# Methods for Multi-objective Optimization PID Controller for quadrotor UAVs

arXiv: https://arxiv.org/abs/2509.17423 · HTML: https://mlsanity.com/paper/2509.17423/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 7 of its 70 references that are in the map.
- First posted on arXiv 22 Sep 2025.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 7 of 7)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [A Tour of Reinforcement Learning: The View from Continuous Control](https://mlsanity.com/paper/1806.09460/) (2018)
- [Deep Reinforcement Learning for Multi-objective Optimization](https://mlsanity.com/paper/1906.02386/) (2019)
- [Comparison of High-Dimensional Bayesian Optimization Algorithms on BBOB](https://mlsanity.com/paper/2303.00890/) (2023)
- [Investigating Bayesian optimization for expensive-to-evaluate black box functions: Application in fluid dynamics](https://mlsanity.com/paper/2207.09154/) (2022)
- [A Novel Entropy-Maximizing TD3-based Reinforcement Learning for Automatic PID Tuning](https://mlsanity.com/paper/2210.02381/) (2022)
- [Drone Acoustic Analysis for Predicting Psychoacoustic Annoyance via Artificial Neural Networks](https://mlsanity.com/paper/2410.22208/) (2024)

## Abstract (arXiv)

Integrating unmanned aerial vehicles into daily use requires controllers that ensure stable flight, efficient energy use, and reduced noise. Proportional integral derivative controllers remain standard but are highly sensitive to gain selection, with manual tuning often yielding suboptimal trade-offs. This paper studies different optimization techniques for the automated tuning of quadrotor proportional integral derivative gains under a unified simulation that couples a blade element momentum based aerodynamic model with a fast deep neural network surrogate, six degrees of freedom rigid body dynamics, turbulence, and a data driven acoustic surrogate model that predicts third octave spectra and propagates them to ground receivers. We compare three families of gradient-free optimizers: metaheuristics, Bayesian optimization, and deep reinforcement learning. Candidate controllers are evaluated using a composite cost function that incorporates multiple metrics, such as noise footprint and power consumption, simultaneously. Metaheuristics improve performance consistently, with Grey Wolf Optimization producing optimal results. Bayesian optimization is sample efficient but carries higher per iteration overhead and depends on the design domain. The reinforcement learning agents do not surpass the baseline in the current setup, suggesting the problem formulation requires further refinement. On unseen missions the best tuned controller maintains accurate tracking while reducing oscillations, power demand, and acoustic emissions. These results show that noise aware proportional integral derivative tuning through black box search can deliver quieter and more efficient flight without hardware changes.