A min-max model predictive control for a class of hybrid dynamical systems

• Published in: Proceedings 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation. Computational Intelligence in Robotics and Automation for the New Millennium (Cat. No.03EX694) Vol. 2; pp. 694 - 699 vol.2
• Main Authors: Mukai, M., Kojima, A., Azuma, T., Fujita, M.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2003
• Subjects: Control system synthesis; Control systems; Convergence; Piecewise linear techniques; Prediction algorithms; Predictive control; Predictive models; Remotely operated vehicles; State feedback; Systems engineering and theory
• DOI: 10.1109/CIRA.2003.1222265
• ISBN: 9780780378667; 0780378660

This paper presents a min-max model predictive control algorithm for a class of hybrid systems by exploiting the equivalence between piecewise linear systems and mixed logical dynamical systems. The control algorithm consists of two control modes which are a state feedback mode and a min-max model predictive control mode. In the min-max model predictive control mode, the constrained positively invariant sets are used as the end set constraint, and an approach based on a min-max model predictive control formulation is employed. This control algorithm guarantees that the state converges to a union of constrained positively invariant sets with no constraint violation.