Multiple Model Predictive Control: A State Estimation based Approach

An augmented state formulation for multiple model predictive control (MMPC) is developed to improve the regulation of nonlinear and uncertain process systems. By augmenting disturbances as states that are estimated using a Kalman filter, improved disturbance rejection is achieved compared to an additive output disturbance assumption. The approach is applied to a quadratic tank example, which has challenging dynamic behavior, switching from minimum phase to nonminimum phase behavior as the operating conditions are changed.