A Framework for Teaching Safety Critical Control and Artificially Intelligent Systems to Undergrads
2022-26-1223
05/26/2022
- Event
- Content
- There is a need to teach systems to the undergrad and graduate levels in colleges. Efforts are being made by industry, academia, and professional societies to join hands to bridge the gap. The students require examples to get their “hands dirty” on an actual system. Many of the aerospace examples are not available in the open domain as they are considered proprietary. The world is going toward Artificial Intelligence. Though AI has been used in aerospace industry for optimization for quite some time, certification of AI systems in the safety critical control domain are still not very popular. Experiments on such system are required to clearly understand the testing and safety aspects of deploying such systems. This paper looks at an environmental control system which has enough complexity to merit an industry level attention but is again simple enough to be understood by the students. This is a physics model. Simulation runs from this physics model is used to train a Neural Network model as an observer and as a controller. The NN model can predict divergence from a nominal behavior and can raise flags for warning and a subsequent control change. We bring together all these as a framework for study of NN in safety critical domain. We bring out the safety bounds by the traditional approach of putting in zones based on the Principal Component Analysis. The added advantage is that these models and framework will be made available in the open domain for the community to experiment and learn. We provide a suggested syllabus for a system engineering curriculum that can be easily framed around this model.
- Citation
- Jeppu, Y., and Raman, R., "A Framework for Teaching Safety Critical Control and Artificially Intelligent Systems to Undergrads," SAE Technical Paper 2022-26-1223, 2022, .