In this paper, we present a Robust Model Predictive Control (MPC) based on the Singular Value Decomposition (SVD) analysis to handle long prediction and control horizons where numerical instability might appear. The proposed method is developed following the Extended Prediction Self-Adaptive Control (EPSAC) algorithm. The performance of the controller is evaluated in simulation using a 4th order mass–spring–damper system, and the dynamic walking of the humanoid COMAN. The stability of the closed-loop system is analysed using root-locus and Bode plots whilst robustness tests are performed by introducing modelling errors in the prediction model. The results show that the proposed extension increases the robustness of the feedback control, and therefore the operational range of the system.
|Number of pages||13|
|Journal||Robotics and Autonomous Systems|
|Publication status||Published - Dec 2015|