Complexity reduction of Model Predictive Control for a de-manufacturing plant

Abstract

The use of advanced optimization-based techniques will be a fundamental step towards performance enhancement of flexible manufacturing plants. However, the mixed integer nature of the resulting optimization problems and the associated computational issues can represent a bottleneck and a severe limitation to their diffusion. This paper describes the design and efficient implementation of a Model Predictive Control (MPC) algorithm for a de-manufacturing plant. To reduce the computational effort due to the on-line optimization, the main idea is to resort to the so-called control horizon, widely used in classical MPC applications. A heuristic control strategy, requiring a lower computational effort, is first designed. Then, the MPC algorithm is implemented by considering a prediction horizon larger than the control horizon, and assuming that the heuristic rules are used from the end of the control horizon onwards. This significantly reduces the number of optimization variables to be computed through the solution of a Mixed Integer Linear Programming (MILP) problem. Notably, by adopting a rolling horizon strategy, the heuristic rules are never applied in practice. Simulation results are reported to compare the performances of the algorithm here developed, in terms of computational time and plant throughput, to those of a standard MPC problem and of the heuristic rules. These results witness the ability to the developed method to reduce the on-line optimization time without a significant performance reduction with respect to the standard MPC.