Design of Adaptive PID Controller Based On Asynchronous Advantage Actor Critic Learning Method for QuadCopter Control

2026-09-17

AI Summary

为解决四旋翼无人机控制难题，本文提出基于异步优势动作评价学习方法动态优化PID参数的自适应控制器，提高系统适应性和控制性能。

Abstract

Quadcopters offer great utility in many applications, but their nonlinear nature and disturbance sensitivity present great control challenges. Basic PID controllers are generally not sophisticated enough to cope with these complexities. This paper suggests a control system that integrates the Asynchronous Advantage Actor-Critic (A3C) algorithm with a PID controller for quadcopter attitude and trajectory tracking. The A3C controller uses parallel agents to optimize PID parameters dynamically using a neural network. A system identification module for the complementary system makes predictions about system states for optimal control policy. The proposed framework was compared with a standard actor-critic (A2C) model. Simulation results verify that they both track accurately. However, the A3C-based controller converges much more for the loss function, as evidenced by reward figures and loss curves, demonstrating better parameter optimization. This shows that A3C-based approach results in improved performance for the control of quadcopter, effectively integrating reinforcement learning and traditional control to achieve higher adaptability.

Problem

• Quadcopter
• Nonlinear
• Disturbance Sensitivity
• PID Controller

Innovation

• Adaptive PID Controller
• Asynchronous Advantage Actor-Critic (A3C)
• Neural Network
• System Identification