Systems and Control Engineering – IINF-04/A
Systems and Control Engineering, owing to its methodological approach and interdisciplinarity, plays a significant role in scientific and technological innovation. The paradigms outlined by the programmatic guidelines of the Horizon Europe Community Program and the National Industry 4.0 Plan necessitate Systems and Control Engineering technologies to ensure safety and well-being in quality of life, alongside efficiency and quality in sustainable industrial production with reduced environmental impact.
Main Research Areas
- Robotics and Drones. Planning and control of underwater vehicles, drones, industrial manipulators and service robots, collaborative robots, soft robots with electrically and magnetically active materials, mobile robots, and anthropomorphic robots. Applications include logistics, agriculture, production systems, and healthcare.
- Networked Systems Control. Adaptive video streaming, congestion control for real-time video streams, control and orchestration of cloud and CDN resources, terrestrial and aerial mobile robotics, and edge artificial intelligence. Methodologies include non-linear control techniques, also for pure delay systems, optimal control, model predictive control, robust control, and reinforcement learning.
- Modeling and control of manufacturing and process systems, agent and sensor networks, fault detection and recovery, logistics, production and distribution systems, scheduling and planning, workflow management, control models and strategies for cybersecurity, re-engineering of production processes with collaborative robots, digital twin, and virtual and augmented reality technologies.
- Management and control of complex systems: intelligent transportation systems, road and rail traffic, modal, co-modal, inter-modal, and multi-modal logistics systems, and the transport of dangerous goods; electric mobility management, decision support systems for the planning and management of smart grids, smart cities, and smart buildings. Utilized methodologies include: model predictive control, artificial intelligence and optimization, and distributed and decentralized control and optimization algorithms.
- Fractional-order systems and controllers: identification, estimation, and modeling; analog and digital approximations; control of electric drives, robots, marine engines, internal combustion engines, and compressed natural gas engines; oscillation prevention in control systems with non-linear elements; and fault-tolerant control of marine engines.
- Diagnosis, identification, classification, prevention, and prediction of faults and anomalies. This involves the application of machine learning and deep learning algorithms for engines and propulsion systems in aeronautical and marine applications, alongside the simulation of real-world scenarios and laboratory testing. Furthermore, it includes the development of monitoring, prediction, and control software.
Personnel
