The Complex Dynamical Systems group does research in the field of modeling, simulation, analysis, optimization and control of complex dynamical systems as well as in image processing, sensor fusion, and cognitive robotics. The primary goal of our research is the improvement of the system behavior in view of the dynamic properties, the accuracy, the robustness, the reliability, the flexibility, the productivity, the overall equipment efficiency (energy, resources) by reducing the product costs at the same time.
Furthermore, we develop methods and algorithms which give the systems a certain degree of cognitive skills. A special focus is placed on machine vision methods and sensor fusion to perceive structures and objects such that robots and machines can, to a certain extent, act independently and can learn from experience. This paves the way to highly flexible manufacturing systems with robots as intelligent tools and to new service robots in different areas of life. In this context, our core expertise is on safe navigation, 2D and 3D attention, object modelling, object class detection, affordance-based grasping, dynamic collision-free real-time path planning, system optimization, as well as adaptive and learning control.
The philosophy of our research activities is a systems approach. Thereby, the design of complex dynamical systems requires a systematic analysis and a deep understanding of all interrelationships between the construction, the sensor and actuator elements, and the implementation of information technology and automatic control utilizing the possibilities of modern machine learning methods and adaptive control strategies. Generally, we put large emphasis on a systematic physics-based modeling and we utilize this model information together with suitable data-based concepts for the system analysis and the system design. Moreover, meeting strict real-time requirements and questions on robustness and reliability also play an important role in our research activities.
The research field Nonlinear Systems is concerned with the development of methods, and the practical implementation of observer and control strategies for systems with significant nonlinear behavior. Similar to other fields of research, a focus lies on the practical implementation of the developed controller and observer strategies in a real (industrial) application. Read more →
The focus of this research field lies on the development and practical application of control methods that are directly designed on the basis of distributed-parameter models. Here, the description of the mathematical model and control strategies are formulated by means of partial differential equations. Read more →
We devise machine vision methods to perceive structures and objects such that robots act in and learn from every day situations. This paves the way to automated manufacturing and robots performing household tasks. Solutions develop the situated approach to integrate task, robot and perception knowledge. Core expertise is safe navigation, 2D and 3D attention, object modelling, object class detection, affordance-based grasping, and manipulation of objects in relation to object functions. Read more →
In the research field optimal systems, methods of mathematical optimization are theoretically developed and practically applied to solve various control problems. Typical subjects of research are state and parameter estimation, trajectory planning, and model predictive control. Read more →
Production systems is the field of research where control engineering methods are applied to and developed for production systems and the manufacturing industry. Objectives like highest product quality, efficient use of resources, cost saving and environmental friendliness are in the focus. Read more →