A complete framework for controller verification in manufacturing

Abstract

Programmable Logic Controllers (PLCs) have been established as standard devices for automation and process control since the 1990s. Although a lot of research work has been done on the field of controller modeling and verification, it is still daily practice that control software is manually developed without applying formal validation methods. On the other hand, controller modeling is often seen detached from the plant or its model, i.e. as an open loop. The results of analysis of open-loop controller behavior give very little or almost no indications of the correct behavior of the closed-loop system. The contribution therefore proposes an approach to generate formal models out of PLC code. These controller models enable formal verification of the closed loop in combination with a specification. Due to the year of publication, all solution approaches are based on the syntax definition of the IEC 61131-3, which is not fulfilled by every industrial PLC vendor. Therefore, this contribution will show a way to use the defined xml formats of the Technical Committee 6 of the PLCopen as input for the formal model generation.

An Overview of Verification and Validation Challenges for Inspection Robots

“…• Traditionally, analytic stability/avoidance proofs for control algorithms [12,13]. • Formal verification techniques for controller [14,15] and state estimation systems [16,17] are available by generating formal models out of the underlying physical or software models. • Simulation-based testing and user validation are also frequently used in this aspect.…”

Neural Network Virtual Sensors for Fuel Injection Quantities with Provable Performance Specifications

“…Beyond adversarial examples, other relevant work includes those studying model verification in contexts beyond image classification, typically within the context of controller verification. To name a few, hybrid controllers for automated highway systems were verified to be safe by design with game theoretic techniques [63], and frameworks for verifying controller software for manufacturing plants were developed for programmable logic controllers [64]. While some work has looked at verifying properties of neural networks for safety critical applications, they are mostly limited to measuring the confidence and monitoring the performance of an existing neural network without offering any formal guarantees [65], [66].…”

Modeling and Verification of Reconfigurable and Energy-Efficient Manufacturing Systems

“…Petri nets [20,21] have found an extensive application to discrete event systems [22,23], including automated flexible manufacturing systems [24][25][26][27][28] and reconfigurable systems [29]. Reconfigurable timed net condition/event systems (R-TNCESs) [30,31] are reconfigurable extensions of timed net condition/event systems (TNCESs) [32,33]. TNCESs [34,35] have a visual graph expression, a clear modular structure, and an exact mathematical definition inherited from Petri nets [36][37][38][39][40].…”