Control of van de Vusse Reactor using Deep Reinforcement Learning

van de Vusse reaction system in a continuous stirred tank reactor is a benchmark example to study the control of nonlinear non-minimum phase systems. Although different control strategies exist to deal with such systems, the controller's performance strongly depends on the accuracy of the process model. In this work, we develop a model-free, deep reinforcement learning-based control strategy to perform setpoint tracking control. In particular, we develop a deep Q-learning based controller and demonstrate the tracking performance for different reference trajectories of the desired product concentration. Furthermore, we illustrate the disturbance rejection ability of the proposed deep RL controller.