A Feedback Force Controller Fusing Traditional Control and Reinforcement Learning Strategies

Chen, Bo-Hsun; Wang, Yu‐Hsun; Lin, Pei-Chun

doi:10.1109/aim.2019.8868711

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…The training results depend on the setting of random seeds, which is unstable. This is the reason why the current DRL algorithm is mainly implemented on the simulation platform and is difficult to apply to the real vehicle [29].…”

Section: Introductionmentioning

confidence: 99%

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

2021

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Cooperative adaptive cruise control (CACC) has important significance for the development of the connected and automated vehicle (CAV) industry. The traditional proportional integral derivative (PID) platoon controller adjustment is not only time-consuming and laborious, but also unable to adapt to different working conditions. This paper proposes a learning control method for a vehicle platooning system using a deep deterministic policy gradient (DDPG)-based PID. The main contribution of this study is automating the PID weight tuning process by formulating this objective as a deep reinforcement learning (DRL) problem. The longitudinal control of the vehicle platooning is divided into upper and lower control structures. The upper-level controller based on the DDPG algorithm can adjust the current PID controller parameters. Through offline training and learning in a SUMO simulation software environment, the PID controller can adapt to different road and vehicular platooning acceleration and deceleration conditions. The lower-level controller controls the gas/brake pedal to accurately track the desired acceleration and speed. Based on the hardware-in-the-loop (HIL) simulation platform, the results show that in terms of the maximum speed error, for the DDPG-based PID controller this is 0.02–0.08 m/s less than for the conventional PID controller, with a maximum reduction of 5.48%. In addition, the maximum distance error of the DDPG-based PID controller is 0.77 m, which is 14.44% less than that of the conventional PID controller.

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Section: Introductionmentioning

confidence: 99%

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

2021

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“…In addition, this work utilized different concepts in comparison with our previous work, [30] which set the PID controller as the main control role and deep RL as the compensation controller. This work set the PID controller as the assistant for trajectory tracking and the deep imitation learning as the main trajectory generator that produced the complete trajectories offline before the task.…”

Section: Introductionmentioning

confidence: 99%

Deep Imitation Learning for Optimal Trajectory Planning and Initial Condition Optimization for an Unstable Dynamic System

Chen,

Lin

2023

Advanced Intelligent Systems

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In this article, an innovative offline deep imitation learning algorithm for optimal trajectory planning is proposed. While many state‐of‐the‐art works achieved optimal trajectory planning, their systems were stable or quasistable, and their approaches rarely optimized the system's initial conditions (ICs). Here, a new unstable dynamic system task called “internal sliding object stabilization control” is proposed, modeled, and solved by deep imitation learning. Given the system's ICs, the neural networks (NNs) can imitate the iterative linear quadratic regulator (iLQR), generate optimal trajectories, and compute faster. A proportional–integral–derivative (PID) controller is used to track the unstable trajectories. Leveraging on the gradients of NNs, it can optimize the system's ICs, avoid obstacles stepwise, and ensure the worst bounds of NNs for safety. Subsequently, thorough simulations are conducted, including comparing the iLQR and PID controllers in the task, optimizing the system's different ICs by gradient descent, and finding the worst bound of the performance by gradient ascent. Results show that the proposed algorithm achieves considerably improved performance. Finally, experiments are conducted with a real manipulator to compare the proposed structure with the original iLQR. Results indicate that the proposed algorithm resembles the iLQR well. Program code and experiment results are in https://github.com/DanielYamChen/ISOSC.git.

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An adaptive impedance control method for blade polishing based on the Kalman filter

Zhao,

Liu,

Yang

et al. 2024

Int J Adv Manuf Technol

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A Feedback Force Controller Fusing Traditional Control and Reinforcement Learning Strategies

Cited by 5 publications

References 18 publications

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

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

Deep Imitation Learning for Optimal Trajectory Planning and Initial Condition Optimization for an Unstable Dynamic System

An adaptive impedance control method for blade polishing based on the Kalman filter

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