mlsanityData to 14 Oct 2026

Predictive reinforcement learning based adaptive PID controller

arXiv 2506.08509 ↗First posted 10 Jun 2025eess.SY cs.SYcited by 0

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

Purpose: This study aims to address the challenges of controlling unstable and nonlinear systems by proposing an adaptive PID controller based on predictive reinforcement learning (PRL-PID), where the PRL-PID combines the advantages of both data-driven and model-driven approaches. Design/methodology/approach: A predictive reinforcement learning framework is introduced, incorporating action smooth strategy to suppress overshoot and oscillations, and a hierarchical reward function to support training. Findings: Experimental results show that the PRL-PID controller achieves superior stability and tracking accuracy in nonlinear, unstable, and strongly coupled systems, consistently outperforming existing RL-tuned PID methods while maintaining excellent robustness and adaptability across diverse operating conditions. Originality/Value: By adopting predictive learning, the proposed PRL-PID integrates system model priors into data-driven control, enhancing both the control framework's training efficiency and the controller's stability. As a result, PRL-PID provides a balanced blend of model-based and data-driven approaches, delivering robust, high-performance control.

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 10 of its 36 references that are in the map. First posted on arXiv 10 Jun 2025.

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10 of 36 references.

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Same direction, newest first

  1. 2024Improving a Proportional Integral Controller with Reinforcement Learning on a Throttle Valve Benchmark21 Feb

Other references · 9

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  3. 2018Soft Actor-Critic Algorithms and Applicationscited by 1,240
  4. 2018A Tour of Reinforcement Learning: The View from Continuous Controlcited by 151
  5. 2022A Survey on Model-based Reinforcement Learningcited by 66
  6. 2021Deep Reinforcement Learning with Shallow Controllers: An Experimental Application to PID Tuningcited by 24
  7. 2023Event-Triggered Safe Bayesian Optimization on Quadcopterscited by 4
  8. 2025System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approachcited by 2
  9. 2025Controlling dynamics of stochastic systems with deep reinforcement learningcited by 1

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