Search Results for author:
Found 9 papers, 2 papers with code
ARRTOC: Adversarially Robust Real-Time Optimization and Control
To address this, in this paper, we present the Adversarially Robust Real-Time Optimization and Control (ARRTOC) algorithm.
An Analysis of Multi-Agent Reinforcement Learning for Decentralized Inventory Control Systems
Most solutions to the inventory management problem assume a centralization of information that is incompatible with organisational constraints in real supply chain networks.
Tube-based Distributionally Robust Model Predictive Control for Nonlinear Process Systems via Linearization
Unlike SMPC, which requires the exact knowledge of the disturbance distribution, our scheme decides the control action with respect to the worst distribution from a distribution ambiguity set.
Neural ODEs as Feedback Policies for Nonlinear Optimal Control
Neural ordinary differential equations (Neural ODEs) define continuous time dynamical systems with neural networks.
Learning Linear Representations of Nonlinear Dynamics Using Deep Learning
The vast majority of systems of practical interest are characterised by nonlinear dynamics.
Safe Real-Time Optimization using Multi-Fidelity Gaussian Processes
This paper proposes a new class of real-time optimization schemes to overcome system-model mismatch of uncertain processes.
Integrating process design and control using reinforcement learning
For this, one can formulate a bilevel optimization problem, with the design as the outer problem in the form of a mixed-integer nonlinear program (MINLP) and a stochastic optimal control as the inner problem.
Real-Time Optimization Meets Bayesian Optimization and Derivative-Free Optimization: A Tale of Modifier Adaptation
This paper investigates a new class of modifier-adaptation schemes to overcome plant-model mismatch in real-time optimization of uncertain processes.
Chance Constrained Policy Optimization for Process Control and Optimization
We propose a chance constrained policy optimization (CCPO) algorithm which guarantees the satisfaction of joint chance constraints with a high probability - which is crucial for safety critical tasks.
