Plant-model mismatch estimation in unconstrained state-space MPC

In this paper, we develop an autocovariance-based method for estimating plant-model mismatch in unconstrained model predictive control systems using discrete-time, linear time-invariant state space models. We rely on knowledge of the process noise model, together with other reasonable assumptions, to derive an explicit expression for the autocovariance matrix of the closed-loop outputs. Then, we prove that by minimizing the discrepancy, in a norm sense, between this theoretical value and the autocovariance matrix of the operating data, we find an asymptotically consistent estimate for the plant-model mismatch. We illustrate the use of this approach with a case study.