45 citations · 91 across the 4 of their papers we have counts for
4 papers
Meta-Reinforcement Learning for the Tuning of PI Controllers: An Offline Approach
Daniel G. McClement, Nathan P. Lawrence, Johan U. Backstrom +3
Meta-learning is a branch of machine learning which trains neural network models to synthesize a wide variety of data in order to rapidly solve new problems. In process control, ma…
Almost Surely Stable Deep Dynamics
Nathan P. Lawrence, Philip D. Loewen, Michael G. Forbes +2
We introduce a method for learning provably stable deep neural network based dynamic models from observed data. Specifically, we consider discrete-time stochastic dynamic models, a…
Optimal PID and Antiwindup Control Design as a Reinforcement Learning Problem
Nathan P. Lawrence, Gregory E. Stewart, Philip D. Loewen +3
Deep reinforcement learning (DRL) has seen several successful applications to process control. Common methods rely on a deep neural network structure to model the controller or pro…
Reinforcement Learning based Design of Linear Fixed Structure Controllers
Nathan P. Lawrence, Gregory E. Stewart, Philip D. Loewen +3
Reinforcement learning has been successfully applied to the problem of tuning PID controllers in several applications. The existing methods often utilize function approximation, su…