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Found 16 papers, 3 papers with code
Deep Hankel matrices with random elements
Willems' fundamental lemma enables a trajectory-based characterization of linear systems through data-based Hankel matrices.
Machine learning for industrial sensing and control: A survey and practical perspective
Soft sensing contains a wealth of industrial applications of statistical and machine learning methods.
Stabilizing reinforcement learning control: A modular framework for optimizing over all stable behavior
For the training of reinforcement learning agents, the set of all stable linear operators is given explicitly through a matrix factorization approach.
Reinforcement Learning with Partial Parametric Model Knowledge
We adapt reinforcement learning (RL) methods for continuous control to bridge the gap between complete ignorance and perfect knowledge of the environment.
A modular framework for stabilizing deep reinforcement learning control
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 guarantees provided by using the Youla-Kucera parameterization to define the search domain.
Automated deep reinforcement learning for real-time scheduling strategy of multi-energy system integrated with post-carbon and direct-air carbon captured system
However, the adoption of CDRT is not economically viable at the current carbon price.
Data Quality Over Quantity: Pitfalls and Guidelines for Process Analytics
A significant portion of the effort involved in advanced process control, process analytics, and machine learning involves acquiring and preparing data.
Modern Machine Learning Tools for Monitoring and Control of Industrial Processes: A Survey
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
In this work, we briefly reintroduce our methodology and demonstrate how it can be extended to proportional-integral-derivative controllers and second order systems.
Meta-Reinforcement Learning for the Tuning of PI Controllers: An Offline Approach
In tests reported here, the meta-RL agent was trained entirely offline on first order plus time delay systems, and produced excellent results on novel systems drawn from the same distribution of process dynamics used for training.
Deep Reinforcement Learning with Shallow Controllers: An Experimental Application to PID Tuning
In addition to its simplicity, this approach has several appealing features: No additional hardware needs to be added to the control system, since a PID controller can easily be implemented through a standard programmable logic controller; the control law can easily be initialized in a "safe'' region of the parameter space; and the final product -- a well-tuned PID controller -- has a form that practitioners can reason about and deploy with confidence.
Almost Surely Stable Deep Dynamics
We introduce a method for learning provably stable deep neural network based dynamic models from observed data.
A Meta-Reinforcement Learning Approach to Process Control
Meta-learning is appealing for process control applications because the perturbations to a process required to train an AI controller can be costly and unsafe.
Reinforcement Learning based Design of Linear Fixed Structure Controllers
Reinforcement learning has been successfully applied to the problem of tuning PID controllers in several applications.
Optimal PID and Antiwindup Control Design as a Reinforcement Learning Problem
In this work, we focus on the interpretability of DRL control methods.
On-line Bayesian parameter estimation in general non-linear state-space models: A tutorial and new results
The simultaneous estimation is performed by filtering an extended vector of states and parameters using an adaptive sequential-importance-resampling (SIR) filter with a kernel density estimation method.
