Reference tracking using H∞ predictive control based on a minimax predictor

We present a reference tracking controller which utilizes a particular form of the internal model principle to achieve asymptotic tracking. The internal model takes the form of a non-Schur weighting filter which is stabilized by inclusion in the closed loop. The filter is combined with the plant yielding a modified plant, with the controller being designed for the modified plant using the polynomial-based H_∞ predictive control scheme. The predictor is unique in that it minimizes the cost function of the prediction error in a minimax sense, yielding minimization of the peaks of the prediction error spectrum. This predictor is then used for the derivation of the H_∞ predictive control law. When applied to the plant, the minimax predictor based H_∞ controller is combined with the internal model to simultaneously provide robust disturbance rejection and asymptotic tracking. The algorithm offers a good tuning means in the form of a prediction horizon which can be used to select the performance/robustness trade-off