Enhancing the robustness of the EPSAC predictive control using a Singular Value Decomposition approach

Juan A. Castano, Andres Hernandez, Zhibin Li, Nikos G. Tsagarakis, Darwin G. Caldwell, Robin De Keyser

Research output: Contribution to journalArticlepeer-review

Abstract

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.
Original languageEnglish
Pages (from-to)283-295
Number of pages13
JournalRobotics and Autonomous Systems
Volume74
Issue numberA
DOIs
Publication statusPublished - Dec 2015

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