Optimal Supervisor Simplification in AMS based on Petri Nets and Genetic Algorithm
Chen Chen, Chan Gu, Hesuan Hu · 2021 60th IEEE Conference on Decision and Control (CDC) · 2021
Supervisor simplification is of significant importance in the supervisory control of automated manufacturing systems. In many situations, simplification results are not unique. However, there is little study on the optimal simplification. In the paradigm of discrete event systems, solving the optimal simplification is always faced with the combinational explosion problem. Based on the simplification method proposed by the same authors, our work further investigates the optimal simplified supervisor based on the genetic algorithm (GA). There are two contributions. First, we present a GA in which our simplification method is embedded to derive the basic optimal simplified supervisor when the parameters of the specifications are fixed. Second, a hierarchical GA is proposed to obtain the advanced optimal simplified supervisor when the parameters of the specifications are changeable. This is a multiple-objective optimization problem where both structure simplification and behavior permissiveness are considered. The examples show the effectiveness of our algorithms in solving the optimal supervisor simplification problem.