Highly Robust Adaptive Sliding Mode Trajectory Tracking Control of Autonomous Vehicles

Abstract

Autonomous driving technology has not yet been widely adopted, in part due to the challenge of achieving high-accuracy trajectory tracking in complex and hazardous driving scenarios. To this end, we proposed an adaptive sliding mode controller optimized by an improved particle swarm optimization (PSO) algorithm. Based on the improved PSO, we also proposed an enhanced grey wolf optimization (GWO) algorithm to optimize the controller. Taking the expected trajectory and vehicle speed as inputs, the proposed control scheme calculates the tracking error based on an expanded vector field guidance law and obtains the control values, including the vehicle’s orientation angle and velocity on the basis of sliding mode control (SMC). To improve PSO, we proposed a three-stage update function for the inertial weight and a dynamic update law for the learning rates to avoid the local optimum dilemma. For the improvement in GWO, we were inspired by PSO and added speed and memory mechanisms to the GWO algorithm. Using the improved optimization algorithm, the control performance was successfully optimized. Moreover, Lyapunov’s approach is adopted to prove the stability of the proposed control schemes. Finally, the simulation shows that the proposed control scheme is able to provide more precise response, faster convergence, and better robustness in comparison with the other widely used controllers.

Temperature Compensation of Wind Tunnel Balance Signal Detection System Based on IGWO-ELM

“…where U T is the measured value of the wind tunnel balance signal detection system at T°C; U re f indicates the measured value at the standard temperature, i.e., 25 °C; and U(FS) indicates the range of the wind tunnel balance signal detection system. The temperature-induced drift caused by measurement errors in the six dimensions can be observed using Equation (27) as depicted in Figure 8. From the figure, it is evident that the temperature-related errors exhibit predominantly nonlinear behavior.…”

“…However, GWO has drawbacks, such as difficulty in handling a large number of variables and escaping local optima when solving large-scale problems [ 26 ]. In a previous study, Xie et al proposed a three-stage update function for inertia weights and a dynamic update method for learning rate to enhance GWO performance by avoiding local optima [ 27 ]. Jin et al proposed optimal Latin hypercube sampling initialization, nonlinear convergence factor, and dynamic weights for IGWO to enhance its accuracy in optimizing support vector regression (SVR) parameters [ 28 ].…”

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

“…In [37], a nonsingular terminal sliding mode controller with disturbance observer was designed for the AGV path following. In [38], a robust adaptive sliding mode path tracking control law optimized with particle swamp optimization was formulated for the AGV. In [39], an observer-based differential flatness-based control has been suggested for path tracking of an AGV subjected to disturbances.…”

Trajectory Tracking Coordinated Control of 4WID-4WIS Electric Vehicle Considering Energy Consumption Economy Based on Pose Sensors

“…Due to the great potential of intelligent driving technology to enhance vehicle safety [ 10 , 11 ], improve traffic efficiency, and reduce energy consumption [ 12 ], the research on trajectory tracking control of 4WID-4WIS electric vehicles has received increasing attention in the automotive industry, and numerous control methods have been developed [ 13 , 14 , 15 ]. In [ 16 ], LTV-MPC (Linear Time-Varying Model Predictive Control) based on DYC (Direct Yaw Control) is used to realize velocity tracking and trajectory tracking, which improves stability during trajectory tracking.…”