Disturbance suppression via robust MPC using prior disturbance data: Low computational complexity method

This note addresses disturbance suppression problem for uncertain plant systems using prior disturbance data which contain some measurement errors, and gives a further result which attains lower computational complexity than our previously proposed method. Under the conditions that the plant uncertainties are assumed to be expressed by bounded but time-invariant uncertain delays at the control input and the measurement errors are expressed as affine with respect to some bounded constant uncertain vector, we have already proposed a MPC-based controller which achieves disturbance suppression with robustness against the plant uncertainties and the measurement errors. In this paper, we tackle the same problem but under the condition that the implemented computing power is limited. For this problem, we propose a simple methodology, i.e. constant inputs are supposed for several steps. We apply our proposed method to flight controller design problem to suppress vertical acceleration driven by turbulence, i.e. Gust Alleviation (GA) flight controller design problem, and demonstrate the applicability with numerical simulations.