Development of Path Tracking Controller for An Autonomous Tracked Vehicle

Subari, Muhammad Akhimullah; Hudha, Khisbullah; Kadir, Zulkiffli Abd; Dardin, S.; Amer, Noor Hafizah

doi:10.1109/cspa48992.2020.9068686

Cited by 5 publications

(2 citation statements)

References 11 publications

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“…According to the existing studies on lateral motion control, based on the adoption of different control methods, they can be classified into model-free control methods [3][4][5], linear control methods [6], and nonlinear control methods [7,8]. Among these methods, the common PID control algorithms [9,10] are hindered by the need for trial-and-error in parameter selection due to unknown or time-varying vehicle models, resulting in laborious work and suboptimal practical efficacy. The pure pursuit algorithm [11,12] does not consider the vehicle's force conditions, and its tracking performance relies on the selection of the lookahead distance, making it difficult to obtain an optimal solution and resulting in variable tracking effectiveness.…”

Section: Introductionmentioning

confidence: 99%

Path Tracking and Anti-Roll Control of Unmanned Mining Trucks on Mine Site Roads

Wang,

Wan,

et al. 2024

WEVJ

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Aiming to address the tracking accuracy and anti-rollover problem of the unmanned mining truck path tracking process under the complex unstructured road conditions in mining areas, a coordinated control strategy for path tracking and anti-rollover based on topology theory is proposed. Moreover, optimal equilibrium weights are assigned to path tracking control and anti-rollover control to ensure that the path tracking accuracy of the mining vehicle can be effectively improved in a safe and stable driving state. Regarding the path tracking problem, a lateral preview error model is established, and a path tracking controller is designed using LQR (linear quadratic regulator) control theory. In the design of the anti-rollover controller, the effects of understeer and trip-type rollover on the stability of the vehicle are taken into account, and the ideal transverse swing angular velocity and trip-type rollover evaluation index are introduced for controller design, which reduce the effects of the curves and roadway excitation on the mining truck and improve the rollover motion. Based on a joint simulation using Trucksim and Simulink and the construction of a hardware-in-the-loop simulation platform for verification, the single control strategy and coordinated control strategy are compared and analyzed. The final simulation results show that the tracking error, yaw velocity, and center of mass side deviation angle are optimized by 45%, 32.5%, and 20%, respectively. Therefore, the Extension theory-based coordinated controller satisfies the complex road conditions in the mining area and improves the tracking accuracy to the maximum extent while ensuring the safety and smoothness of vehicle driving and exhibiting good adaptability and robustness.

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Section: Introductionmentioning

confidence: 99%

Path Tracking and Anti-Roll Control of Unmanned Mining Trucks on Mine Site Roads

Wang,

Wan,

et al. 2024

WEVJ

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“…The steering wheel angle of the vehicle obtained by this type of controller is usually related to the heading angle error and lateral error of the vehicle. This method is easy to implement, but it can only achieve the real-time response of the vehicle at low speeds, and is not suitable for tracking control in high-speed conditions [ 1 , 2 ]. When considering complex urban conditions and high-speed traffic environments, the reliability and robustness of the controller of the model are not highly based on kinematics, so a vehicle dynamics model needs to be introduced.…”

Section: Introductionmentioning

confidence: 99%

Path-Tracking Control Strategy of Unmanned Vehicle Based on DDPG Algorithm

Yao

2022

Sensors

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This paper proposes a deep reinforcement learning (DRL)-based algorithm in the path-tracking controller of an unmanned vehicle to autonomously learn the path-tracking capability of the vehicle by interacting with the CARLA environment. To solve the problem of the high estimation of the Q-value of the DDPG algorithm and slow training speed, the controller adopts the deep deterministic policy gradient algorithm of the double critic network (DCN-DDPG), obtains the trained model through offline learning, and sends control commands to the unmanned vehicle to make the vehicle drive according to the determined route. This method aimed to address the problem of unmanned-vehicle path tracking. This paper proposes a Markov decision process model, including the design of state, action-and-reward value functions, and trained the control strategy in the CARLA simulator Town04 urban scene. The tracking task was completed under various working conditions, and its tracking effect was compared with the original DDPG algorithm, model predictive control (MPC), and pure pursuit. It was verified that the designed control strategy has good environmental adaptability, speed adaptability, and tracking performance.

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