LAUSR

ABSTRACT This paper addresses the problem of control design for timed continuous Petri net (TCPN) systems. The problem is studied using model predictive control that determines the control vector, under some constraints, to drive a TCPN from an initial marking to a steady state by minimising certain cost functions. In order to reduce the computational complexity, a new cost function is first proposed and additional constrains that enforce constant control sequences are considered. An adaptive prediction horizon is also proposed. Then, the main contribution is to reduce the actuator solicitation by combining a new weighted term, which takes into account the flow variations, and an online adaptation of the weighting factor with a terminal constraint that ensures the asymptotic stability of the closed-loop system.