A Learning Control Method of Automated Vehicle Platoon at Straight Path with DDPG-Based PID

Yang, Junru; Wei-feng, Peng; Sun, Chuan

doi:10.3390/electronics10212580

Cited by 18 publications

(9 citation statements)

References 50 publications

(58 reference statements)

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“…Driving in convoy can result in reduced demand for drivers, ultimately lowering manpower costs. Furthermore, it can be highly effective in expanding road capacity, enhancing transport efficiency, and reducing air resistance of the trailing vehicle, leading to lower fuel consumption and a decrease in air pollution [19]. The practice can also yield significant benefits to improve vehicle economy and combat environmental pollution.…”

Section: Vehicle Formation Control Systemmentioning

confidence: 99%

Design of a Collaborative Vehicle Formation Control Simulation Test System

Xu,

Zhang,

Ding

et al. 2023

Electronics

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The purpose of this research is to tackle one of the most difficult issues in the realm of self-driving cars, which is the testing of advanced self-driving application scenarios. Thus, this study proposes a simulation testing system based on hardware-in-the-loop simulation technology. This system can enable data exchange between hardware systems as well as replicate and evaluate the algorithmic operations of the equipment under laboratory conditions. The system can integrate scenario simulation software with MATLAB to evaluate algorithm performance. The vehicle formation control system is tailored for collaborative vehicle formation management scenarios and tested in the simulation test system. The findings display the functional integrity of the vehicle formation control system, the reliability of lane changing and the stability and safety of cruising. It additionally demonstrates that the simulation testing system has the ability to recreate cooperative vehicle arrangement management situations and assess their functionality and performance. In forthcoming research, comprehensive functional and performance assessments will be executed on various typical scenarios for advanced autonomous driving applications in order to authenticate the simulation test system’s applicability.

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Section: Vehicle Formation Control Systemmentioning

confidence: 99%

Design of a Collaborative Vehicle Formation Control Simulation Test System

Xu,

Zhang,

Ding

et al. 2023

Electronics

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“…To enhance the controller performance, the DDPG has to study the regulation strategy µ (actor network) and calculate the probability of each action. Consequently, the controller parameters are updated in real time to maximize the total reward [25,26].…”

Section: Ddpg-based Smcesomentioning

confidence: 99%

DDPG-Based Adaptive Sliding Mode Control with Extended State Observer for Multibody Robot Systems

Khan,

Lee

et al. 2023

Robotics

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This research introduces a robust control design for multibody robot systems, incorporating sliding mode control (SMC) for robustness against uncertainties and disturbances. SMC achieves this through directing system states toward a predefined sliding surface for finite-time stability. However, the challenge arises in selecting controller parameters, specifically the switching gain, as it depends on the upper bounds of perturbations, including nonlinearities, uncertainties, and disturbances, impacting the system. Consequently, gain selection becomes challenging when system dynamics are unknown. To address this issue, an extended state observer (ESO) is integrated with SMC, resulting in SMCESO, which treats system dynamics and disturbances as perturbations and estimates them to compensate for their effects on the system response, ensuring robust performance. To further enhance system performance, deep deterministic policy gradient (DDPG) is employed to fine-tune SMCESO, utilizing both actual and estimated states as input states for the DDPG agent and reward selection. This training process enhances both tracking and estimation performance. Furthermore, the proposed method is compared with the optimal-PID, SMC, and H∞ in the presence of external disturbances and parameter variation. MATLAB/Simulink simulations confirm that overall, the SMCESO provides robust performance, especially with parameter variations, where other controllers struggle to converge the tracking error to zero.

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“…As the measurement algorithm, the PID-based CACC algorithm [25] is employed. The tested CACC algorithm is suitably simplified and the parameters are shown in Table 6.…”

Section: Cacc Testmentioning

confidence: 99%

A Hardware-in-the-Loop V2X Simulation Framework: CarTest

Wang

Zhu

2022

Sensors

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Vehicle to Everything (V2X) technology is fast evolving, and it will soon transform our driving experience. Vehicles employ On-Board Units (OBUs) to interact with various V2X devices, and these data are used for calculation and detection. Safety, efficiency, and information services are among its core uses, which are currently in the testing stage. Developers gather logs during the real field test to see if the application is fair. Field testing, on the other hand, has low efficiency, coverage, controllability, and stability, as well as the inability to recreate extreme hazardous scenarios. The shortcomings of actual road testing can be compensated for by indoor testing. An HIL-based laboratory simulation test framework for V2X-related testing is built in this study, together with the relevant test cases and a test evaluation system. The framework can test common applications such as Forward Collision Warning (FCW), Intersection Collision Warning (ICW) and others, as well as more advanced features such as Cooperative Adaptive Cruise Control (CACC) testing and Global Navigation Satellite System (GNSS) injection testing. The results of the tests reveal that the framework (CarTest) has reliable output, strong repeatability, the capacity to simulate severe danger scenarios, and is highly scalable, according to this study. Meanwhile, for the benefit of researchers, this publication highlights several relevant HIL challenges and solutions.

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A Learning Control Method of Automated Vehicle Platoon at Straight Path with DDPG-Based PID

Cited by 18 publications

References 50 publications

Design of a Collaborative Vehicle Formation Control Simulation Test System

Design of a Collaborative Vehicle Formation Control Simulation Test System

DDPG-Based Adaptive Sliding Mode Control with Extended State Observer for Multibody Robot Systems

A Hardware-in-the-Loop V2X Simulation Framework: CarTest

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