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22nd World Congress of the International Federation of Automatic control (IFAC)

作者： Baumgart, Urs Moreno-Mora, Francisco Beckenbach, Lukas Burger, Michael Streif, Stefan Fraunhofer Inst Ind Math ITWM D-67663 Kaiserslautern Germany Tech Univ Chemnitz Automat Control & Syst Dynam Lab D-09126 Chemnitz Germany

ISBN: (纸本)9781713872344

Automated vehicles have been proposed as a way to influence traffic flow to avoid congestion and maintain a smooth traffic flow. Experiments have shown that congestion formation can be reproduced in an artificial ring road scenario. We design a model predictive controller for the ring road system assuming heterogeneous drivers and an automated vehicle for which congestion resolution and convergence to a reference speed can be shown. The driver's model captures human driving responses. A stabilizing predictive controller framework is employed under the use of a local controllability assumption connected to a local linear quadratic regulator. A case study shows the efficacy of the proposed controller and provides numerical values for the required prediction horizon, highlighting congestion resolution as well as theoretical conservativeness. Copyright (c) 2023 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： nonlinear and optimal automotive control Multi-vehicle systems Predictive control Stability Autonomous vehicles

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nonlinear optimal control of Traffic Flow with Stability Guarantees

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IFAC-PapersOnLine 2023年第2期56卷 4965-4970页

作者： Urs Baumgart Francisco Moreno-Mora Lukas Beckenbach Michael Burger Stefan Streif Fraunhofer Institute for Industrial Mathematics ITWM Kaiserslautern 67663 Germany Technische Universität Chemnitz Chemnitz 09126 Germany Automatic Control and System Dynamics Lab

关键词： nonlinear and optimal automotive control Multi-vehicle systems Predictive control Stability Autonomous vehicles

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A Koopman-based approach for torque vectoring in electric vehicles 4

A Koopman-based approach for torque vectoring in electric ve...

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4th Modeling, Estimation, and control Conference (MECC)

作者： Sassella, Andrea Lenzo, Basilio Univ Padua Dept Ind Engn Via Venezia 1 Padua Italy

Torque Vectoring plays a pivotal role in enhancing vehicle dynamics and performance. In electric vehicles with multiple motors, the torque at each wheel may be controlled independently, offering significant opportunity to enhance safety and stability. This study explores the application of Koopman-based Model Predictive control (MPC) in torque vectoring systems. By considering an electric vehicle equipped with four in-wheel motors, a linear Koopman model is identified from data. The model is then used to formulate a linear MPC. The performance of the scheme is assessed through numerical simulations. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Koopman control Vehicle control Torque Vectoring nonlinear predictive control Model predictive and optimization-based control nonlinear and optimal automotive control

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R²NMPC: A Real-Time Reduced Robustified nonlinear Model Predictive control with Ellipsoidal Uncertainty Sets for Autonomous Vehicle Motion control 8

R<SUP>2</SUP>NMPC: A Real-Time Reduced Robustified Nonlinear...

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8th IFAC Conference on nonlinear Model Predictive control (NMPC)

作者： Zarrouki, Baha Nunes, Joao Betz, Johannes Tech Univ Munich Chair Automot Technol Munich Germany Tech Univ Munich TUM Sch Engn & Design Munich Inst Robot & Machine Intelligence MIRMI Autonomous Vehicle Syst Munich Germany

In this paper, we present a novel Reduced Robustified NMPC ((RNMPC)-N-2) algorithm that has the same complexity as an equivalent nominal NMPC while enhancing it with robustified constraints based on the dynamics of ellipsoidal uncertainty sets. This promises both a closed-loop- and constraint satisfaction performance equivalent to common Robustified NMPC approaches while drastically reducing the computational complexity. The main idea lies in approximating the ellipsoidal uncertainty sets propagation over the prediction horizon with the system dynamics' sensitivities inferred from the last optimal control problem (OCP) solution and similarly for the gradients to robustify the constraints. Thus, we do not require the decision variables related to the uncertainty propagation within the OCP, rendering it computationally tractable. Next, we illustrate the real-time control capabilities of our algorithm in handling a complex, high-dimensional, and highly nonlinear system, namely the trajectory following of an autonomous passenger vehicle modeled with a dynamic nonlinear single-track model. Our experimental findings, alongside a comparative assessment against other Robust NMPC approaches, affirm the robustness of our method in effectively tracking an optimal racetrack trajectory while satisfying the nonlinear constraints. This performans-1, and sce is achieved while fully utilizing the vehicle's interface limits, even at high speeds of up to 37.5m uccessfully managing state estimation disturbances. Our approach maintains a mean solving frequency of 140Hz. The code used in this research is publicly accessible as open-source software: https://***/bzarr/TUM-control Copyright (C) 2024 The Authors.

关键词： Robust control nonlinear predictive control Real-time optimal control nonlinear and optimal automotive control Adaptive and robust control of automotive systems

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Turbocharger control for Emission Reduction Based on Deep Reinforcement Learning 22

Turbocharger Control for Emission Reduction Based on Deep Re...

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22nd World Congress of the International Federation of Automatic control (IFAC)

作者： Picerno, Mario Koch, Lucas Badalian, Kevin Lee, Sung-Yong Andert, Jakob Teaching & Res Area Mechatron Mobile Prop D-52074 Aachen Germany

ISBN: (纸本)9781713872344

The development of embedded systems is a time-consuming process that relies on the calibration of the control systems by experienced engineers and the availability of prototype vehicles. Modern embedded systems must operate with non-linear functions in a wide range of applications. As a result, even experts are unable to fully comprehend all system interconnections, and optimal performance is rarely achieved. Machine Learning (ML)-based techniques can redefine the conventional approach to decision-making problems in this field. Because Reinforcement Learning (RL) algorithms are self-adaptive, near-optimal solutions can be developed with minimal human intervention, providing significant potential for cost and time savings. In this paper, it is demonstrated how RL can be used to design a control function that competes with and outperforms a state-of-the-art controller, while ensuring the stability of the system. In the Model-in-the-Loop (MiL) framework, a method of accelerated training for the control of the turbocharger is presented that considers both performance and emissions criteria. Data efficiency and training robustness were tested for the Proximal Policy Optimization (PPO) algorithm. Selected policies have been evaluated under transient conditions and are shown to enhance the overall boost pressure accuracy by up to 23%, while reducing cumulative NOx and soot emissions by 4% and 10%, respectively, compared to the reference controller. This work demonstrates another step towards the application of the PPO algorithm in the development of embedded systems for complex control problems. Due to its proven robustness and stability, the method is suitable for transfer to real hardware applications. Copyright (c) 2023 The Authors.

关键词： Engine modelling and control Reinforcement Learning nonlinear and optimal automotive control Emission control Neural networks

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Model Predictive control for Lane Merging Automation with Recursive Feasibility Guarantees 22

Model Predictive Control for Lane Merging Automation with Re...

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22nd World Congress of the International Federation of Automatic control (IFAC)

作者： Geurts, M. E. Katriniok, A. Silvas, E. Heemels, W. P. M. H. Eindhoven Univ Technol Control Syst Technol Sect Dept Mech Engn Eindhoven Netherlands Ford Res & Innovat Ctr RIC Aachen Germany TNO Dept Integrated Vehicle Safety Helmond Netherlands

ISBN: (纸本)9781713872344

In order to make the complex driving task of merging safer, in this paper we consider the automated merging of an autonomous vehicle into a mixed-traffic flow scenario (i.e., traffic including autonomous and manually driven vehicles). In particular, we propose a novel MPC-based algorithm to perform a merging procedure from a double lane into a single lane and continue with (adaptive) cruise control ((A)CC) functionality after the merge. The proposed MPC balances fast progress along the path with comfort, while obeying safety and maximum allowed velocity bounds. Recursive feasibility, leading to safety and proper behavior, is guaranteed by the design of a proper terminal set, extending existing ones in the literature. The on-line MPC problem is translated into a mixed integer quadratic program (MIQP) that can be solved for global optimality. Through numerical simulations we demonstrate the behavior and effectiveness of the proposed MPC merging scheme. Copyright (c) 2023 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Autonomous vehicles Predictive control Trajectory Tracking and Path Following nonlinear and optimal automotive control Safety

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Integrating Recurrent Neural Networks into Model Predictive control for Thermal Torque Derating of Electric Machines 22

Integrating Recurrent Neural Networks into Model Predictive ...

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22nd World Congress of the International Federation of Automatic control (IFAC)

作者： Winkler, Alexander Wang, Weizhou Norouzi, Armin Gordon, David Koch, C. R. Andert, Jakob Rhein Westfal TH Aachen Teaching & Res Area Mechatron Mobile Prop Aachen Germany Univ Alberta Dept Mech Engn Edmonton AB Canada

ISBN: (纸本)9781713872344

For cost and space reasons, the electric machine (EM) and its cooling system in electrified powertrains for road vehicles are usually designed in such a way that the maximum power cannot be called up continuously. For this purpose, control systems are used that derate the power of the EM depending on its temperature. In this context, this paper presents the integration of a Recurrent Neural Network (RNN) model into a nonlinear model predictive control (MPC) to efficiently control the driving performance of an electric vehicle (EV) on a race track and reduce thermal derating effects. The discrete black-box RNN model incorporating a Long Short-Term Memory (LSTM) layer describing the thermal dynamics of the electric machine is combined with the continuous-time one-dimensional vehicle dynamics to form the hybrid MPC. For the selected example, the RNN-MPC outperforms a standard linear derating strategy by 6.23% in terms of energy consumption while achieving equal lap speed and maximum machine temperatures. Compared to an existing grey-box thermal network model, the RNN model significantly reduces the temperature prediction error by 28%. The trajectory tracking problem is formulated using acados and deployed on a dSPACE SCALEXIO embedded system to meet real-time requirements. Copyright (c) 2023 The Authors.

关键词： nonlinear and optimal automotive control neural networks temperature control

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Model-based and Koopman-based predictive control: a braking control systems comparison 3

Model-based and Koopman-based predictive control: a braking ...

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3rd Modeling, Estimation and control Conference (MECC)

作者： Sassella, Andrea Breschi, Valentina Korda, Milan Formentin, Simone Politecn Milan Dipartimento Elett Informat & Bioingn DEIB Piazza Leonardo da Vinci 32 I-20133 Milan Italy Eindhoven Univ Technol Eindhoven Netherlands CNRS LAAS F-31400 Toulouse France Czech Tech Univ Fac Elect Engn Prague 16626 Czech Republic

ISBN: (纸本)9781713872344

Anti-locking Braking systems are crucial safety systems in modern vehicles. In this work, we investigate the possibility to use Model Predictive control (MPC) for braking systems by considering three different models identified from data. Specifically, we consider two models, whose structure and the identification procedure are driven by physics principles, and a third black-box modeling approach that relies on Koopman theory. By comparing the effectiveness of the three resulting MPC schemes in a high-fidelity simulation environment, we show that Koopman-based MPC can generally be a viable solution for the design of braking controllers, which might not be the case of nonlinear MPC or approximated scheme like the second one we test. Copyright (c) 2023 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Koopman control Braking control nonlinear predictive control Model predictive and optimization-based control nonlinear and optimal automotive control

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Trajectory Planning for Autonomous Vehicles combining nonlinear optimal control and Supervised Learning 21st

Trajectory Planning for Autonomous Vehicles combining Nonlin...

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21st IFAC World Congress on Automatic control - Meeting Societal Challenges

作者： Markolf, Lukas Eilbrecht, Jan Stursberg, Olaf Univ Kassel Dept Elect Engn & Comp Sci Control & Syst Theory Kassel Germany

This paper considers computationally efficient planning of reference trajectories for autonomous on-road vehicles in a cooperative setting. The basic element of the approach is the notion of so-called maneuvers, which allow to cast the nonlinear and non-convex planning task into a highly structured optimal control problem. This can be solved quite efficiently, but not fast enough for online operation when considering nonlinear vehicle models. Therefore, the approach proposed in this paper aims at approximating solutions using a supervised learning approach: First, training data are generated by solving optimal control problems and are then used to train a neural network. As is demonstrated for a cooperative overtaking maneuver, this approach shows good performance, while (contrasting approaches like reinforcement learning) requiring only low training effort. Copyright (C) 2020 The Authors.

关键词： nonlinear and optimal automotive control automated driving machine learning intelligent control neural networks

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A Sequential Quadratic Programming Approach to Combined Energy and Emission Management of a Heavy-Duty Parallel-Hybrid Vehicle 10th

A Sequential Quadratic Programming Approach to Combined Ener...

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10th IFAC Symposium on Advances in automotive control (AAC)

作者： Mennen, S. C. M. Willems, F. P. T. Donkers, M. C. F. Eindhoven Univ Technol Eindhoven Netherlands

Combined Energy and Emission Management (CEEM) problems are a class of optimal control problems that aim to minimize operational costs of (hybrid electric) powertrains with after-treatment system subject to constraints on emissions imposed by legislation. In this paper, a parallel-hybrid heavy-duty vehicle with a Variable Turbine Geometry (VTG) and an Exhaust-Gas Recirculation (EGR) system is considered. The CEEM problem is solved using Sequential Quadratic Programming (SQP) for which the powertrain and after-treatment models are approximated as smooth functions. It will be shown that solving the CEEM problem using SQP is computationally much more efficient when compared to other techniques like dynamic programming. It will also be shown that most of the benefits from CEEM come from the hybrid powertrain and not from regulating the VTG and ERG mass flows. Furthermore, zero emission zones and local emission constraints can also be included without too much effort. Copyright (c) 2022 The Authors. This is an open access article under the CC BY-NC-ND license

关键词： nonlinear and optimal automotive control Engine Modelling and control

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