Recurrent neural network non‐singular terminal sliding mode control for path following of autonomous ground vehicles with parametric uncertainties

Abstract

Uncertain characteristics along with unknown exterior disturbances are fundamental issues in the path‐following control of autonomous ground vehicles (AGVs). Here, a novel robust non‐singular terminal sliding mode (NTSM) control method based on a recurrent neural network (RNN) structure is proposed to enhance the path‐following performance of AGVs. First, based on the dynamic model and path‐following model of AGVs, the robust NTSM steering controller is proposed to ensure that the lateral offset converges to zero within a limited time, and concurrently suppresses the chattering phenomenon that occurs in the regular sliding mode control (SMC) strategy. Then, the RNN is applied to approximate the unknown dynamics section of the system online. With an implied feedback loop, its study ability precedes the regular neural network to catch up with the dynamic response. In addition, the adaptive learning algorithms of the parameters in the RNN are derived using the Lyapunov stability theorem and Taylor linearisation technique to ensure the stability of the closed‐loop system. Finally, the robustness and effectiveness of the proposed control approach against parametric uncertainties and unknown external disturbances are verified in the Simulink‐CarSim simulation platform by comparison with selected reported control methods.

Path following control for 4WID-EV based on extended state observer and sliding mode control considering yaw stability

“…It should be noted that the third term B 2 _ u dp on the right hand in the system (12) will affect the convergence of the tracking error. Due to the presence of this term, the tracking error will not converge to zero when the vehicle is traveling on a curve.…”

“…Except the pure sliding mode controller application to path following, some scholars also combine other control techniques with it, such as backstepping method 11 and recurrent neural network. 12 In Xia et al, 13 a lateral path tracking controller based on ADRC and differential flatness was proposed, which improves the control accuracy and enhances the robustness.…”

Adaptive terminal sliding mode trajectory tracking control with fixed‐time prescribed performance considering rollover stability of autonomous vehicles

“…16 In addition to the robustness of the trajectory tracking control strategies, the response and convergence speeds are also of concern. Literature 17 proposes the non-singular fast terminal sliding mode (NFTSM), which has a faster convergence speed compared to the conventional non-singular terminal sliding mode (NTSM) 18 and can converge in a finite time. However, when the initial state of the vehicle is far from the equilibrium state, there may be a problem with a long convergence time.…”

Neuro-Adaptive Path following Control of Autonomous Ground Vehicles with Input Deadzone

“…In [40], a neural network-based control has been proposed for steering control of AGV to keep tracking the target path. The authors in [41][42][43][44][45][46], presented a chattering-free sliding mode control with a neural network approximator to minimize the impact of unknown perturbations. Jin et al [47] utilized adaptive backstepping variable structure control with a neural estimator to tackle vehicle trajectory following deviation and suppress environmental disturbances.…”