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94th Annual Meeting of the International Association of Applied mathematics and Mechanics, GAMM 2024

作者： Dierkes, Eva Kappertz, Lars Solovievskyi, Viacheslav Hackenberg, Annika Büskens, Christof Steinbeis Innovation Center for Optimization and Control Grasberg Germany Center for Industrial Mathematics University of Bremen Bremen Germany

This work introduces an optimally controlled forecast-based energy management system (EMS) that integrates energy demand and production forecast with optimal control techniques to optimize the management of renewable energy sources. Although controlling the energy generation is not feasible for weather-dependent power plants, it is possible to predict the amount of energy produced. Self-sufficiency can be improved by regulating individual devices and storage units based on anticipated energy production and demand. For electricity tariffs much larger than feed-in tariffs, such demand-side management can notably improve the profitability of (private) renewable energy systems. The EMS is based on long-term predictions that enable high-level control decisions and specific controls for the next controlling steps on a low level. Weather forecasts and forecasts of the power demand and state behavior of each device are considered to evaluate both cost function and constraints for the next prediction horizon. The system’s performance is evaluated through a case study, demonstrating its ability to control the energy distribution successfully. The proposed EMS presents a promising approach to smart energy management that can contribute to a more sustainable future. © 2024 The Author(s). Proceedings in Applied mathematics & Mechanics published by Wiley-VCH GmbH.

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Let Hybrid A* Path Planner Obey Traffic Rules: A Deep Reinforcement Learning-Based Planning Framework

Let Hybrid A* Path Planner Obey Traffic Rules: A Deep Reinfo...

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International Conference on Autonomous Robots and Agents, ICARA

作者： Xibo Li Shruti Patel Christof Büskens Optimization and Optimal Control Center for Industrial Mathematics University of Bremen Bremen Germany Department for Autonomous Systems TOPAS Industriemathematik Innovation gGmbH Bremen Germany

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

Deep reinforcement learning (DRL) allows a system to interact with its environment and take actions by training an efficient policy that maximizes self-defined rewards. In autonomous driving, it can be used as a strategy for high-level decision making, whereas low-level algorithms such as the hybrid A* path planning have proven their ability to solve the local trajectory planning problem. In this work, we combine these two methods where the DRL makes high-level decisions such as lane change commands. After obtaining the lane change command, the hybrid A* planner is able to generate a collision-free trajectory to be executed by a model predictive controller (MPC). In addition, the DRL algorithm is able to keep the lane change command consistent within a chosen time-period. Traffic rules are implemented using linear temporal logic (LTL), which is then utilized as a reward function in DRL. Furthermore, we validate the proposed method on a real system to demonstrate its feasibility from simulation to implementation on real hardware.

关键词： Training Trajectory planning Predictive models Deep reinforcement learning Prediction algorithms Planning Trajectory Logic Autonomous vehicles Robots

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Indoor Localization for an Autonomous Model Car: A Marker-Based Multi-Sensor Fusion Framework 19

Indoor Localization for an Autonomous Model Car: A Marker-Ba...

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19th IEEE International Conference on Automation Science and Engineering, CASE 2023

作者： Li, Xibo Patel, Shruti Stronzek-Pfeifer, David Büskens, Christof University of Bremen Group Optimization and Optimal Control Center for Industrial Mathematics Bremen28359 Germany

ISBN: (纸本)9798350320695

Global navigation satellite systems readily provide accurate position information when localizing a robot outdoors. However, an analogous standard solution does not exist yet for mobile robots operating indoors. This paper presents an integrated framework for indoor localization and experimental validation of an autonomous driving system based on an advanced driver-assistance system (ADAS) model car. The global pose of the model car is obtained by fusing information from fiducial markers, inertial sensors and wheel odometry. In order to achieve robust localization, we investigate and compare two extensions to the Extended Kalman Filter;first with adaptive noise tuning and second with ?2-testing for measurement outlier detection. An efficient and low-cost ground truth measurement method using a single LiDAR sensor is also proposed to validate the results. The performance of the localization algorithms is tested on a complete autonomous driving system with trajectory planning and model predictive control. © 2023 IEEE.

关键词： Fault detection

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Benchmarking solution methods for parameter identification in dynamical systems

Benchmarking solution methods for parameter identification i...

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93rd Annual Meeting of the International Association of Applied mathematics and Mechanics, GAMM 2023

作者： Wiesner, Marek Büskens, Christof Optimization and Optimal Control Center for Industrial Mathematics University of Bremen Bremen Germany

Accurate models are crucial for simulating, optimizing, and controlling real-world processes. Parameter identification—the task of estimating the unknown parameters of a dynamical system based on measurements—is challenging and there exist various methods to approach it. Integration-based methods, such as shooting methods and full discretization, approximate the model output by numerically solving the dynamical system. Gradient matching methods, on the other hand, avoid solving the dynamical system and focus on minimizing the error between the measurement slope and the state derivatives instead. All approaches have advantages and disadvantages and a recipe for which method is best in a particular situation does not exist. In this paper, we present the results of a benchmark comparing single shooting, multiple shooting, full discretization, and gradient matching using a comprehensive database of test problems. We investigate if there is a notable difference in the performance of the various methods and if one approach is superior to the others. From the benchmark, we conclude that the integration-based methods outperform gradient matching. While full discretization exhibits robustness, single and multiple shooting provide higher precision. Additionally, we observe that finding an optimal configuration of the methods across a diverse set of parameter identification problems remains challenging and often requires fine-tuning. © 2023 The Authors. Proceedings in Applied mathematics and Mechanics published by Wiley-VCH GmbH.

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Benefits of optimal flight planning on noise and emissions abatement at the Frankfurt airport

Benefits of optimal flight planning on noise and emissions a...

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AIAA Guidance, Navigation, and control Conference 2012

作者： de Oliveira, Rafael Fernandes Büskens, Christof EADS Innovation Works München Germany University of Bremen Bremen Germany Optimization and Optimal Control department Center for Industrial Mathematics Germany

ISBN: (纸本)9781600869389

The aviation environmental impact, including emissions and noise, has increasing importance in the design and operation of commercial aircrafts. The evolution of aircraft design has the potential to significantly improve flight efficiency, but it represents only a long-term solution, whereas improving flight procedures can contribute to the reduction of environmental impact for current and future aircrafts in a shorter time scale. Trajectory optimization methods have been successfully used to minimize pollutant emissions during departure and arrivals, and noise over communities near the airports. In this context, a trajectory optimization framework has been developed, capable of finding flight paths that minimize emissions, flight costs and noise impact while taking into account operational restrictions. This can become a very powerful tool both for policy makers when studying departure and arrival routes, and for pilots, when the next generation of air traffic management systems, studied by SESAR and NextGen, will allow the aircraft to have greater freedom in defining its own flight path. This work introduces this framework and further focus on a numerical benchmark against recorded ADS-B flights departing from the Frankfurt airport. Based on estimated performance data, new optimized trajectories are calculated for each original flight. Analysis of the suggested departure procedures showed improvements in the order of 11% for sleep disturbance by only optimizing the vertical flight profile, and up to 21% in aircraft gaseous emissions. © 2012 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.

关键词： Flight paths

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Analyzing the Influence of Measurements in Dynamical Parameter Identification Using Parametric Sensitivities

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IFAC-PapersOnLine 2021年第14期54卷 7-12页

作者： M. Wiesner K. Schäfer W. Bergmann A. Berger P. Shulpyakov C. Dittert C. Büskens Optimization and Optimal Control Center for Industrial Mathematics University of Bremen 28359 Bremen Germany

In many modern applications, a central task is to model a dynamical behavior with ordinary differential equations. A common way to identify parameters within such a model is to fit its output against given measurements. Since it can be difficult to understand the connection between measurements and the parameter identification result, it is desirable to develop methods for analyzing this aspect. In this paper, we show how information from parametric sensitivity analysis can be used to gain a better insight into the impact of certain measurement regions on the identified model parameters. We parameterize the measurements by a B-spline regression and formulate the task of parameter identification using a collocation approach. In the resulting nonlinear optimization problem, we consider the B-spline coefficients as perturbation parameters. Next, we identify the model parameters and compute their parametric sensitivities with respect to these perturbations. In a final step, we evaluate a newly developed measure to characterize the desired influence. The corresponding optimization problems are solved with the nonlinear programming solver WORHP in combination with its integrated module WORHP Zen, which computes the required parametric sensitivities efficiently. We demonstrate the proposed approach by applying it to the example of parameter identification of a driving car.

关键词： Modeling Identification Identification for control Nonlinear system identification Input design Parametric sensitivity analysis Nonlinear optimization

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Parameter Identification for Finite Element Based Models in Dry Machining Applications

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Procedia CIRP 2015年 31卷 328-333页

作者： H. Wernsing C. Büskens Optimization and Optimal Control Center for Industrial Mathematics (ZeTeM) MZH Bibliothekstr. 1 28359 Bremen Germany

Dry machining is a challenging topic in industrial manufacturing: The absence of coolants results in ecological and economical benefits, but also in a significant increase of the occurring thermal stress. This leads to geometrical deviations of the machined workpiece impairing the functional performance of the final part. The increased thermal stress is especially important in high-precision manufacturing and requires careful process planning. To determine process parameters in order to minimize geometrical errors, optimal control is an appropriate tool. A precise partial differential equation model representing the material behavior is required. The partial differential equation contains unknown physical quantities, such as heat fluxes, which cannot be measured directly. One common method to derive these quantities is solving an inverse problem by parameter identification, i.e. determining the model parameters, such that the model matches the measurements best. We will present a simultaneous analysis and design approach which states a numerical finite element discretization of the model as constraints of the resulting nonlinear optimization problem (NLP). The proposed method takes advantage of both, direct consideration of state constraints, like temperature boundaries, and lower computational costs compared to the nested analysis and design approach. The additional implementation costs can be significantly reduced by using an external finite element library for assembling the optimization constraints and its derivatives. This approach for determining physical parameters of milling experiments is user-independent and works fully automated. It is the first step for an optimal control of dry machining processes. We will give a description of this method as well as numerical identification results of real heat flux densities by the sparse NLP solver WORHP.

关键词： optimization Identification Finite element method (FEM) Modeling Machining

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controlling an Autonomous Vehicle with Deep Reinforcement Learning*

arXiv

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arXiv 2019年

作者： Folkers, Andreas Rick, Matthias Büskens, Christof WG Optimization and Optimal Control Center for Industrial Mathematics University of Bremen Germany

— We present a control approach for autonomous vehicles based on deep reinforcement learning. A neural network agent is trained to map its estimated state to acceleration and steering commands given the objective of reaching a specific target state while considering detected obstacles. Learning is performed using state-of-the-art proximal policy optimization in combination with a simulated environment. Training from scratch takes five to nine hours. The resulting agent is evaluated within simulation and subsequently applied to control a full-size research vehicle. For this, the autonomous exploration of a parking lot is considered, including turning maneuvers and obstacle avoidance. Altogether, this work is among the first examples to successfully apply deep reinforcement learning to a real vehicle. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep learning

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Indoor Localization for an Autonomous Model Car: A Marker-Based Multi-Sensor Fusion Framework

Indoor Localization for an Autonomous Model Car: A Marker-Ba...

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Xibo Li Shruti Patel David Stronzek-Pfeifer Christof Büskens working group Optimization and Optimal Control Center for Industrial Mathematics University of Bremen Bremen Germany

Global navigation satellite systems readily provide accurate position information when localizing a robot outdoors. However, an analogous standard solution does not exist yet for mobile robots operating indoors. This paper presents an integrated framework for indoor localization and experimental validation of an autonomous driving system based on an advanced driver-assistance system (ADAS) model car. The global pose of the model car is obtained by fusing information from fiducial markers, inertial sensors and wheel odometry. In order to achieve robust localization, we investigate and compare two extensions to the Extended Kalman Filter; first with adaptive noise tuning and second with $\chi^{2}-\mathbf{testing}$ for measurement outlier detection. An efficient and low-cost ground truth measurement method using a single LiDAR sensor is also proposed to validate the results. The performance of the localization algorithms is tested on a complete autonomous driving system with trajectory planning and model predictive control.

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Indoor Localization for an Autonomous Model Car: A Marker-Based Multi-Sensor Fusion Framework

arXiv

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arXiv 2023年

作者： Li, Xibo Patel, Shruti Stronzek-Pfeifer, David Büskens, Christof The Working Group Optimization and Optimal Control Center for Industrial Mathematics University of Bremen Bremen28359 Germany

Global navigation satellite systems readily provide accurate position information when localizing a robot outdoors. However, an analogous standard solution does not exist yet for mobile robots operating indoors. This paper presents an integrated framework for indoor localization and experimental validation of an autonomous driving system based on an advanced driver-assistance system (ADAS) model car. The global pose of the model car is obtained by fusing information from fiducial markers, inertial sensors and wheel odometry. In order to achieve robust localization, we investigate and compare two extensions to the Extended Kalman Filter;first with adaptive noise tuning and second with χ2−testing for measurement outlier detection. An efficient and low-cost ground truth measurement method using a single LiDAR sensor is also proposed to validate the results. The performance of the localization algorithms is tested on a complete autonomous driving system with trajectory planning and model predictive control. Copyright © 2023, The Authors. All rights reserved.

关键词： Fault detection

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