Error Propagation Analysis for Hybrid Block Diagram and Finite State Machine Models

• Applicant: Professor Dr. Klaus Janschek
• Subject Area: Automation, Mechatronics, Control Systems, Intelligent Technical Systems, Robotics
• Term: from 2016 to 2020
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 310613481

Model-based control software development is widely used in a variety of safety critical domains including automotive, aerospace, and industrial automation. Control algorithms are typically developed using the combination of two classical types of models: time discrete block diagrams (BD) and discrete event finite state machines (FSM). The model-based approach ensures high consistency between base-line models and production code, which allows avoiding many faults that could be introduced in case of manual software development.However, even certified and well-tested model-driven control software is vulnerable to hardware faults, e.g. single event upsets. Such faults result in silent data errors that might propagate through the system to critical outputs. The likelihood of the fault activation and error propagation depends on a large number of factors. Analysis of this complex process is extremely helpful in a wide range of analytical tasks associated with dependable systems development. Recently we have introduced a new probabilistic approach to error propagation analysis using a dual-graph error propagation model. The central idea is a synchronous examination of a control flow, a data flows and reliability properties of system components. A discrete time Markov chain (DTMC) model was applied in order to obtain probabilities of erroneous and error-free system execution scenarios. This approach has been tested and shown promising results on a mechatronic case study that was developed using a base-line UML model.The proposed project is devoted to a model-based probabilistic error propagation analysis for control algorithms built from hybrid time-discrete BD and discrete event FSM models. On the basis of our baseline dual-graph error propagation model and the Markov-based approach we will develop extensions for abstract modeling of specific BD/FSM properties (e.g. hierarchical nesting, multi-rate, internal memory) and methods for automated mapping of hybrid BD/FSM models in dual-graph error propagation models and computationally efficient DTMC models. The newly developed methods will be prototyped in a tool chain enabling validation of the new approach on the basis of a representative case study.

• DFG Programme: Research Grants