Systematic Evaluation of Initial States and Exploration-Exploitation Strategies in PID Auto-Tuning: A Framework-Driven Approach Applied on Mobile Robots

• URL: http://arxiv.org/abs/2505.03159v1
• Date: Tue, 06 May 2025 04:12:09 GMT
• Title: Systematic Evaluation of Initial States and Exploration-Exploitation Strategies in PID Auto-Tuning: A Framework-Driven Approach Applied on Mobile Robots
• Authors: Zaid Ghazal, Ali Al-Bustami, Khouloud Gaaloul, Jaerock Kwon,
• Abstract summary: The influence of initial system states on convergence and the balance between exploration and exploitation remains underexplored.<n>A novel framework is introduced to evaluate the impact of systematically varying these factors on the PID auto-tuning processes.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: PID controllers are widely used in control systems because of their simplicity and effectiveness. Although advanced optimization techniques such as Bayesian Optimization and Differential Evolution have been applied to address the challenges of automatic tuning of PID controllers, the influence of initial system states on convergence and the balance between exploration and exploitation remains underexplored. Moreover, experimenting the influence directly on real cyber-physical systems such as mobile robots is crucial for deriving realistic insights. In the present paper, a novel framework is introduced to evaluate the impact of systematically varying these factors on the PID auto-tuning processes that utilize Bayesian Optimization and Differential Evolution. Testing was conducted on two distinct PID-controlled robotic platforms, an omnidirectional robot and a differential drive mobile robot, to assess the effects on convergence rate, settling time, rise time, and overshoot percentage. As a result, the experimental outcomes yield evidence on the effects of the systematic variations, thereby providing an empirical basis for future research studies in the field.

Related papers

• Situationally-Aware Dynamics Learning [57.698553219660376]
  We propose a novel framework for online learning of hidden state representations.<n>Our approach explicitly models the influence of unobserved parameters on both transition dynamics and reward structures.<n>Experiments in both simulation and real world reveal significant improvements in data efficiency, policy performance, and the emergence of safer, adaptive navigation strategies.
  arXiv  Detail & Related papers  (2025-05-26T06:40:11Z)
• A Noise-Robust Turn-Taking System for Real-World Dialogue Robots: A Field Experiment [18.814181652728486]
  We propose a noise-robust voice activity projection model to enhance real-time turn-taking in dialogue robots.<n>We conducted a field experiment in a shopping mall, comparing the VAP system with a conventional cloud-based speech recognition system.<n>The results showed that the proposed system significantly reduced response latency, leading to a more natural conversation.
  arXiv  Detail & Related papers  (2025-03-08T14:53:20Z)
• AdaWM: Adaptive World Model based Planning for Autonomous Driving [34.57859869929471]
  World model based reinforcement learning (RL) has emerged as a promising approach for autonomous driving.<n>The pretrain-finetune paradigm is often used, where online RL is performance by a pretrained model and a policy learned offline.<n>We introduce AdaWM, an Adaptive World Model based planning method, featuring two key steps: (a) mismatch identification, which quantifies the mismatches and informs the finetuning strategy, and (b) alignment-driven finetuning, which selectively updates either the policy or the model as needed.
  arXiv  Detail & Related papers  (2025-01-22T18:34:51Z)
• Distributed Robust Learning based Formation Control of Mobile Robots based on Bioinspired Neural Dynamics [14.149584412213269]
  We first introduce a distributed estimator using a variable structure and cascaded design technique, eliminating the need for derivative information to improve the real time performance. Then, a kinematic tracking control method is developed utilizing a bioinspired neural dynamic-based approach aimed at providing smooth control inputs and effectively resolving the speed jump issue. To address the challenges for robots operating with completely unknown dynamics and disturbances, a learning-based robust dynamic controller is developed.
  arXiv  Detail & Related papers  (2024-03-23T04:36:12Z)
• Modelling, Positioning, and Deep Reinforcement Learning Path Tracking Control of Scaled Robotic Vehicles: Design and Experimental Validation [3.807917169053206]
  Scaled robotic cars are commonly equipped with a hierarchical control acthiecture that includes tasks dedicated to vehicle state estimation and control. This paper covers both aspects by proposing (i) a federeted extended Kalman filter (FEKF) and (ii) a novel deep reinforcement learning (DRL) path tracking controller trained via an expert demonstrator. The experimentally validated model is used for (i) supporting the design of the FEKF and (ii) serving as a digital twin for training the proposed DRL-based path tracking algorithm.
  arXiv  Detail & Related papers  (2024-01-10T14:40:53Z)
• NeuroFlow: Development of lightweight and efficient model integration scheduling strategy for autonomous driving system [0.0]
  This paper proposes a specialized autonomous driving system that takes into account the unique constraints and characteristics of automotive systems. The proposed system systematically analyzes the intricate data flow in autonomous driving and provides functionality to dynamically adjust various factors that influence deep learning models.
  arXiv  Detail & Related papers  (2023-12-15T07:51:20Z)
• Leveraging the Power of Data Augmentation for Transformer-based Tracking [64.46371987827312]
  We propose two data augmentation methods customized for tracking. First, we optimize existing random cropping via a dynamic search radius mechanism and simulation for boundary samples. Second, we propose a token-level feature mixing augmentation strategy, which enables the model against challenges like background interference.
  arXiv  Detail & Related papers  (2023-09-15T09:18:54Z)
• Robustness and Generalization Performance of Deep Learning Models on Cyber-Physical Systems: A Comparative Study [71.84852429039881]
  Investigation focuses on the models' ability to handle a range of perturbations, such as sensor faults and noise. We test the generalization and transfer learning capabilities of these models by exposing them to out-of-distribution (OOD) samples.
  arXiv  Detail & Related papers  (2023-06-13T12:43:59Z)
• Tuning Legged Locomotion Controllers via Safe Bayesian Optimization [47.87675010450171]
  This paper presents a data-driven strategy to streamline the deployment of model-based controllers in legged robotic hardware platforms. We leverage a model-free safe learning algorithm to automate the tuning of control gains, addressing the mismatch between the simplified model used in the control formulation and the real system.
  arXiv  Detail & Related papers  (2023-06-12T13:10:14Z)
• Active Learning of Discrete-Time Dynamics for Uncertainty-Aware Model Predictive Control [46.81433026280051]
  We present a self-supervised learning approach that actively models the dynamics of nonlinear robotic systems. Our approach showcases high resilience and generalization capabilities by consistently adapting to unseen flight conditions.
  arXiv  Detail & Related papers  (2022-10-23T00:45:05Z)
• Unified Automatic Control of Vehicular Systems with Reinforcement Learning [64.63619662693068]
  This article contributes a streamlined methodology for vehicular microsimulation. It discovers high performance control strategies with minimal manual design. The study reveals numerous emergent behaviors resembling wave mitigation, traffic signaling, and ramp metering.
  arXiv  Detail & Related papers  (2022-07-30T16:23:45Z)
• Isolating and Leveraging Controllable and Noncontrollable Visual Dynamics in World Models [65.97707691164558]
  We present Iso-Dream, which improves the Dream-to-Control framework in two aspects. First, by optimizing inverse dynamics, we encourage world model to learn controllable and noncontrollable sources. Second, we optimize the behavior of the agent on the decoupled latent imaginations of the world model.
  arXiv  Detail & Related papers  (2022-05-27T08:07:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.