Reinforcement learning approach to autonomous PID tuning
Many industrial processes utilize proportional-integral-derivative (PID) controllers due to
their practicality and often satisfactory performance. The proper controller parameters …
their practicality and often satisfactory performance. The proper controller parameters …
PID-inspired inductive biases for deep reinforcement learning in partially observable control tasks
I Char, J Schneider - Advances in Neural Information …, 2024 - proceedings.neurips.cc
Deep reinforcement learning (RL) has shown immense potential for learning to control
systems through data alone. However, one challenge deep RL faces is that the full state of …
systems through data alone. However, one challenge deep RL faces is that the full state of …
Stability-preserving automatic tuning of PID control with reinforcement learning
AI Lakhani, MA Chowdhury, Q Lu - arXiv preprint arXiv:2112.15187, 2021 - arxiv.org
PID control has been the dominant control strategy in the process industry due to its
simplicity in design and effectiveness in controlling a wide range of processes. However …
simplicity in design and effectiveness in controlling a wide range of processes. However …
A Novel Entropy-Maximizing TD3-based Reinforcement Learning for Automatic PID Tuning
MA Chowdhury, Q Lu - 2023 American Control Conference …, 2023 - ieeexplore.ieee.org
Proportional-integral-derivative (PID) controllers have been widely used in the process
industry. However, the satisfactory control performance of a PID controller depends strongly …
industry. However, the satisfactory control performance of a PID controller depends strongly …
Learning-Based Parameter Optimization for a Class of Orbital Tracking Control Laws
This paper presents a learning algorithm for tuning the parameters of a family of stabilizing
nonlinear controllers for orbital tracking, in order to minimize a cost function which combines …
nonlinear controllers for orbital tracking, in order to minimize a cost function which combines …
Reinforcement Learning Based Autonomous E-Vehicle Speed Control
E Saranya, MS Krishnan, S Kaliappan… - 2023 International …, 2023 - ieeexplore.ieee.org
Greater efficiency in both energy use and traffic flow are two benefits of autonomous self-
driving cars. Due to their superior performance, effectiveness, and lack of carbon emission …
driving cars. Due to their superior performance, effectiveness, and lack of carbon emission …
Reinforcement Learning-based Process Control Under Sensory Uncertainty
O Dogru - 2023 - era.library.ualberta.ca
Process industries involve processes that have complex, interdependent, and sometimes
uncontrollable/unobservable features that are subject to a variety of uncertainties such as …
uncontrollable/unobservable features that are subject to a variety of uncertainties such as …
[图书][B] Development of Robotic Ankle Rehabilitation System to Enhance Human Machine Interaction
PB Jephil - 2023 - search.proquest.com
Ankle injuries are quite prevalent and are one of the leading factors that might prevent a
person from engaging in daily activities. These injuries may result from running, falling, a …
person from engaging in daily activities. These injuries may result from running, falling, a …
Advancing Model-Based Reinforcement Learning with Applications in Nuclear Fusion
I Char - kilthub.cmu.edu
Reinforcement learning (RL) may be the key to overcoming previ ous insurmountable
obstacles, leading to technological and scientific innovations. One such example where RL …
obstacles, leading to technological and scientific innovations. One such example where RL …