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IEEE Transactions on Intelligent Vehicles 2024年第9期9卷 1-12页

作者： Borsboom, Olaf Salazar, Mauro Keulen, Thijs van Control Systems Technology section Department of Mechanical Engineering Eindhoven University of Technology Eindhoven MB The Netherlands

This paper explores the eco-driving problem of parallel hybrid electric vehicles, intended to drive a certain distance within a limited amount of time, where the longitudinal vehicle velocity and powertrain controls are optimized to minimize the fuel consumption. In particular, we incorporate Pontryagin's Minimum Principle (PMP) and singular control theory in an optimization framework to find the fuel-optimal velocity and power-split control policy for the prime mover and the electric machine with global optimality guarantees. In addition, we present reformulations and derivations, so that the same problem can be solved jointly using another framework based on convex optimization, with the same global optimality properties, employing methods originally derived for timeoptimal control of race cars. Thereby, we formally show the equivalence between the eco-driving and the racing problem. We showcase both our frameworks with numerical solutions, drawing three comparisons: First, we solve the velocity and power-split problem, both sequentially and jointly, using the PMP framework. We show that the latter can improve the fuel consumption by 2.6 %. Second, we benchmark the PMP and the convex framework by solving the joint problem with both methods and observe a discrepancy of 0.14% in terms of the resulting fuel energy consumption. Finally, in a numerical study addressing the performance of both methods individually, we observe that the efficiency of the PMP and the convex framework are strongly dependent on the stopping criteria and the discretization step size, respectively. Authors

关键词： Convex optimization

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Comparative analysis of the ISO tolerance class of 3D-printed spur cylindrical gears produced with material extrusion and powder bed fusion techniques

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International Journal of Powertrains 2024年第3期13卷 225-247页

作者： Vakouftsis, Christos Vasileiou, Georgios Kaisarlis, Georgios Kalligeros, Christos Papalexis, Christos Zalimidis, Pavlos Provatidis, Christopher Spitas, Vasileios National Technical University of Athens Greece Mechanical Design and Control Systems Section School of Mechanical Engineering National Technical University of Athens Athens Greece Athens Greece

The present work correlates the printing accuracy of two major 3D-printing techniques applied for the production of spur gears by calculating the ISO tolerance class that defines the accuracy level of gears in all industrial applications. Specimens were produced using material extrusion MEX-TRB/P/ABS and powder bed fusion PBF-LB/P/PA22 techniques, evaluated by using a touch-probe coordinate measuring machine (CMM) with appropriate software and finally compared in terms of standards gear geometry errors and deviations. MEX-TRB produced specimens exhibited higher levels of accuracy and ISO quality class correspondingly. Both sets of specimens were found to comply with ISO Q12 or higher that greatly varies from the quality class of most metallic gears used in industrial applications. The printing parameters are detailed and discussed extensively in an attempt to provide insight on further research and process optimisation for the production of AM gears. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Coordinate measuring machines

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Data-Efficient Quadratic Q-Learning Using LMIs 63

Data-Efficient Quadratic Q-Learning Using LMIs

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Van Hulst, J.S. Heemels, W.P.M.H. Antunes, D.J. Eindhoven University of Technology Control Systems Technology Section Department of Mechanical Engineering Netherlands

ISBN: (纸本)9798350316339

Reinforcement learning (RL) has seen significant research and application results but often requires large amounts of training data. This paper proposes two data-efficient off-policy RL methods that use parametrized Q-learning. In these methods, the Q-function is chosen to be linear in the parameters and quadratic in selected basis functions in the state and control deviations from a base policy. A cost penalizing the 1-norm of Bellman errors is minimized. We propose two methods: Linear Matrix Inequality Q-Learning (LMI-QL) and its iterative variant (LMIQLi), which solve the resulting episodic optimization problem through convex optimization. LMI-QL relies on a convex relaxation that yields a semidefinite programming (SDP) problem with linear matrix inequalities (LMIs). LMI-QLi entails solving sequential iterations of an SDP problem. Both methods combine convex optimization with direct Q-function learning, significantly improving learning speed. A numerical case study demonstrates their advantages over existing parametrized Q-learning methods. © 2024 IEEE.

关键词： Convex optimization

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AIRPORT INFRASTRUCTURE SIZING FOR A REGIONAL ELECTRIC AVIATION NETWORK 34th

AIRPORT INFRASTRUCTURE SIZING FOR A REGIONAL ELECTRIC AVIATI...

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34th Congress of the International Council of the Aeronautical Sciences, ICAS 2024

作者： Vehlhaber, Finn Salazar, Mauro Control Systems Technology section Department of Mechanical Engineering Eindhoven University of Technology Eindhoven5600 MB Netherlands

Electric aircraft will soon be introduced to regional markets and airport operators will need to provide the necessary charging infrastructure to enable their operations. Most regional airports lack the grid connection to support fleet charging and a recent push to reduce emissions in the energy mix that sees national power grids struggling calls for a grid-independent and sustainable solution for these airports. This paper introduces a framework for the sizing of a system for on-site energy generation and storage in support of electric flight operations for a regional aviation network. Specifically, it shows how the properties of the flight network can be leveraged for the strategic sizing of the electric infrastructure for a case study on the Dutch Leeward Antilles, highlighting airport specific requirements that arise from considering the air transport and energy system as a whole. Results with rough cost estimations for the infrastructure components show that installing energy and charging infrastructure at every airport in the flight network leads to a marginal cost decrease compared to a selective strategy where infrastructure is only installed at few airports. This latter case is also shown to have repercussions on infrastructure decisions on every other airport in the network. On the other hand, adapting the battery size of the aircraft in the fleet to the flight network requirements can lead to a significant decrease of up to 17% in total infrastructure costs. © 2024, International Council of the Aeronautical Sciences. All rights reserved.

关键词： Fleet operations

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Incremental Stability of Discrete-Time First-Order Projection Elements ⁎

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IFAC-PapersOnLine 2024年第11期58卷 123-128页

作者： Riccardo Bertollo Sebastiaan van den Eijnden W.P.M.H. (Maurice) Heemels Control Systems Technology section Department of Mechanical Engineering Eindhoven University of Technology The Netherlands

In this paper, we present and analyze a class of discrete-time and sampled-data controllers named first-order projection elements (FOPEs). FOPEs form a class of hybrid controllers that have an underlying linear base dynamics, which are combined with a projection operator that ensures that the input-output (I/O) pair is contained in a well-designed sector. The I/O sector-constraint enables performance benefits in various application domains including motion control of high-precision machines. After the introduction of FOPEs, we focus on incremental stability analysis of these hybrid controllers. We show that the properties of the projection operator ensure incremental stability of the open-loop FOPE dynamics in case of an underlying linear dynamics that is exponentially stable. In this case, we compute tight bounds on the incremental gain. Under an appropriate small-gain condition, we show incremental input-to-state stability of the sampled-data interconnection of a FOPE with a continuous-time linear time-invariant plant. For the case of projected integrator dynamics, we provide a counterexample showing that incremental stability can be compromised. We provide additional, necessary and sufficient conditions under which FOPEs with integrator dynamics are incrementally asymptotically stable. Numerical simulations provide a practical example of the established incremental properties for the trajectories of a dynamical system coming from an industrial motion control application.

关键词： Hybrid integrator-gain systems Projected dynamics Incremental stability Sampled-data systems

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Iterative Robust Experiment design for MIMO System Identification via the S-Lemma

Iterative Robust Experiment Design for MIMO System Identific...

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2023 IEEE Conference on control Technology and Applications, CCTA 2023

作者： Dirkx, Nic Tiels, Koen Oomen, Tom Asml Veldhoven Netherlands Eindhoven University of Technology Control Systems Technology Dept. of Mechanical Engineering Eindhoven Netherlands Eindhoven University of Technology Control Systems Technology Section Department of Mechanical Engineering Eindhoven Netherlands Delft University of Technology Delft Center for Systems and Control Delft Netherlands

ISBN: (纸本)9798350335446

Optimal input design plays an important role in system identification for complex and multivariable systems. A known paradox in input design is that the optimal inputs depend on the true but unknown system. The aim of this paper is to design inputs for multivariable systems that are robust to all system variations within a given continuous uncertainty set. In the presented approach, the robust design problem is cast as an infinite-dimensional min-max optimization problem, and tackled via the S-lemma in an iterative approximation scheme. Experimental results from a multivariable motion system show that the algorithm enables significant robustness improvements. © 2023 IEEE.

关键词： Iterative methods

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Research on workflow recognition for liver rupture repair surgery

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Mathematical Biosciences and engineering 2024年第2期21卷 1844-1856页

作者： Men, Yutao Zhao, Zixian Chen, Wei Wu, Hang Zhang, Guang Luo, Feng Yu, Ming Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China Medical Support Technology Research Department Systems Engineering Institute Academy of Military Sciences People’s Liberation Army Tianjin300161 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin300384 China

Liver rupture repair surgery serves as one tool to treat liver rupture, especially beneficial for cases of mild liver rupture hemorrhage. Liver rupture can catalyze critical conditions such as hemorrhage and shock. Surgical workflow recognition in liver rupture repair surgery videos presents a significant task aimed at reducing surgical mistakes and enhancing the quality of surgeries conducted by surgeons. A liver rupture repair simulation surgical dataset is proposed in this paper which consists of 45 videos collaboratively completed by nine surgeons. Furthermore, an end-to-end SA-RLNet, a self attention-based recurrent convolutional neural network, is introduced in this paper. The self-attention mechanism is used to automatically identify the importance of input features in various instances and associate the relationships between input features. The accuracy of the surgical phase classification of the SA-RLNet approach is 90.6%. The present study demonstrates that the SA-RLNet approach shows strong generalization capabilities on the dataset. SA-RLNet has proved to be advantageous in capturing subtle variations between surgical phases. The application of surgical workflow recognition has promising feasibility in liver rupture repair surgery. ©2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons.

关键词： Image classification

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Data-Efficient Quadratic Q-Learning Using LMIs

arXiv

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arXiv 2024年

作者： van Hulst, J.S. Heemels, W.P.M.H. Antunes, D.J. The Control Systems Technology Section Department of Mechanical Engineering Eindhoven University of Technology Netherlands

Reinforcement learning (RL) has seen significant research and application results but often requires large amounts of training data. This paper proposes two data-efficient off-policy RL methods that use parametrized Q-learning. In these methods, the Q-function is chosen to be linear in the parameters and quadratic in selected basis functions in the state and control deviations from a base policy. A cost penalizing the 1-norm of Bellman errors is minimized. We propose two methods: Linear Matrix Inequality Q-Learning (LMI-QL) and its iterative variant (LMI-QLi), which solve the resulting episodic optimization problem through convex optimization. LMI-QL relies on a convex relaxation that yields a semidefinite programming (SDP) problem with linear matrix inequalities (LMIs). LMI-QLi entails solving sequential iterations of an SDP problem. Both methods combine convex optimization with direct Q-function learning, significantly improving learning speed. A numerical case study demonstrates their advantages over existing parametrized Q-learning methods. © 2024, CC BY-NC-ND.

关键词： Convex optimization

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Data-Efficient Quadratic Q-Learning Using LMIs

Data-Efficient Quadratic Q-Learning Using LMIs

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IEEE Conference on Decision and control

作者： J.S. Van Hulst W.P.M.H. Heemels D.J. Antunes Department of Mechanical Engineering Control Systems Technology Section Eindhoven University of Technology the Netherlands

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Reinforcement learning (RL) has seen significant research and application results but often requires large amounts of training data. This paper proposes two data-efficient off-policy RL methods that use parametrized Q-learning. In these methods, the Q-function is chosen to be linear in the parameters and quadratic in selected basis functions in the state and control deviations from a base policy. A cost penalizing the $\ell_{1}$-norm of Bellman errors is minimized. We propose two methods: Linear Matrix Inequality Q-Learning (LMI-QL) and its iterative variant (LMIQLi), which solve the resulting episodic optimization problem through convex optimization. LMI-QL relies on a convex relaxation that yields a semidefinite programming (SDP) problem with linear matrix inequalities (LMIs). LMI-QLi entails solving sequential iterations of an SDP problem. Both methods combine convex optimization with direct Q-function learning, significantly improving learning speed. A numerical case study demonstrates their advantages over existing parametrized Q-learning methods.

关键词： Q-learning Semidefinite programming Costs Training data Minimization Linear programming Linear matrix inequalities Iterative methods Optimization Convergence

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Experimental Comparison of H-Infinity Synthesis and Feedback Linearization on a MIMO Gimbal Example

Experimental Comparison of H-Infinity Synthesis and Feedback...

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2023 IEEE Conference on control Technology and Applications, CCTA 2023

作者： Ozcelik, Mert Kurkcu, Burak Bayraktaroglu, Zeki Yagiz Aselsan Inc. Control Systems Design Department Ankara06750 Turkey University of California Department of Mechanical Engineering BerkeleyCA94720 United States Hacettepe University Department of Computer Engineering Ankara06800 Turkey Istanbul Technical University Department of Mechanical Engineering Istanbul34437 Turkey

ISBN: (纸本)9798350335446

This study investigates the controller types and effects of the design techniques on gimbal stabilization. Since implementing the controllers and determining the system's robustness is straightforward, the frequency domain design approaches are widely used with linear controllers in electro-optical inertial stabilized systems. Besides, the required system models can be obtained from experimental data easily. Nevertheless, the system model could change in the operational conditions. Therefore, nonlinear controllers are needed in this situation. In this investigation, we obtained a proper system model using system identification methods to design a mixed-sensitivity H∞ controller. Then, we proposed an input-output feedback linearization controller with a linear quadratic regulator (LQR) based state feedback controller design to stabilize the 2 degrees of freedom (DOF) gimbal. Finally, we compared these two controllers regarding reference tracking, sensitivity function, and disturbance rejection performances on the experimental setup under 6 DOF disturbance profiles. © 2023 IEEE.

关键词： controllers

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