146 citations · 435 across the 22 of their papers we have counts for
22 papers
Error whitening: Why Gauss-Newton outperforms Newton
Maricela Best McKay, Nathan P. Lawrence, Brian Wetton +1
The Gauss-Newton matrix is widely viewed as a positive semidefinite approximation of the Hessian, yet mounting empirical evidence shows that Gauss-Newton descent outperforms Newton…
DiLQR: Differentiable Iterative Linear Quadratic Regulator via Implicit Differentiation
Shuyuan Wang, Philip D. Loewen, Michael Forbes +2
While differentiable control has emerged as a powerful paradigm combining model-free flexibility with model-based efficiency, the iterative Linear Quadratic Regulator (iLQR) remain…
A view on learning robust goal-conditioned value functions: Interplay between RL and MPC
Nathan P. Lawrence, Philip D. Loewen, Michael G. Forbes +2
Reinforcement learning (RL) and model predictive control (MPC) offer a wealth of distinct approaches for automatic decision-making under uncertainty. Given the impact both fields h…
Guiding Reinforcement Learning with Incomplete System Dynamics
Shuyuan Wang, Jingliang Duan, Nathan P. Lawrence +4
Model-free reinforcement learning (RL) is inherently a reactive method, operating under the assumption that it starts with no prior knowledge of the system and entirely depends on…
Deep Hankel matrices with random elements
Nathan P. Lawrence, Philip D. Loewen, Shuyuan Wang +2
Willems' fundamental lemma enables a trajectory-based characterization of linear systems through data-based Hankel matrices. However, in the presence of measurement noise, we ask:…
Stabilizing reinforcement learning control: A modular framework for optimizing over all stable behavior
Nathan P. Lawrence, Philip D. Loewen, Shuyuan Wang +2
We propose a framework for the design of feedback controllers that combines the optimization-driven and model-free advantages of deep reinforcement learning with the stability guar…
Reinforcement Learning with Partial Parametric Model Knowledge
Shuyuan Wang, Philip D. Loewen, Nathan P. Lawrence +2
We adapt reinforcement learning (RL) methods for continuous control to bridge the gap between complete ignorance and perfect knowledge of the environment. Our method, Partial Knowl…
A modular framework for stabilizing deep reinforcement learning control
Nathan P. Lawrence, Philip D. Loewen, Shuyuan Wang +2
We propose a framework for the design of feedback controllers that combines the optimization-driven and model-free advantages of deep reinforcement learning with the stability guar…
Data Quality Over Quantity: Pitfalls and Guidelines for Process Analytics
Lim C. Siang, Shams Elnawawi, Lee D. Rippon +2
A significant portion of the effort involved in advanced process control, process analytics, and machine learning involves acquiring and preparing data. Literature often emphasizes…
Modern Machine Learning Tools for Monitoring and Control of Industrial Processes: A Survey
R. Bhushan Gopaluni, Aditya Tulsyan, Benoit Chachuat +6
Over the last ten years, we have seen a significant increase in industrial data, tremendous improvement in computational power, and major theoretical advances in machine learning.…
Meta-Reinforcement Learning for Adaptive Control of Second Order Systems
Daniel G. McClement, Nathan P. Lawrence, Michael G. Forbes +3
Meta-learning is a branch of machine learning which aims to synthesize data from a distribution of related tasks to efficiently solve new ones. In process control, many systems hav…
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…
Deep Reinforcement Learning with Shallow Controllers: An Experimental Application to PID Tuning
Nathan P. Lawrence, Michael G. Forbes, Philip D. Loewen +3
Deep reinforcement learning (RL) is an optimization-driven framework for producing control strategies for general dynamical systems without explicit reliance on process models. Goo…
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…
A Meta-Reinforcement Learning Approach to Process Control
Daniel G. McClement, Nathan P. Lawrence, Philip D. Loewen +3
Meta-learning is a branch of machine learning which aims to quickly adapt models, such as neural networks, to perform new tasks by learning an underlying structure across related t…
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…
Deep Reinforcement Learning for Process Control: A Primer for Beginners
Steven Spielberg, Aditya Tulsyan, Nathan P. Lawrence +2
Advanced model-based controllers are well established in process industries. However, such controllers require regular maintenance to maintain acceptable performance. It is a commo…
Input design for Bayesian identification of non-linear state-space models
Aditya Tulsyan, Swanand R. Khare, Biao Huang +2
We propose an algorithm for designing optimal inputs for on-line Bayesian identification of stochastic non-linear state-space models. The proposed method relies on minimization of…
Error analysis in Bayesian identification of non-linear state-space models
Aditya Tulsyan, Biao Huang, R. Bhushan Gopaluni +1
In the last two decades, several methods based on sequential Monte Carlo (SMC) and Markov chain Monte Carlo (MCMC) have been proposed for Bayesian identification of stochastic non-…
On-line Bayesian parameter estimation in general non-linear state-space models: A tutorial and new results
Aditya Tulsyan, Biao Huang, R. Bhushan Gopaluni +1
On-line estimation plays an important role in process control and monitoring. Obtaining a theoretical solution to the simultaneous state-parameter estimation problem for non-linear…
A particle filter approach to approximate posterior Cramér-Rao lower bound
Aditya Tulsyan, Biao Huang, R. Bhushan Gopaluni +1
The posterior Cramér-Rao lower bound (PCRLB) derived in Tichavský et al., 1998, provides a bound on the mean square error (MSE) obtained with any non-linear state filter. Computing…