Review and performance evaluation of path tracking controllers of autonomous vehicles

Rokonuzzaman, Md.; Mohajer, Navid; Nahavandi, Saeid; Mohamed, Shady

doi:10.1049/itr2.12051

Cited by 77 publications

(54 citation statements)

References 182 publications

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“…The TTC layer is composed of speed controller and direction controller. In this section, we establish the optimal preview control driver model, which is a remarkably representative steering control method [12,28]. Figure 4 is the schematic diagram of the optimal control driver model.…”

Section: Trajectory Tracking Control Layermentioning

confidence: 99%

“…The objective of TTC is to achieve the vehicle following the planned path with small lateral displacement error and other performance requirements through the vehicle's steering and drive control subsystem. A large number of trajectory tracking algorithms have been applied to autonomous motion control subsystems, such as Stanley Model, Optimal Preview Control (OCM), Proportion Integration Differentiation (PID), Model predictive control (MPC), linear quadratic regulator (LQR), Sliding Mode Control (SMC), H∞ control, Neural Network Model (NNM), etc., [11,12]. However, these algorithms only provide the vehicle with the ability to perform direction and speed control under good road adhesion conditions, but they seldom consider the lateral stability under extreme conditions.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Intrinsic Mechanistic of Stability-Tracking Control for Distributed Drive Autonomous Electric Vehicle

Tang

Wang

et al. 2021

Electronics

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For distributed drive autonomous vehicles, adding lateral stability control (LSC) to the trajectory tracking control (TTC) can optimize the distribution of the driving torque of each wheel, so that the vehicle can track the planned trajectory while maintaining stable lateral motion. However, the influence of adding LSC on the TTC system is still unclear. Firstly, a stability-track hierarchical control structure composed of LSC and TTC was established, and the interaction between the two layers was identified as the key of this paper. Then, the Intrinsic Mechanistic framework of the stability-tracking control (STC) was proposed by establishing and analyzing the vehicle dynamic model and control process of two layers. Finally, through simulation experiments, it was found that the change in the curvature of the target trajectory will make the tracking target trajectory and maintaining the lateral stability of the vehicle appear to conflict; in addition, in the LSC layer, the steering characteristics and delay characteristics of different reference models have a greater impact on the lateral stability and trajectory tracking performance; moreover, adjusting the preview time has a more obvious effect on trajectory tracking and lateral stability than the stability correction intensity coefficient.

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Section: Trajectory Tracking Control Layermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis of Intrinsic Mechanistic of Stability-Tracking Control for Distributed Drive Autonomous Electric Vehicle

Tang

Wang

et al. 2021

Electronics

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“…In the motion control of AGVs, path‐following control is the most important problem, which concerns the steering system that enables the AGV to follow the desired route provided by the motion planning and decision‐making layer [4]. Changeable traffic conditions and the high dynamic nonlinearity of vehicles present significant challenges in AGV path‐following control.…”

Section: Introductionmentioning

confidence: 99%

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

Xing

Wei

Meng

2022

IET Intelligent Trans Sys

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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.

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“…A number of techniques have been proposed to design the PTC [1], [6]. Geometry-based controllers such as Pure Pursuit [7] and Stanley controller [8] are quite popular due to their low computational cost, ease of implementation and acceptable performance at low speeds.…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Control With Learned Vehicle Dynamics for Autonomous Vehicle Path Tracking

et al. 2021

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Model Predictive Controller (MPC) is a capable technique for designing Path Tracking Controller (PTC) of Autonomous Vehicles (AVs). The performance of MPC can be significantly enhanced by adopting a high-fidelity and accurate vehicle model. This model should be capable of capturing the full dynamics of the vehicle, including nonlinearities and uncertainties, without imposing a high computational cost for MPC. A data-driven approach realised by learning vehicle dynamics using vehicle operation data can offer a promising solution by providing a suitable trade-off between accurate state predictions and the computational cost for MPC. This work proposes a framework for designing an MPC with a Neural Network (NN)-based learned dynamic model of the vehicle using the plethora of data available from modern vehicle systems. The objective is to integrate an NN-based model with higher accuracy than the conventional vehicle models for the required prediction horizon into MPC for improved tracking performances. The proposed NN-based model is highly capable of approximating latent system states, which are difficult to estimate, and provides more accurate predictions in the presence of parametric uncertainties. The observation of the results in various road conditions shows that the proposed approach outperforms the MPCs with conventional vehicle models.

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Review and performance evaluation of path tracking controllers of autonomous vehicles

Cited by 77 publications

References 182 publications

Analysis of Intrinsic Mechanistic of Stability-Tracking Control for Distributed Drive Autonomous Electric Vehicle

Analysis of Intrinsic Mechanistic of Stability-Tracking Control for Distributed Drive Autonomous Electric Vehicle

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

Model Predictive Control With Learned Vehicle Dynamics for Autonomous Vehicle Path Tracking

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