Abstract
In this paper the application of model predictive control (MPC) to a two-mode model of the dynamics of the combustion process is considered. It is shown that the MPC by itself does not stabilize the combustor and the control gains obtained by applying the MPC algorithms need to be optimized further to ensure that the phase difference between the two modes is also stable. The results of applying the algorithm are compared with the open loop model amplitude responses and to the closed loop responses obtained by the application of a direct adaptive control algorithm. It is shown that the MPC coupled with the cost parameter optimisation proposed in the paper, always guarantees the closed loop stability, a feature that may not always be possible with an adaptive implementations.
Towards Risk-aware Machine Learning Supported Model Predictive Control and Open-loop Optimization for Repetitive Processes
