A Reinforcement Learning-based Economic Model Predictive Control Framework for Autonomous Operation of Chemical Reactors
Economic model predictive control (EMPC) is a promising methodology for optimal operation of dynamical processes that has been shown to improve process economics considerably. However, EMPC performance relies heavily on the accuracy of the process model used. As an alternative to model-based control strategies, reinforcement learning (RL) has been investigated as a model-free control methodology, but issues regarding its safety and stability remain an open research challenge. This work presents a novel framework for integrating EMPC and RL for online model parameter estimation of a class of nonlinear systems. In this framework, EMPC optimally operates the closed loop system while maintaining closed loop stability and recursive feasibility. At the same time, to optimize the process, the RL agent continuously compares the measured state of the process with the model's predictions (nominal states), and modifies model parameters accordingly. The major advantage of this framework is its simplicity; state-of-the-art RL algorithms and EMPC schemes can be employed with minimal modifications. The performance of the proposed framework is illustrated on a network of reactions with challenging dynamics and practical significance. This framework allows control, optimization, and model correction to be performed online and continuously, making autonomous reactor operation more attainable.
Code (0)
등록된 구현이 없습니다.
Tasks
Model Predictive Controlparameter estimationReinforcement Learning (RL)Similar Papers 제목 키워드 기반
RL-Guided MPC for Autonomous Greenhouse Control
The efficient operation of greenhouses is essential for enhancing crop yield while minimizing energy costs. This paper investigates a control strategy that integrates Reinforcement Learning (RL) and Model Predictive Cont…
Model Predictive ControlReinforcement Learning (RL)Reinforcement Learning based on Scenario-tree MPC for ASVs
In this paper, we present the use of Reinforcement Learning (RL) based on Robust Model Predictive Control (RMPC) for the control of an Autonomous Surface Vehicle (ASV). The RL-MPC strategy is utilized for obstacle avoida…
Model Predictive ControlPoint TrackingQ-Learningreinforcement-learning+2Deep Reinforcement-Learning-Guided Model Predictive Control for Preventing Overtakes in Autonomous Racing
This paper addresses defensive blocking in autonomous racing, where a vehicle must prevent a faster opponent from overtaking while operating near its dynamic limits. Different from lap-time minimization, we formulate def…
End-to-End Reinforcement Learning of Koopman Models for Economic Nonlinear Model Predictive Control
(Economic) nonlinear model predictive control ((e)NMPC) requires dynamic models that are sufficiently accurate and computationally tractable. Data-driven surrogate models for mechanistic models can reduce the computation…
Model Predictive Controlreinforcement-learningReinforcement LearningCoordinated Energy-Trajectory Economic Model Predictive Control for Autonomous Surface Vehicles under Disturbances
The paper proposes a novel Economic Model Predictive Control (EMPC) scheme for Autonomous Surface Vehicles (ASVs) to simultaneously address path following accuracy and energy constraints under environmental disturbances.…
CPUModel Predictive Control