Vehicle Path Tracking Control Using Pure Pursuit With MPC-Based Look-Ahead Distance Optimization

Kim, S.; Lee, Jonghyup; Han, Kyoungseok; Choi, Seibum B.

doi:10.1109/tvt.2023.3304427

Cited by 6 publications

(3 citation statements)

References 34 publications

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“…By calculating the time of the system in the two phases of converging and sliding motions (26) and (29), it is seen that the non-singular terminal sliding mode controller allows the system to converge to the origin in finite time (t r + t s ) from any initial state. This proof is completed.…”

Section: S(t) = Smentioning

confidence: 99%

“…A hierarchical control method based on the MPC approach for AGVs to achieve efficient and safe parallel operation is proposed [25]. MPC is used to achieve the trajectory tracking of intelligent vehicles while ensuring the stability and safety of intelligent vehicle driving [26]. However, it requires a large amount of computation in calculating the optimal solution and has high requirements for computer hardware.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive Terminal Sliding Mode Trajectory Tracking Control for Autonomous Vehicles Considering Completely Unknown Parameters and Unknown Perturbation Conditions

Feng,

Shen,

Wang

et al. 2024

Machines

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In the actual implementation of autonomous vehicle controller and related applications, it is difficult to obtain all the actual parameters of the vehicle. Considering factors such as uneven pavement and different pavement conditions, it is difficult to accurately establish the vehicle dynamic system model. Based on the non-singular terminal sliding mode and adaptive control theory, this paper establishes a trajectory tracking control strategy for an autonomous vehicle with unknown parameters and unknown disturbances. Firstly, the complex trajectory tracking problem is decoupled from the position and heading angle tracking problem, and the preview error equation is established. Secondly, a non-singular terminal sliding mode (NTSM) controller is established to stabilize the trajectory tracking error to the origin in a finite time, and adaptive laws are proposed to estimate the unknown vehicle parameters to adapt to environmental changes. Through the CarSim–Matlab platform, typical working conditions are implemented to verify the proposed controller. Our experimental outcomes affirm that the NTSM controller effectively guarantees the autonomous vehicle’s accurate following of the reference path, ensuring smooth control inputs throughout the entire process.

show abstract

Section: S(t) = Smentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive Terminal Sliding Mode Trajectory Tracking Control for Autonomous Vehicles Considering Completely Unknown Parameters and Unknown Perturbation Conditions

Feng,

Shen,

Wang

et al. 2024

Machines

View full text Add to dashboard Cite

show abstract

“…A study has presented experiments industrial-grade service robots with regulated PurePursuit adjusting linear velocities with particular focus on safety in constrained and partially observable indoor spaces [7]. A related study has applied MPC based lookahead distance optimization for pure pursuit tracking control to improve cutting corner problems in simulation and experiments on various roads, including handling course, double lane change, and 90-degree turn [8].…”

Section: Introductionmentioning

confidence: 99%

Outdoor Tests of Autonomous Navigation System Based on Two Different Reference Points of PurePursuit Algorithm for 10-ton Articulated Vehicle

Latif,

Lindbäck,

Lideskog

et al. 2024

IEEE Access

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This work presents outdoor test results of autonomous navigation system based on two different reference points of PurePursuit algorithm for a 10-ton articulated research platform. PurePursit is commonly used simplified tracking algorithm based on geometric calculation of desired steering angle by pursuing certain number of points/distance ahead on given path from a fixed reference point of vehicle. Choice of fixed reference point effects the tracking accuracy particularly for articulated/center-steered vehicles. In this experimental work, PurePursuit algorithm with a virtual reference point(PPV) and commonly used front-axle reference point(PPF) is evaluated for heavy duty articulated vehicles in outdoor experiments .Experimental data shows that choice of reference point in PurePursuit algorithm for articulated vehicle has impact on tracking accuracy in terms of crosstrack errors and heading errors. Navigation tests were performed on a flat asphalt surface for paths of sharp complexities i-e a path with continuous curvature (circular path) and a path with sharp turns (zigzag path) with different initial conditions i-e initial position of vehicle. In general, it can be concluded that PurePursuit algorithm with front reference point (PPF) produced fewer crosstrack errors while PurePursuit algorithm with virtual midpoint reference (PPV) produced fewer heading errors.

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Preview‐Based Path‐tracking Stability Control with Vehicle Dynamic Uncertainty via Robust Weighted LPV/H∞ Technique

Cao,

Zhou,

et al. 2024

ISA Transactions

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Vehicle Path Tracking Control Using Pure Pursuit With MPC-Based Look-Ahead Distance Optimization

Cited by 6 publications

References 34 publications

Adaptive Terminal Sliding Mode Trajectory Tracking Control for Autonomous Vehicles Considering Completely Unknown Parameters and Unknown Perturbation Conditions

Adaptive Terminal Sliding Mode Trajectory Tracking Control for Autonomous Vehicles Considering Completely Unknown Parameters and Unknown Perturbation Conditions

Outdoor Tests of Autonomous Navigation System Based on Two Different Reference Points of PurePursuit Algorithm for 10-ton Articulated Vehicle

Preview‐Based Path‐tracking Stability Control with Vehicle Dynamic Uncertainty via Robust Weighted LPV/H∞ Technique

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