cs.AI updates on arXiv.org 13小时前
Self-Tuning PID Control via a Hybrid Actor-Critic-Based Neural Structure for Quadcopter Control
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本文提出一种基于强化学习的自调PID控制器,用于四旋翼飞行器的姿态和高度控制,通过模型无关的混合神经网络结构进行PID参数调整,提高控制器对模型参数不确定性和外部干扰的鲁棒性。

arXiv:2307.01312v2 Announce Type: replace-cross Abstract: Proportional-Integrator-Derivative (PID) controller is used in a wide range of industrial and experimental processes. There are a couple of offline methods for tuning PID gains. However, due to the uncertainty of model parameters and external disturbances, real systems such as Quadrotors need more robust and reliable PID controllers. In this research, a self-tuning PID controller using a Reinforcement-Learning-based Neural Network for attitude and altitude control of a Quadrotor has been investigated. An Incremental PID, which contains static and dynamic gains, has been considered and only the variable gains have been tuned. To tune dynamic gains, a model-free actor-critic-based hybrid neural structure was used that was able to properly tune PID gains, and also has done the best as an identifier. In both tunning and identification tasks, a Neural Network with two hidden layers and sigmoid activation functions has been learned using Adaptive Momentum (ADAM) optimizer and Back-Propagation (BP) algorithm. This method is online, able to tackle disturbance, and fast in training. In addition to robustness to mass uncertainty and wind gust disturbance, results showed that the proposed method had a better performance when compared to a PID controller with constant gains.

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PID控制器 四旋翼飞行器 强化学习 神经网络 鲁棒性
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