A Robust Intelligent Controller for Autonomous Ground Vehicle Longitudinal Dynamics

Abstract

In this paper, a novel adaptive sliding mode controller (SMC) was designed based on a robust law considering disturbances and uncertainties for autonomous ground vehicle (AGV) longitudinal dynamics. The robust law was utilized in an innovative method involving the upper bounds of disturbances and uncertainties. Estimating this lumped uncertainty upper limit based on a neural network approach allowed its online knowledge. It guided the controller to withstand the disturbance and to compensate for the uncertainties. A stability analysis, according to Lyapunov, was completed to confirm the asymptotic convergence of the states to the desired state. The effectiveness and benefits of the planned approach were scrutinized by simulations and comparative studies.

An Improved Object Detection and Trajectory Prediction Method for Traffic Conflicts Analysis

“…Autonomous driving technology means that the vehicle senses its environment through various sensors (radar, camera, LIDAR, etc.) and makes decisions to control the vehicle, driving itself "without the driver" [9][10][11]. The trial evaluation and prediction mentioned in this paper can be applied to the decision-making phase of autonomous driving technology.…”

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.…”

“…Moreover, contrary to [24-26, 32, 33], an integral error term is added to the proposed backstepping controller to reduce steady-state tracking errors and enhance the tracking accuracy. • In contrast to the neural networks in [40][41][42][43][44][45][46][47] where the whole weight vector is updated, in this paper, a single parameter that corresponds to the norm of the weight vector of the neural network is updated. This significantly reduces the computational load and improves the convergence speed of the controller.…”

Coupled Lateral and Longitudinal Controller for Over-Actuated Vehicle in Evasive Maneuvering With Sliding Mode Control Strategy

“…Therefore, higher-order is the most common type for vehicle controllers that use SMC theory, as in [21], [26], [32]. Other recent SMC strategies include fractional-order [27], [33], [34] and the use of machine learning combined with SMC [25], [35]- [37]. Whereas MPC can accommodate vehicle nonlinearities and multiple hard and soft constraints, its main disadvantage is the high computational cost, which can be unsuitable for real-time applications [38].…”