Self Optimisation and Automatic Code Generation by Evolutionary Algorithms in PLC based Controlling Processes
This addresses the need for efficient data processing and code generation in industrial automation, though it appears incremental as it applies evolutionary algorithms to a specific domain.
The paper tackles the problem of optimizing system logic and generating code for industrial automation processes by proposing a novel evolutionary algorithm approach, which is evaluated on an industrial liquid station process with multi-objective optimization.
The digital transformation of automation places new demands on data acquisition and processing in industrial processes. Logical relationships between acquired data and cyclic process sequences must be correctly interpreted and evaluated. To solve this problem, a novel approach based on evolutionary algorithms is proposed to self optimise the system logic of complex processes. Based on the genetic results, a programme code for the system implementation is derived by decoding the solution. This is achieved by a flexible system structure with an upstream, intermediate and downstream unit. In the intermediate unit, a directed learning process interacts with a system replica and an evaluation function in a closed loop. The code generation strategy is represented by redundancy and priority, sequencing and performance derivation. The presented approach is evaluated on an industrial liquid station process subject to a multi-objective optimisation problem.