Q-Learning-Based Multivariate Nonlinear Model Predictive Controller: Experimental Validation on Batch Reactor for Temperature Trajectory Tracking
Document Type
Article
Publication Title
ACS Omega
Abstract
This study introduces a Q-learning-based nonlinear model predictive control (QL-NMPC) framework for temperature control in batch reactors. A reinforcement learning agent is trained in simulation to learn optimal control strategies using coolant flow rate and heater current as inputs. The resulting policy, represented as a Q-table, is implemented in real time on a physical reactor setup using the NVIDIA Jetson Orin platform. The proposed QL-NMPC framework employs a value iteration-based Q-learning algorithm, enabling model-free policy optimization without explicit policy evaluation steps, and demonstrates effective temperature tracking while highlighting the potential of reinforcement learning for controlling nonlinear batch processes without relying on system identification.
First Page
28362
Last Page
28371
DOI
10.1021/acsomega.5c03482
Publication Date
7-8-2025
Recommended Citation
Vegesna, Abhiram Varma; Shamaiah Narayanarao, Muralikrishna; Bhamidipati, Kishore; and Indiran, Thirunavukkarasu, "Q-Learning-Based Multivariate Nonlinear Model Predictive Controller: Experimental Validation on Batch Reactor for Temperature Trajectory Tracking" (2025). Open Access archive. 12926.
https://impressions.manipal.edu/open-access-archive/12926
