A First Step Towards Adaptive Control for Linear Systemsin Max Algebra
Discrete Event Dynamic Systems
Survey Constrained model predictive control: Stability and optimality
Automatica (Journal of IFAC)
Brief Model predictive control for max-plus-linear discrete event systems
Automatica (Journal of IFAC)
Brief Model reference control for timed event graphs in dioids
Automatica (Journal of IFAC)
Constrained infinite-horizon model predictive control for fuzzy-discrete-time systems
IEEE Transactions on Fuzzy Systems
Hi-index | 22.17 |
Max-plus-linear (MPL) systems are a class of event-driven nonlinear dynamic systems that can be described by models that are ''linear'' in the max-plus algebra. In this paper we derive a solution to a finite-horizon model predictive control (MPC) problem for MPL systems where the cost is designed to provide a trade-off between minimizing the due date error and a just-in-time production. In general, MPC can deal with complex input and states constraints. However, in this paper we assume that these are not present and it is only required that the input should be a nondecreasing sequence, i.e. we consider the ''unconstrained'' case. Despite the fact that the controlled system is nonlinear, by employing recent results in max-plus theory we are able to provide sufficient conditions such that the MPC controller is determined analytically and moreover the stability in terms of Lyapunov and in terms of boundedness of the closed-loop system is guaranteed a priori.