Output-feedback model predictive control with constraints - improve robustness using past information

We present nonlinear model predictive control design for a type of discrete-time nonlinear system. Range constraints are imposed on input, output and input rate. When states are not available, an observer based on Newton´s algorithm is used to estimate all states. We also present an idea to improve robustness of the control system when plant is subjected to modeling uncertainties. By using past information, the correction to the plant model can be computed from the difference between past actual states and past model states using a simple linear least-square algorithm. The correction is applied to the plant model in the future time step. Two illustrative examples are included