Abstract
This paper presents a novel neural-fuzzy-based adaptive sliding mode automatic steering control strategy to improve the driving performance of vision-based unmanned electric vehicles with time-varying and uncertain parameters. Primarily, the kinematic and dynamic models which accurately express the steering behaviors of vehicles are constructed, and in which the relationship between the look-ahead time and vehicle velocity is revealed. Then, in order to overcome the external disturbances, parametric uncertainties and time-varying features of vehicles, a neural-fuzzy-based adaptive sliding mode automatic steering controller is proposed to supervise the lateral dynamic behavior of unmanned electric vehicles, which includes an equivalent control law and an adaptive variable structure control law. In this novel automatic steering control system of vehicles, a neural network system is utilized for approximating the switching control gain of variable structure control law, and a fuzzy inference system is presented to adjust the thickness of boundary layer in real-time. The stability of closed-loop neural-fuzzy-based adaptive sliding mode automatic steering control system is proven using the Lyapunov theory. Finally, the results illustrate that the presented control scheme has the excellent properties in term of error convergence and robustness.
Highlights
Unmanned electric vehicles have attracted considerable research interests due to its strong power to sufficiently handle these severe problems of traffic congestion and safety
A nested PID automatic steering control system of vehicle is established in Ref. [2], and the results illustrate that this strategy has the strong robustness to the uncertain vehicle physical parameters
In the 2005 grand challenge organized by DARPA, a novel nonlinear automatic steering controller is developed for the robot “Stanley” [6]
Summary
Unmanned electric vehicles have attracted considerable research interests due to its strong power to sufficiently handle these severe problems of traffic congestion and safety. To reduce the demand of particular model information and handle the chattering effect, a neural-based SMC control strategy is proposed [24,25,26] These applications have been verified as a powerful and efficient way for a nonlinear and uncertain system. In the proposed automatic steering control system of unmanned electric vehicles, the control gain of proposed control scheme is regulated by the neural network technique to enhance the tracking performance, and the thickness of boundary layer is adaptively adjusted by the fuzzy theory to relieve the chattering phenomenon.
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