An Optimization Design of Adaptive Cruise Control System Based on MPC and ADRC

Yang, Zengfu; Wang, Zengcai; Yan, Ming

doi:10.3390/act10060110

Cited by 29 publications

(19 citation statements)

References 35 publications

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“…Artificial intelligence (AI) is becoming more commonly used in many applications especially autonomous driving (AD) ones such as augmented reality (AR) [ 14 ], automatic emergency braking (AEB) [ 15 ], lane keeping assist (LKA) [ 16 ], active cruise control (ACC) [ 17 ], and crash avoidance (CA) [ 18 ]. Forward collision warning (FCW) [ 19 ] and automatic emergency braking (AEB) are considered as the initial trials to integrate crash avoidance functionality.…”

Section: Literature Reviewmentioning

confidence: 99%

SDC-Net: End-to-End Multitask Self-Driving Car Camera Cocoon IoT-Based System

Abdou¹,

Kamal

2022

Sensors

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Currently, deep learning and IoT collaboration is heavily invading automotive applications especially in autonomous driving throughout successful assistance functionalities. Crash avoidance, path planning, and automatic emergency braking are essential functionalities for autonomous driving. Trigger-action-based IoT platforms are widely used due to its simplicity and ability of doing receptive tasks accurately. In this work, we propose SDC-Net system: an end-to-end deep learning IoT hybrid system in which a multitask neural network is trained based on different input representations from a camera-cocoon setup installed in CARLA simulator. We build our benchmark dataset covering different scenarios and corner cases that the vehicle may expose in order to navigate safely and robustly while testing. The proposed system aims to output relevant control actions for crash avoidance, path planning and automatic emergency braking. Multitask learning with a bird’s eye view input representation outperforms the nearest representation in precision, recall, f1-score, accuracy, and average MSE by more than 11.62%, 9.43%, 10.53%, 6%, and 25.84%, respectively.

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Section: Literature Reviewmentioning

confidence: 99%

SDC-Net: End-to-End Multitask Self-Driving Car Camera Cocoon IoT-Based System

Abdou¹,

Kamal

2022

Sensors

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“…For more examples about the combination of adaptive control and ADRC, the reader can refer to [27][28][29][30][31]. Although the adaptive control law greatly improves the performance of ADRC scheme due to its characteristic of parameter self-tuning, it is limited by a large amount of calculation and timeliness.…”

Section: Modeling Of Single-link Flexible Armmentioning

confidence: 99%

“…[26], an active disturbance rejection control scheme using RBF-NN as a feedforward inverse controller to deal with the uncertainties of the model is proposed, which reduces the burden of ESO. For more examples about the combination of adaptive control and ADRC, the reader can refer to [27][28][29][30][31]. Although the adaptive control law greatly improves the performance of ADRC scheme due to its characteristic of parameter self-tuning, it is limited by a large amount of calculation and timeliness.…”

Section: Introductionmentioning

confidence: 99%

Sliding Mode Robust Active Disturbance Rejection Control for Single-Link Flexible Arm with Large Payload Variations

et al. 2021

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This paper proposes a novel robust control scheme for tip trajectory tracking of a lightweight flexible single-link arm. The developed control scheme deals with the influence of tip payload changes and disturbances during the working process of the flexible arm, thus realizing the accurate tracking for the tip reference trajectory. The robust control scheme is composed of an inner loop and an outer loop. The inner loop adopts the traditional PD control, and an active disturbance rejection control (ADRC) with a sliding mode (SM) compensation is designed in the outer loop. Moreover, the sliding mode compensation is mainly used to cope with the disturbance estimation error from the extended state observer (ESO), by which the insensitivity to tip payload variations and strong disturbance resistance is achieved. Finally, some numerical simulations are performed to support the theoretical analysis. The results show that the system is more robust to the tip mass variations of the arm and more resistant to the external torque after adding the sliding mode robustness term to the ADRC.

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“…Equation (10) guarantees that the vehicle's acceleration and deceleration rates are in a comfortable range. Comfortable acceleration and deceleration rates are considered as below 2 𝑚 𝑠 2 [54,55]. Equation (11) guarantees safety.…”

Section: Optimization Functionmentioning

confidence: 99%

Collective Driving to Mitigate Climate Change: Collective-Adaptive Cruise Control

Vasebi

Hayeri

2021

Sustainability

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The transportation sector is the largest producer of greenhouse gas (GHG) emissions in the United States. Energy-optimal algorithms are proposed to reduce the transportation sector’s fuel consumption and emissions. These algorithms optimize vehicles’ speed to lower energy consumption and emissions. However, recent studies argued that these algorithms could negatively impact traffic flow, create traffic congestions, and increase fuel consumption on the network-level. To overcome this problem, we propose a collective-energy-optimal adaptive cruise control (collective-ACC). Collective-ACC reduces fuel consumption and emissions by directly optimizing vehicles’ trajectories and indirectly by improving traffic flow. Collective-ACC is a bi-objective non-linear integer optimization. This optimization was solved by the Non-dominated Sorting Genetic Algorithm (NSGA-II). Collective-ACC was compared with manual driving and self-centered adaptive cruise control (i.e., conventional energy-optimal adaptive cruise controls (self-centered-ACC)) in a traffic simulation. We found that collective-ACC reduced fuel consumption by up to 49% and 42% compared with manual driving and self-centered-ACC, respectively. Collective-ACC also lowered CO2, CO, NOX, and PMX by up to 54%, 70%, 58%, and 64% from manual driving, respectively. Game theory analyses were conducted to investigate how adopting collective-ACC could impact automakers, consumers, and government agencies. We propose policy and business recommendations to accelerate adopting collective-ACC and maximize its environmental benefits.

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An Optimization Design of Adaptive Cruise Control System Based on MPC and ADRC

Cited by 29 publications

References 35 publications

SDC-Net: End-to-End Multitask Self-Driving Car Camera Cocoon IoT-Based System

SDC-Net: End-to-End Multitask Self-Driving Car Camera Cocoon IoT-Based System

Sliding Mode Robust Active Disturbance Rejection Control for Single-Link Flexible Arm with Large Payload Variations

Collective Driving to Mitigate Climate Change: Collective-Adaptive Cruise Control

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