Adaptive control of nonlinear uncertain active suspension systems with prescribed performance. (January 2015)
- Record Type:
- Journal Article
- Title:
- Adaptive control of nonlinear uncertain active suspension systems with prescribed performance. (January 2015)
- Main Title:
- Adaptive control of nonlinear uncertain active suspension systems with prescribed performance
- Authors:
- Huang, Yingbo
Na, Jing
Wu, Xing
Liu, Xiaoqin
Guo, Yu - Abstract:
- Abstract: This paper proposes adaptive control designs for vehicle active suspension systems with unknown nonlinear dynamics (e.g., nonlinear spring and piece-wise linear damper dynamics). An adaptive control is first proposed to stabilize the vertical vehicle displacement and thus to improve the ride comfort and to guarantee other suspension requirements (e.g., road holding and suspension space limitation) concerning the vehicle safety and mechanical constraints. An augmented neural network is developed to online compensate for the unknown nonlinearities, and a novel adaptive law is developed to estimate both NN weights and uncertain model parameters (e.g., sprung mass), where the parameter estimation error is used as a leakage term superimposed on the classical adaptations. To further improve the control performance and simplify the parameter tuning, a prescribed performance function (PPF) characterizing the error convergence rate, maximum overshoot and steady-state error is used to propose another adaptive control. The stability for the closed-loop system is proved and particular performance requirements are analyzed. Simulations are included to illustrate the effectiveness of the proposed control schemes. Highlights: Two adaptive controls are proposed for vehicle active suspension systems with unknown nonlinear dynamics. A novel adaptive law is proposed so that precise estimation of unknown parameters is achieved. A prescribed performance function (PPF) is used toAbstract: This paper proposes adaptive control designs for vehicle active suspension systems with unknown nonlinear dynamics (e.g., nonlinear spring and piece-wise linear damper dynamics). An adaptive control is first proposed to stabilize the vertical vehicle displacement and thus to improve the ride comfort and to guarantee other suspension requirements (e.g., road holding and suspension space limitation) concerning the vehicle safety and mechanical constraints. An augmented neural network is developed to online compensate for the unknown nonlinearities, and a novel adaptive law is developed to estimate both NN weights and uncertain model parameters (e.g., sprung mass), where the parameter estimation error is used as a leakage term superimposed on the classical adaptations. To further improve the control performance and simplify the parameter tuning, a prescribed performance function (PPF) characterizing the error convergence rate, maximum overshoot and steady-state error is used to propose another adaptive control. The stability for the closed-loop system is proved and particular performance requirements are analyzed. Simulations are included to illustrate the effectiveness of the proposed control schemes. Highlights: Two adaptive controls are proposed for vehicle active suspension systems with unknown nonlinear dynamics. A novel adaptive law is proposed so that precise estimation of unknown parameters is achieved. A prescribed performance function (PPF) is used to address the transient and steady-state suspension performance. The performance requirement on ride comfort, road holding and mechanical limitation are all guaranteed. … (more)
- Is Part Of:
- ISA transactions. Volume 54(2015:Jan.)
- Journal:
- ISA transactions
- Issue:
- Volume 54(2015:Jan.)
- Issue Display:
- Volume 54 (2015)
- Year:
- 2015
- Volume:
- 54
- Issue Sort Value:
- 2015-0054-0000-0000
- Page Start:
- 145
- Page End:
- 155
- Publication Date:
- 2015-01
- Subjects:
- Active suspension system -- Adaptive control -- Prescribed performance -- Neural networks
Engineering instruments -- Periodicals
Engineering instruments
Periodicals
Electronic journals
629.805 - Journal URLs:
- http://www.sciencedirect.com/science/journal/00190578 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.isatra.2014.05.025 ↗
- Languages:
- English
- ISSNs:
- 0019-0578
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 4582.700000
British Library DSC - BLDSS-3PM
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