Self-Tuning PID Control via a Hybrid Actor-Critic-Based Neural Structure for Quadcopter Control

TL;DR

This paper presents a novel self-tuning PID control method for quadcopters using a hybrid actor-critic neural network, enhancing robustness and performance under uncertainties and disturbances.

Contribution

It introduces an online reinforcement learning-based neural structure for dynamic PID gain tuning, improving quadcopter control robustness and adaptability.

Findings

Enhanced robustness to disturbances and mass uncertainty

Faster training and adaptation compared to traditional methods

Superior control performance with dynamic gain tuning

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.