A New Adaptive Parameter Estimation Algorithm for Robust Constrained Predictive Control

Model predictive control (MPC) combined with adaptation mechanism has been one of the new research topics for the control of constrained uncertain systems. This paper proposes a new adaptive parameter estimation algorithm using input and output data which can explicitly evaluate that the upper bound of parameter estimation error will become smaller in the future. That is, this method enables us to predict the monotonously decreasing future worst-case estimation error bound of uncertain system parameters. This distinctive feature contributes to the development of less conservative robust adaptive-type MPC schemes. Moreover, it basically can be incorporated into not only state-feedback but also output- feedback MPC. As one of the applications, we first present how the discrete-time adaptive state-feedback MPC scheme can be developed based on the proposed method. Then the numerical example shows its key features.