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Found 12 papers, 5 papers with code
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.
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.
Safe Chance Constrained Reinforcement Learning for Batch Process Control
Specifically, we propose a data-driven approach that utilizes Gaussian processes for the offline simulation model and use the associated posterior uncertainty prediction to account for joint chance constraints and plant-model mismatch.
Safe model-based design of experiments using Gaussian processes
Construction of kinetic models has become an indispensable step in the development and scale up of processes in the industry.
Constrained Model-Free Reinforcement Learning for Process Optimization
We propose an 'oracle'-assisted constrained Q-learning algorithm that guarantees the satisfaction of joint chance constraints with a high probability, which is crucial for safety critical tasks.
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.
Constrained Reinforcement Learning for Dynamic Optimization under Uncertainty
We use chance constraints to guarantee the probabilistic satisfaction of process constraints, which is accomplished by introducing backoffs, such that the optimal policy and backoffs are computed simultaneously.
Reinforcement Learning for Batch Bioprocess Optimization
In this work, we applied the Policy Gradient method from batch-to-batch to update a control policy parametrized by a recurrent neural network.
Optimization and Control Systems and Control
Input-Output Stability of Barrier-Based Model Predictive Control
Conditions for input-output stability of barrier-based model predictive control of linear systems with linear and convex nonlinear (hard or soft) constraints are established through the construction of integral quadratic constraints (IQCs).
Systems and Control
