Neural-Based Command Filtered Backstepping Control for Trajectory Tracking of Underactuated Autonomous Surface Vehicles
| Autor: | Yingjie Wei, Chengju Zhang, Cong Wang, Jinqiang Wang |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Lyapunov stability
0209 industrial biotechnology General Computer Science Artificial neural network neural network Computer science Underactuation 020208 electrical & electronic engineering General Engineering 02 engineering and technology Sliding mode control low-frequency learning techniques 020901 industrial engineering & automation Control theory Robustness (computer science) Backstepping 0202 electrical engineering electronic engineering information engineering Trajectory trajectory tracking General Materials Science Autonomous surface vehicle lcsh:Electrical engineering. Electronics. Nuclear engineering Robust control lcsh:TK1-9971 anti-windup design |
| Zdroj: | IEEE Access, Vol 8, Pp 42481-42490 (2020) |
| ISSN: | 2169-3536 |
| DOI: | 10.1109/access.2020.2975898 |
| Popis: | This paper is concerned with the problem of trajectory tracking control of underactuated autonomous surface vehicles subject to parameter uncertainties and nonlinear external disturbances. A robust control scheme is presented by employing backstepping method, neural network and sliding mode control. In addition, the overall signals are guaranteed the uniformly ultimate boundness by the Lyapunov stability theory. These advantages are highlighted as follows: (i) The derivations of virtual variables are obtained by a second-order filter. A compensation loop is proposed to reduce the filtered errors between the filtered variables and virtual variables. (ii) The neural network is combined with low-frequency learning techniques to estimate and approximate unknown functions of system.(iii) An anti-windup design is employed to restrict the amplitude of control inputs. Finally, simulation results show the strong robustness and tracking effectiveness of the designed control scheme under the nonlinear external disturbances. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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