Institut für Systemdynamik - ISD
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Institute
Numerical Diffusion and its Impact on System-Identification for an Industrial Heating Process
(2025)
This paper deals with system-identification for a distributed parameter heating process where a solid substrate is moving through a spatially extended heating zone and heated up by applying hot air to its surface. The temperature distribution inside the substrate is modeled in a spatial plane, where heat conduction is considered in the direction, perpendicular to the direction of movement. In contrast to previous work, where scalar model parameters (e.g.The thermal parameters of the substrate) have been identified, here, the quantities for the heat transfer (heat transfer coefficient and air temperature) are identified as functions yielding a significantly improved fit to the measurement data. This improved system-identification is performed for two early-lumping modeling approaches, which differ in the way the advection term in the governing Partial Differential Equation is discretized: one uses Eulerian coordinates, where the computational grid is stationary, whereas the second employs Lagrangian coordinates where the grid is moving with the substrate. The differences of the two approaches are discussed with the main focus on numerical diffusion. Especially its impact on the system-identification is investigated: Although the fit to the measurement is comparably good in both cases, very different solutions are obtained for the identified functions which, we argue, is due to the optimizer counteracting the smoothing effect of numerical diffusion.
Autonomous surface vessels are a promising building block of the future’s transport sector and are investigated by research groups worldwide. This paper presents a comprehensive and systematic overview of the autonomous research vessel Solgenia including the latest investigations and recently presented methods that contributed to the fields of autonomous systems, applied numerical optimization, nonlinear model predictive control, multi-extended-object-tracking, computer vision, and collision avoidance. These are considered to be the main components of autonomous water taxi applications. Autonomous water taxis have the potential to transform the traffic in cities close to the water into a more efficient, sustainable, and flexible future state. Regarding this transformation, the test platform Solgenia offers an opportunity to gain new insights by investigating novel methods in real-world experiments. An established test platform will strongly reduce the effort required for real-world experiments in the future.
This paper presents an approach to control the temperature of an industrial heating process where a workpiece is transported through a heating unit in order to reach a certain temperature. To control this process is challenging due to the variable and possibly abruptly changing feed rate while requiring the final workpiece temperature to be maintained at a constant value. The speed itself cannot be influenced by the control system. However, its future trajectory is known to a certain extent at runtime. A model predictive controller is presented, which is characterized by the fact that the prediction horizon is dynamically adjusted to the transportation speed, that the traveled distance of the workpiece within the horizon is constant. Moreover, the step size of the integrator is also adapted, yielding a constant number of integration steps despite large variations in velocity. Because of the special structure of the used model, the system state cannot be reused from one time step to the next and needs to be reconstructed in a receeding horizon fashion, even if the model was perfect. This is also a distinctive feature of the presented control scheme. Simulation and measurement results are provided, showing that the presented controller achieves promising results and outperforms the existing lookup-table-based controller currently in use.
Efficient and safe autonomous control of surface
vessels is seminal for the future of maritime transport systems.
In this paper, we use an iterative learning–based nonlinear
model predictive control scheme leveraging past experiences of
the motion of vessels in a current field to reach optimal behavior.
We define an optimal control problem including a detailed vessel
model but only a roughly estimated current model. This current
model is improved from trial to trial. The learned controller is
compared to a linear track controller, a zero–offset nonlinear
model predictive controller without current information, and a
nonlinear model predictive controller including a perfect model
of the current field. The results of this comparison show that by
including experiences from previous trials, the controller can
improve its performance significantly.We believe that numerical
optimal control has the potential to disrupt the future control
design of maritime systems.
Interacting multiple model filters are most commonly used in the context of maneuvering targets, as they can represent the different dynamics of a real system by combining the estimates of multiple models. However, the interacting multiple model approach generally requires more computational effort than a single Kalman filter. In this work, down-sampling is used to reduce the computational effort. We propose an adaptive scheme to maintain the accuracy of the estimator to a defined level. To this end, the trace of the innovation covariance matrix is evaluated, and if it lies above a certain threshold, out-of-sequence measurements are iteratively used to improve the estimate until the uncertainty threshold is met. The approach is evaluated by Monte Carlo analysis. The results show that with this approach, the number of measurements to be processed, and thus the computational effort can be dynamically reduced, while the accuracy remains at a desired level.
In extended object tracking, basic parametric shapes such as ellipses and rectangles or non-parametric shape representations such as Fourier series or Gaussian processes can be utilized as shape priors. However, flexible non-parametric shape representations can be disproportionately detailed and computationally intensive for many applications. Therefore, we propose to adopt deformable superellipses for a low-dimensional and flexible representation of basic parametric shapes in this paper. We present a measurement model in 2D space that can cope with boundary and interior measurements simultaneously by recursively estimating an artificial noise variance for interior measurements. We investigate and compare the model in a simulated and real-world maritime scenario with the result that the combination of deformable superellipses and artificial measurement noise estimation performs better than state-of-the-art methods.
In extended object tracking, random matrices are commonly used to filter the mean and covariance matrix from measurement data. However, the relation from mean and covariance matrix to the extension parameters can become challenging when a lidar sensor is used. To address this, we propose virtual measurement models to estimate those parameters iteratively by adapting them, until the statistical moments of the measurements they would cause, match the random matrix result. While previous work has focused on 2D shapes, this paper extends the methodology to encompass 3D shapes such as cones, ellipsoids and rectangular cuboids. Additionally, we introduce a classification method based on Chamfer distances for identifying the best-fitting shape when the object’s shape is unknown. Our approach is evaluated through simulation studies and with real lidar data from maritime scenarios. The results indicate that a cone is the best representation for sailing boats, while ellipsoids are optimal for motorboats.
Flash memory is essential in modern electronics due to its fast access, high storage density, and cost-effectiveness. As the demand for expanded local storage capacity continues, its appliance is increasing.
This work primarily focuses on enhancing the reliability of flash memories. It begins with a comprehensive characterization of the flash channel, identifying and analyzing various sources of errors. The study delves into different bit-labeling schemes and investigates the achievable capacities associated with them. Additionally, the importance of read reference voltages is explained, particularly in adapting them to the life-cycle condition. The thesis also introduces calibration and adaptation algorithms for this purpose.
The challenges related to error correction codes are addressed extensively, focusing on algorithms designed to reduce decoding complexity. The research delves into low-complexity decoding techniques, particularly for scenarios involving small code lengths. Another area of investigation is concatenated codes based on small cyclic codes.
Furthermore, the thesis explores the advantages of joint processing of NAND flash pages, highlighting improvements in hard-decision decoding to minimize the need for additional read-out operations. This joint processing approach is thoroughly compared with conventional processing methods to assess its effectiveness and potential benefits.
Overall, this thesis contributes to enhancing the reliability of flash memories while proposing optimizations for their decoding processes.
The problem of controlling autonomous surface vessels in an energy-optimal way is important for the electrification of maritime systems and is currently being investigated by many researchers. In this paper, we use numerical optimal control to plan an energy-optimal docking trajectory in river currents and show that it can save energy compared to other widespread planning approaches. An optimal control problem including a detailed vessel model is defined, transcribed into a nonlinear optimization problem via direct multiple shooting, and solved using a homotopy procedure. The optimal solution is compared to a geometrical path planning approach with path-velocity decomposition. The results of this comparison show that prescribing a path with fixed vessel orientation leads to very suboptimal results. Further, we demonstrate how shrinking horizon MPC can control the vessel in an energy-optimal way even under severe disturbances, by replanning the energy-optimal trajectories in real-time. We believe that energy-optimal MPC could become a key technology for the electrification of maritime systems.
