Integrated Microgrid Control Platform (IMCP)
This project aims at developing and demonstrating a highly reusable ‘software platform’ that can be easily adapted to and used in various microgrid configurations.
State-of-the-art systems engineering is model-based: models are used in all phases of systems’ lifecycle. An exciting new research direction focuses on symbiotic design where human-driven model-based design processes are complemented by AI/ML assisted components. Our research covers a broad range of engineering activities where models and data are used both in design and in operations.
- Cyber-physical systems and human cyber-physical systems where humans and computing are tightly integrated into a physical environment
- Design-space exploration, both parametric and combinatorial, with optimization and trade-offs
- Fault diagnostics and prognostics, system health management
- Foundations for Model-integrated Computing / Model-driven design: meta-programmable modeling tools, formal frameworks, domain-specific modeling languages, model transformations, and run-time environments for model-driven system development
- Model integration platforms for physical and biological systems
- Resilient systems that can recover from faults of cyber-effects and continue operating
- Assurance of Cyber-physical systems with learning-enabled components
- Software engineering environments for agile and adaptive system development
- System verification and validation, including both formal and coverage-driven methods
- Large-scale heterogeneous simulation environments for studying complex, emerging behaviors in system-of-systems
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Model-based Intent-Driven Adaptive Software (MIDAS)
The project’s goal is to develop a new approach to evolutionary software development and deployment that extends the results of model-based software engineering and provides an integrated, end-to-end framework for building software that is focused on growth and adaptation.
Assurance-based Learning-enabled Cyber-Physical Systems (ALC)
Autonomous vehicles (cars, drones, underwater vehicles, etc.) have started using software components that are built using machine learning techniques. This is due to the fact that these vehicles must operate in highly uncertain environments and that we cannot design a correct algorithm for all possible situations.