Trajectory tracking in batch processes using neural controllers
Optimal operation of batch processes usually involves closely following a pre-optimized batch trajectory, e.g., the temperature trajectory in an exothermic batch reactor. Controllers for trajectory tracking have previously been designed and tuned based on a physical or empirical plant model. In batch processes where it is difficult to build a sufficiently accurate model, it is attractive to tune a nonlinear parameterized controller directly on the plant data, provided the number of batch runs required for the iterative tuning remains acceptably low. In a recent work, the authors have proposed a tuning method that makes the best use of each plant run to rigorously calculate the correct gradient for the iterative tuning optimization. In this work, this method is applied to obtain a tuned neural-network controller for tracking the temperature trajectory in an exothermic batch reactor example taken from the literature. Results indicate the efficacy of the method for optimizing a neural controller without requiring an excessive number of batch runs for the trial-and-error iterations.
nonlinear parameterized control batch-process control data-based controller tuning optimal batch operation tracking setpoint trajectory