Tootfinder

Evaluation of a Robust Control System in Real-World Cable-Driven Parallel Robots
Damir Nurtdinov, Aliaksei Korshuk, Alexei Kornaev, Alexander Maloletov
https://arxiv.org/abs/2510.08270

Evaluation of a Robust Control System in Real-World Cable-Driven Parallel Robots
This study evaluates the performance of classical and modern control methods for real-world Cable-Driven Parallel Robots (CDPRs), focusing on underconstrained systems with limited time discretization. A comparative analysis is conducted between classical PID controllers and modern reinforcement learning algorithms, including Deep Deterministic Policy Gradient (DDPG), Proximal Policy Optimization (PPO), and Trust Region Policy Optimization (TRPO). The results demonstrate that TRPO outperforms ot…

Optimized self-adaptive PID speed control for autonomous vehicles
Yassine Kebbati, Naima Ait-Oufroukh, Vincent Vigneron, Dalil Ichalal, Dominique Gruyer
https://arxiv.org/abs/2509.17214

Optimized self-adaptive PID speed control for autonomous vehicles
The main control tasks in autonomous vehicles are steering (lateral) and speed (longitudinal) control. PID controllers are widely used in the industry because of their simplicity and good performance, but they are difficult to tune and need additional adaptation to control nonlinear systems with varying parameters. In this paper, the longitudinal control task is addressed by implementing adaptive PID control using two different approaches: Genetic Algorithms (GA-PID) and then Neural Networks (N…

Methods for Multi-objective Optimization PID Controller for quadrotor UAVs
Andrea Vaiuso, Gabriele Immordino, Ludovica Onofri, Giuliano Coppotelli, Marcello Righi
https://arxiv.org/abs/2509.17423

Methods for Multi-objective Optimization PID Controller for quadrotor UAVs
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 b…

Rich State Observations Empower Reinforcement Learning to Surpass PID: A Drone Ball Balancing Study
Mingjiang Liu, Hailong Huang
https://arxiv.org/abs/2509.21122 https://…

Rich State Observations Empower Reinforcement Learning to Surpass PID: A Drone Ball Balancing Study
This paper addresses a drone ball-balancing task, in which a drone stabilizes a ball atop a movable beam through cable-based interaction. We propose a hierarchical control framework that decouples high-level balancing policy from low-level drone control, and train a reinforcement learning (RL) policy to handle the high-level decision-making. Simulation results show that the RL policy achieves superior performance compared to carefully tuned PID controllers within the same hierarchical structure…

Coordinated PSO-PID based longitudinal control with LPV-MPC based lateral control for autonomous vehicles
Yassine Kebbati, Naima Ait-Oufroukh, Vincent Vigneron, Dalil Ichalal
https://arxiv.org/abs/2509.19529

Coordinated PSO-PID based longitudinal control with LPV-MPC based lateral control for autonomous vehicles
Autonomous driving is achieved by controlling the coupled nonlinear longitudinal and lateral vehicle dynamics. Longitudinal control greatly affects lateral dynamics and must preserve lateral stability conditions, while lateral controllers must take into account actuator limits and ride comfort. This work deals with the coordinated longitudinal and lateral control for autonomous driving. An improved particle swarm optimized PID (PSO-PID) is proposed to handle the task of speed tracking based on …

Robust Attitude Control of Nonlinear Multi-Rotor Dynamics with LFT Models and $\mathcal{H}_\infty$ Performance
Tanay Kumar, Raktim Bhattacharya
https://arxiv.org/abs/2510.00208 …

Robust Attitude Control of Nonlinear Multi-Rotor Dynamics with LFT Models and $\mathcal{H}_\infty$ Performance
Attitude stabilization of unmanned aerial vehicles in uncertain environments presents significant challenges due to nonlinear dynamics, parameter variations, and sensor limitations. This paper presents a comparative study of $\mathcal{H}_\infty$ and classical PID controllers for multi-rotor attitude regulation in the presence of wind disturbances and gyroscope noise. The flight dynamics are modeled using a linear parameter-varying (LPV) framework, where nonlinearities and parameter variations a…