Medical control engineering


The development of medical autonomous systems in general and physiological closed-loop systems in particular requires an increased level of safety in a highly uncertain and volatile environment. To achieve optimal therapeutic outcomes, adaptation to the individual patient is required. Due to the varying clinical environment and the limited information available during therapy, adaptation is often not easy to achieve.

At Fraunhofer IMTE, algorithms are being researched that allow physiological parameters to be influenced while ensuring safe operation for patients and providing additional information to clinical staff. This is increasingly achieved through the use of predictive and model-based controller architectures, which can adapt to the clinical context on a patient-specific basis and integrate machine learning methods. The challenges of device integration and verification are addressed early in the design process and addressed through appropriate architecture selection and the development of test systems.

Research into these aspects offers the potential to increase patient safety, relieve the burden on clinical staff and more effectively utilise the increasing interconnectivity between devices to improve therapy outcomes.

Fields of research

Automated ventilation

Breathing is an often fundamental physiological process that is rarely consciously perceived, but whose failure represents a medical emergency. Individual respiratory support is a major challenge due to individual metabolic needs, anatomy and physiology, as well as specific breathing rhythms. Studies have shown that many patients are not ventilated optimally with regard to their individual needs. The consequences for the patient range from trauma to the patient due to loss of autonomy over their own breathing to severe lung injury and even death

Research at Fraunhofer IMTE aims to develop a ventilator that recognises the patient's breathing activity, identifies the parameters of the patient model and uses this information to individually and optimally adjust the ventilatory support to the patient's needs using advanced control approaches.


  • Conception, design and implementation of automation and control for safety-critical applications
  • Model-based and learning control
  • Development of digital models
  • Rapid control prototyping and system integration
  • Conception, design and construction of (medical) physiological simulators and test benches
  • Implementation of long-term test series
  • Research and determination of critical test scenarios
  • Research of relevant physiological parameters


  • Real-time development systems
  • Patient simulators (ASL5000, Testchest)