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

Biped locomotion; Model Predictive Control; Singular Values Decomposition; Mass-spring-damper system; Numerical instability; Operational range; Prediction and control; Predictive control; Robust model predictive control; Self adaptive control; Singular values decompositions; Software; Control and Systems Engineering; Mathematics (all); Computer Science Applications

Abstract :

[en] 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.

Disciplines :

Aerospace & aeronautics engineering

Author, co-author :

Castano, Juan A.; Istituto Italiano di Tecnologia, Genova, Italy

Hernandez Naranjo, Jairo Andres ; Université de Liège - ULiège > Aérospatiale et Mécanique (A&M)

Li, Zhibin; Istituto Italiano di Tecnologia, Genova, Italy

Tsagarakis, Nikos G.; Istituto Italiano di Tecnologia, Genova, Italy

Caldwell, Darwin G.; Istituto Italiano di Tecnologia, Genova, Italy

De Keyser, Robin; Department of Electrical Energy, Systems and Automation, Ghent University, Belgium

Language :

English

Title :

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

Publication date :

December 2015

Journal title :

Robotics and Autonomous Systems

ISSN :

0921-8890

Publisher :

Elsevier

Volume :

Pages :

283 - 295

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

https://api.elsevier.com/content/article/PII:S092188901500189X?httpAccept=text/plain

Funding text :

This work is supported by the institute for the Promotion and Innovation by Science and Technology in Flanders IWT SBO-110006.

Available on ORBi :

since 03 October 2025

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