Adaptive reference aware MPC for lateral control of autonomous vehicles

Pereira, Gonçalo Collares; Wahlberg, Bo; Pettersson, Henrik; Mårtensson, Jonas

doi:10.1016/j.conengprac.2022.105403

Cited by 16 publications

(30 citation statements)

References 10 publications

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“…Several element reasons highlight the significance of the control phase in autonomous driving such as safety, trajectory tracking, manoeuvre execution, smooth ride, energy efficiency which includes optimal control strategies to improve energy efficiency by optimizing acceleration, the integration with decision making that determines high‐level actions while the control system translates these decisions into physical vehicle/robot movements, and finally, the control phase requires rigorous testing, validation, and verification to ensure its reliability and performance across a wide range of scenarios. Thorough testing helps identify potential issues and refine control algorithms [46–66].…”

Section: Vehicle Control For Path Trackingmentioning

confidence: 99%

“…Lateral control [57] focuses on adjusting the steering angle to follow the desired reference path. Wherein the complexity of the dynamic model of the vehicle, the environment, and driving situations make the transposition of the proposed solutions of path-following problems into the context of the autonomous driving field quite difficult as discussed in the review article [54].…”

Section: Lateral and Longitudinal Controllersmentioning

confidence: 99%

See 1 more Smart Citation

Advancing autonomous vehicle control systems: An in‐depth overview of decision‐making and manoeuvre execution state of the art

Abdallaoui,

Ikaouassen,

Kribèche

et al. 2023

The Journal of Engineering

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This abstract discusses the significant progress made in autonomous vehicles, focusing on decision‐making systems and control algorithms. It explores recent advances, challenges, and contributions in the field, emphasizing the need for precise navigation and control. The paper covers various methodologies, including rule‐based methods, machine learning, deep learning, probabilistic approaches, and hybrid approaches, examining their applications and effectiveness in ensuring safe navigation. Additionally, it reviews ongoing research efforts, emerging trends, and persistent challenges related to decision‐making and manoeuvre execution in autonomous vehicles, addressing complex topics such as sensor measurement uncertainty, dynamic environment modelling, real‐time responsiveness, and safe interactions with other road users. The objective is to provide a comprehensive overview of the state of the art in autonomous vehicle navigation and control for readers.

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Section: Vehicle Control For Path Trackingmentioning

confidence: 99%

Section: Lateral and Longitudinal Controllersmentioning

confidence: 99%

Advancing autonomous vehicle control systems: An in‐depth overview of decision‐making and manoeuvre execution state of the art

Abdallaoui,

Ikaouassen,

Kribèche

et al. 2023

The Journal of Engineering

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“…However, if the prediction time domain is too large, it will increase the error of the lawn mower at a distant position, thereby reducing the tracking accuracy of the lawn mower at a nearby position [23]. In addition, an excessively large prediction time domain will also increase the computational complexity of the MPC algorithm and reduce the real-time performance of the system [24]. When the prediction time domain is too small, the status information of the lawn mower will decrease.…”

Section: Adaptive Time Domain Module Designmentioning

confidence: 99%

Research on Path Tracking for an Orchard Mowing Robot Based on Cascaded Model Predictive Control and Anti-Slip Drive Control

Wang

Zhang

et al. 2023

Agronomy

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In complex orchard environments, orchard mowing robots are prone to longitudinal slippage because of the characteristics of tires and the adhesion conditions of the road surface, which makes it difficult for the robots to maintain high-precision path tracking and autonomous navigation positioning. This not only affects the accuracy of path tracking but also leads to unstable motion for the mowing robots. To solve the above problems, we take an orchard mowing robot as the control object and establish a cascaded path-tracking controller and an adaptive time domain model based on a kinematics model. By designing a linear error model, an objective function, and constraint conditions for the mowing robot, the optimal linear velocity and angular velocity of the mower are obtained and converted into the speed of the driving wheel. Then, an anti-slip driving controller is designed based on fuzzy control of the slip rate. The slip-rate-based fuzzy controller is constructed according to the real-time speed of the mower and the reference speed of the driving wheel solved by the model predictive controller, and anti-slip driving control is implemented through a combination of a PID controller and a tire dynamics model. To verify the effectiveness of the proposed method, simulation and field experiments are conducted. The experimental results show that the slip rate of the driving wheel of the mower remains within the target slip rate range in the orchard working environment, avoiding excessive driving wheel sliding. Furthermore, the average lateral error of the path-tracking controller is controlled within 0.05 m, and the average value of the longitudinal error is kept within 0.04 m, which satisfies the control accuracy requirements of lawn mower operations. The proposed method provides a reference optimization scheme for improving the path-tracking and motion stability of a mowing robot.

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“…In lane keeping control algorithms, the MPC algorithm not only considers constraints but also has predictive capability. Therefore, MPC offers unique advantages in lane-keeping control [6,7]. In reference [8], MPC was designed to address the properties of parameter uncertainty, time variation and nonlinearity in vehicle models.…”

Section: Introductionmentioning

confidence: 99%

MPC-based lane keeping for commercial vehicles with steering dead zone consideration

zhao,

Feng,

Jin

et al. 2024

Fourth International Conference on Mechanical Engineering, Intelligent Manufacturing, and Automation Technology (MEMAT 2023)

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Commercial vehicles face challenges such as a large steering dead-zone and difficulties in directly obtaining lateral velocity through sensors. To address these issues, a model predictive control (MPC) lane keeping algorithm is designed, taking into account the characteristics of the steering dead-zone. First, a vehicle dynamics model and state error equations are established. Then, the vehicle status is estimated using an extended Kalman filter (EKF). Based on the estimated vehicle parameters, an MPC lane keeping controller is designed. Finally, depending on the different operating states of the vehicle, the compensation strategies are designed based on the yaw rate error and the dead-zone length. The lane keeping algorithm was validated through simulation experiments. The lateral velocity estimation error was less than 4.8%, and the influence of the steering dead-zone decreased by 8 degrees. The results indicate that the improved MPC lane keeping algorithm exhibits excellent tracking accuracy. The dangerous S-shaped trajectory of the vehicle is effectively avoided, and the stability of vehicle operation is improved.

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Adaptive reference aware MPC for lateral control of autonomous vehicles

Cited by 16 publications

References 10 publications

Advancing autonomous vehicle control systems: An in‐depth overview of decision‐making and manoeuvre execution state of the art

Advancing autonomous vehicle control systems: An in‐depth overview of decision‐making and manoeuvre execution state of the art

Research on Path Tracking for an Orchard Mowing Robot Based on Cascaded Model Predictive Control and Anti-Slip Drive Control

MPC-based lane keeping for commercial vehicles with steering dead zone consideration

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