Urban water drainage systems represent complex networks with nonlinear dynamics and different types of interactions. This yields an involved modeling problem for which different off-line simulation approaches are available. Nevertheless, these approaches cannot be used for real-time simulations, i.e., running in parallel to weather now- and forecasts and enabling the monitoring and automatic control of urban water drainage systems. Alternative approaches, used commonly for automation purposes, involve parameterized linear delay systems, which can be used in real-time but lack the necessary level of detail, which, in particular, is required for adequate flood risk prognostics. Given this setup, in the present paper, an approach for the effective modeling of detailed water drainage systems for real-time applications implemented with the open-source Storm Water Management Model (SWMM) software is addressed and exemplified for a part of the water drainage system of the city of Flensburg in northern Germany. Additionally, a freely available early-warning system prototype is introduced and used to combine weather forcast information on a 2-h prediction horizon with the developed model and available measurements. This prototype is subsequently used for data assimilation using the ensemble Kalman filter (EnKF) for the considered area in Flensburg.

Flexibility combined with the ability to consider external constraints comprises the main advantages of nonlinear model predictive control (NMPC). Applied as a motion controller, NMPC enables applications in varying and disturbed environments, but requires time-consuming computations. Hence, given the full nonlinear multi-DOF robot model, a delay-free execution providing short control horizons at appropriate prediction horizons for accurate motions is not applicable in common use. This contribution introduces an approach that analyzes and decomposes the differential kinematics similar to the inverse kinematics method to assign Cartesian boundary conditions to specific systems of equations during the model building, reducing the online computational costs. The resulting fully constrained NMPC realizes the translational obstacle avoidance during trajectory tracking using a reduced model considering both joint and Cartesian constraints coupled with a Jacobian transposed controller performing the end-effector’s orientation correction. Apart from a safe distance from the obstacles, the presented approach does not lead to any limitations of the reachable workspace, and all degrees of freedom (DOFs) of the robot are used. The simulative evaluation in Gazebo using the Stäubli TX2-90 commanded of ROS on a standard computer emphasizes the significantly lower online computational costs, accuracy analysis, and extended adaptability in obstacle avoidance, providing additional flexibility. An interpretation of the new concept is discussed for further use and extensions.

In multistage press hardening processes, where a sheet material undergoes rapid austenitization, tempering, stretch-forming (SF), and die bending (DB), the resulting product properties are influenced by the thermo-mechanical history. This work aims at controlling the product properties of the formed blanks by making use of the model-based estimation and control of the spatial-temporal temperature distribution in the sheet. A data-driven dynamical model is constructed using dynamic mode decomposition (DMD) based on finite element (FE) simulation data. This model is further extended by means of parametric DMD to accommodate changes in process parameters like stroke rate, blank holder force, and austenitization temperature. The approach is validated and the model accuracy is improved through experimental analysis. The dynamics of the available temperature sensors are identified, whereupon a Kalman filter is developed based on the parametric DMD model to estimate the spatial-temporal temperature distribution. A time-varying, stage-dependent output matrix is employed to account for different numbers and locations of thermocouples in the three stages. Additionally, an optimal control strategy is implemented to achieve desired temperature trajectories, allowing targeted manipulation of the blank’s geometry and properties. Experimental validation of this system design and control strategy is carried out under real-time constraints.

Due to climate change, heavy rainfall events are occurring more frequently and at a higher rate. At the same time, the sewer system is often not designed to handle such a large amount of precipitation, which often leads to flooding of certain critical areas. However, modern sensor technology for monitoring the filling and flow rates in the sewer system, as well as for monitoring and forecasting rainfall probabilities, including quantity information, form a good basis for developing modern multifunctional early warning systems, which can serve as an aid for decisions on action. Urban water drainage systems represent complex networks with nonlinear dynamics and different types of interactions. This yields an involved modelling problem for which different off-line simulation approaches are available. Nevertheless, these approaches cannot be used for real-time simulations, i.e., running in parallel to weather now- and forecasts and enabling the monitoring and automatic control of urban water drainage systems. Alternative approaches, used commonly for automation purposes, involve parameterized linear delay systems, which can be used in real-time but lack the necessary level of detail, which is required for adequate flood risk prognostics. Given this setup, an approach for the effective modelling of detailed water drainage systems for real-time applications implemented with the open-source Storm Water Management Model (SWMM) software is addressed and exemplified for a part of the water drainage system of the city of Flensburg in northern Germany. Additionally, a freely available early-warning system prototype is introduced and used to combine weather forecast information on a 2-h prediction horizon with the developed model and available measurements. This prototype is subsequently used for data assimilation using the ensemble Kalman filter (EnKF) for the considered area in Flensburg. The project presented here is part of the NEPTUN project. NEPTUN is financed by Interreg Deutschland-Danmark with means from the European Regional Development Fund.