Scheduling Using Rhonns: A Test Case

Abstract

In Chapter 5, the non-acyclic FMS scheduling problem was considered to be a control regulation problem, where system states (buffer levels), have to reach some prespecified production requirements, by means of control input commands. Based on a recurrent high-order neural-network model of the buffer states, an adaptive continuous-time neural-network controller was developed. Stable control and update laws guaranteeing system stability, boundedness of all signals in the closed-loop system and a uniform ultimate boundedness property of the control error were derived. Dispatching commands were issued by means of a discretization process of the continous control input. Furthermore, modeling errors and discretization effects were taken into account, thus rendering the controller robust and capable of driving system production to the required demand.