Adaptive control of nonlinear uncertain active suspension systems with prescribed performance |
| |
| Affiliation: | 1. Control Systems Laboratory, School of Mechanical Engineering, National Technical University of Athens, 9 Heroon Polytechniou Str., Zografou, Athens 15780, Greece;2. Department of Electrical & Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece;1. State Key Laboratory of Robotics and System (HIT), Harbin Institute of Technology, Harbin 150080, China;2. Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia;1. NBN Sinhgad School of Engineering, Pune, India;2. Tata Consultancy Services, Switzerland;3. College of Engineering, Pune, India;1. School of Mechanical Engineering, Southeast University, Nanjing 211189, PR China;2. School of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin 541004, PR China;3. Department of Mechanical Engineering Hamilton, McMaster University, Ontario, Canada L8S 4L7;4. Department of Engineering, Faculty of Engineering and Science, University of Agder, N-4898 Grimstad, Norway |
| |
| 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. |
| |
| Keywords: | Active suspension system Adaptive control Prescribed performance Neural networks |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
