Trajectory Tracking Control of a Batch Process using Deep Reinforcement Learning

Batch processes are indispensable for the production of low-volume and high-value products. However, control of a batch process is challenging due to the inherent nonlinearity, and time-varying characteristics. In this work, we consider the problem of nonlinear trajectory tracking control in batch processes. We assume that the reference trajectory is known in advance, and does not change between batches as it is common in repetitive processes. The main objective of this work is to develop a reinforcement learning approach to perform tracking control in a repetitive batch process. To this end, the batch process control is formulated as a Markov decision process with a suitable characterization of state vector, and reward design. A model-free function approximation approach based on deep Q learning is developed as a Deep Reinforcement Learning (DRL) controller. A nonlinear batch reactor system is used to demonstrate the efficacy of the proposed DRL controller. Further, we present the performance of the proposed DRL controller under feed and parametric uncertainties.