A Stochastic MPC approach to controlling biological variable processes

The main challenge when controlling agricultural machinery is the biological variability of the incoming crop. This variability renders any process model time-variable and uncertain. Most robust control techniques start with a computationally intensive initialisation method which has to be repeated each time the model changes. Moreover, these techniques mostly focus on process stability, and not process performance. In this paper a different approach is taken with focus on process performance and attention for the computational complexity. The Stochastic MPC framework is used to design a controller that responds as swiftly as possible at all times. Two models are defined, one for the process variables and one for the model error on each of the process variables. The actual configuration parameters of the Model-based Predictive Controller are then calculated in each time step based on the operator settings and the estimated model error. The model error is also used to convert the deterministic process constraints into stochastic constraints which are respected with a given accuracy. Finally this approach is implemented and validated on a capacity control system for a combine harvester.