proposed control approach, and show its effectiveness to operate active suspension systems safely and reliably in various road conditions.This paper presents a new adaptive fuzzy control scheme for active suspension systems subject to control input time delay and unknown nonlinear dynamics. First, a predictor-based compensation scheme is constructed to address the effect of input delay in the closed-loop system. Then, a fuzzy logic system (FLS) is employed as the function approximator to address the unknown nonlinearities. Finally, to enhance the transient suspension response, a novel parameter estimation error-based finite-time (FT) adaptive algorithm is developed to online update the unknown FLS weights, which differs from traditional estimation methods, for example, gradient algorithm with e -modification or σ -modification. In this framework, both the suspension and estimation errors can achieve convergence in FT. A Lyapunov-Krasovskii functional is constructed to prove the closed-loop system stability. Comparative simulation results based on a dynamic simulator built in a professional vehicle simulation software, Carsim, are provided to demonstrate the validity of the proposed control approach, and show its effectiveness to operate active suspension systems safely and reliably in various road conditions. This paper presents a new adaptive fuzzy control scheme for active suspension systems subject to control input time delay and unknown nonlinear dynamics. First, a predictor-based compensation scheme is constructed to address the effect of input delay in the closed-loop system. Then, a fuzzy logic system (FLS) is employed as the function approximator to address the unknown nonlinearities. Finally, to enhance the transient suspension response, a novel parameter estimation error-based finite-time (FT) adaptive algorithm is developed to online update the unknown FLS weights, which differs from traditional estimation methods, for example, gradient algorithm with <inline-formula> <tex-math notation="LaTeX">{e} </tex-math></inline-formula>-modification or <inline-formula> <tex-math notation="LaTeX">{\sigma } </tex-math></inline-formula>-modification. In this framework, both the suspension and estimation errors can achieve convergence in FT. A Lyapunov-Krasovskii functional is constructed to prove the closed-loop system stability. Comparative simulation results based on a dynamic simulator built in a professional vehicle simulation software, Carsim, are provided to demonstrate the validity of the proposed control approach, and show its effectiveness to operate active suspension systems safely and reliably in various road conditions. |
| Author | Huang, Yingbo Li, Guang Na, Jing Wu, Xing Su, Shun-Feng |
| Author_xml | – sequence: 1 givenname: Jing orcidid: 0000-0002-3067-1580 surname: Na fullname: Na, Jing email: najing25@163.com organization: Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, China – sequence: 2 givenname: Yingbo orcidid: 0000-0001-9390-2369 surname: Huang fullname: Huang, Yingbo email: yingbo_huang@126.com organization: Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, China – sequence: 3 givenname: Xing surname: Wu fullname: Wu, Xing email: xingwu@aliyun.com organization: Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, China – sequence: 4 givenname: Shun-Feng orcidid: 0000-0001-9777-128X surname: Su fullname: Su, Shun-Feng email: sfsu@mail.ntust.edu.tw organization: Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan – sequence: 5 givenname: Guang orcidid: 0000-0001-9323-5076 surname: Li fullname: Li, Guang email: g.li@qmul.ac.uk organization: School of Engineering and Materials Science, Queen Mary University of London, London, U.K |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30794520$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | Active control Active suspension systems Adaptive algorithms Adaptive control Closed loop systems Computer simulation Delay effects Delays Dynamic stability Dynamical systems Feedback control finite-time (FT) convergence Fuzzy control Fuzzy logic fuzzy logic systems (FLSs) Fuzzy systems input time delay Nonlinear control Nonlinear dynamics Nonlinear systems Parameter estimation Suspension systems Suspensions (mechanical systems) System effectiveness Systems stability Time lag Vehicle dynamics |
| Title | Adaptive Finite-Time Fuzzy Control of Nonlinear Active Suspension Systems With Input Delay |
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