Structural Controllability of Switching Max-Plus Linear Systems

Structural Controllability of Switching Linear Systems

This paper investigates the structural controllability problem for controlled switching linear systems. Causal manipulations on the bond graph models are carried out in order to determine graphically the controllable state subspaces. Graphical conditions for structural controllability are derived by using these controllable state subspaces.

Model predictive control for stochastic switching max-plus-linear systems

A stabilizing model predictive controller for uncertain max-plus-linear systems and uncertain switching max-plus-linear systems ⋆

We first discuss conditions for stability for uncertain max-plus-linear systems and for uncertain switching max-plus-linear systems, where in the uncertainty description the system matrices live in a union of polyhedra. Based on the newly derived stability conditions, a stabilizing model predictive controller is derived for both uncertain max-plus-linear systems and uncertain switching max-plus...

Modeling and control of switching max-plus-linear systems with random and deterministic switching

Switching max-plus-linear (SMPL) systems are discrete-event systems that can switch between different modes of operation. In each mode the system is described by a max-plus-linear state equation and a max-plus-linear output equation, with different system matrices for each mode. The switching may depend on the inputs and the states, or it may be a stochastic process. In this paper two equivalen...

Model predictive control for switching max-plus-linear systems with random and deterministic switching

Switching max-plus-linear (SMPL) systems are discrete event systems that can switch between different modes of operation. In each mode the system is described by a max-pluslinear state equation and a max-plus-linear output equation, with different system matrices for each mode. The switching may depend on input and state, or it may be a stochastic process. We derive a stabilizing model predicti...