Neural-Fuzzy-Based Adaptive Sliding Mode Automatic Steering Control of Vision-based Unmanned Electric Vehicles

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 surprise 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.

Sensorless Control Strategy of Permanent Magnet Synchronous Motor Based on Fuzzy Sliding Mode Observer

“…A second-order sliding mode algorithm based on superhelix is proposed in references [26]- [28], the chattering of the system is reduced by the combination of superhelix algorithm and SMO algorithm. In references [29], [30], the neural network is applied to the SMO to obtain the switching gain, which can reduce chattering, but it will increase the complexity of the system. A compound sliding mode control method based on a new hybrid reaching law with disturbance compensation is proposed in reference [31], which can compensate the external disturbance.…”

Lateral control of intelligent vehicles using radial basis function neural networks with sliding mode control based on fractional order calculus

“…The proposal of intelligent control algorithms has helped in the development of the automotive industry, and the control algorithm with neural networks as the system solution has gradually become one of the research objectives for scholars and engineers aiming to optimize traditional controllers (Aalizadeh and Asnafi, 2018;Fan et al, 2022). Intelligent control, the frontier technology of automatic control, solves complex linear and uncertain control problems (Guo et al, 2021). The sliding mode control algorithm is simple and easy to implement with high robustness.…”

“…, 2022). Intelligent control, the frontier technology of automatic control, solves complex linear and uncertain control problems (Guo et al. , 2021).…”