Model Predictive Control

The optimal control sequence generated offline does not help realize optimal control in actual systems due to state deviations that are not captured in the state-space model. Such deviations are caused by unmodeled dynamics and noise, leaving only the first few control signals in the optimal control sequence valid and practically useful. In this context, the model predictive control (MPC) is presented in which an optimal control sequence is generated for a finite time into the future (the prediction horizon), and only the first few control signals (the control horizon) are practically used. After each control horizon, the process recurs in its actual state at that time.