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arXiv 2023年

作者： Aguiar, Miguel Das, Amritam Johansson, Karl H. Division of Decision and Control Systems and Digital Futures KTH Royal Institute of Technology Stockholm Sweden Control Systems Group Dept. of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands

We describe a recurrent neural network (RNN) based architecture to learn the flow function of a causal, time-invariant and continuous-time control system from trajectory data. By restricting the class of control inputs to piecewise constant functions, we show that learning the flow function is equivalent to learning the input-to-state map of a discrete-time dynamical system. This motivates the use of an RNN together with encoder and decoder networks which map the state of the system to the hidden state of the RNN and back. We show that the proposed architecture is able to approximate the flow function by exploiting the system’s causality and time-invariance. The output of the learned flow function model can be queried at any time instant. We experimentally validate the proposed method using models of the Van der Pol and FitzHugh-Nagumo oscillators. In both cases, the results demonstrate that the architecture is able to closely reproduce the trajectories of these two systems. For the Van der Pol oscillator, we further show that the trained model generalises to the system’s response with a prolonged prediction time horizon as well as control inputs outside the training distribution. For the FitzHugh-Nagumo oscillator, we show that the model accurately captures the input-dependent phenomena of excitability. © 2023, CC BY-NC-ND.

关键词： Recurrent neural networks

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Learning for Precision Motion of an Interventional X-ray System: Add-on Physics-Guided Neural network Feedforward control

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IFAC-PapersOnLine 2023年第2期56卷 7523-7528页

作者： Johan Kon Naomi de Vos Dennis Bruijnen Jeroen van de Wijdeven Marcel Heertjes Tom Oomen Control Systems Technology Group Departement of Mechanical Engineering Eindhoven University of Technology P.O. Box 513 5600 MB Eindhoven The Netherlands Philips Engineering Solutions Eindhoven The Netherlands ASML Veldhoven The Netherlands Delft Center for Systems and Control Delft University of Technology The Netherlands

Tracking performance of physical-model-based feedforward control for interventional X-ray systems is limited by hard-to-model parasitic nonlinear dynamics, such as cable forces and nonlinear friction. In this paper, these nonlinear dynamics are compensated using a physics-guided neural network (PGNN), consisting of a physical model, embedding prior knowledge of the dynamics, in parallel with a neural network to learn hard-to-model dynamics. To ensure that the neural network learns only unmodelled Effects, the neural network output in the subspace spanned by the physical model is regularized via an orthogonal projection-based approach, resulting in complementary physical model and neural network contributions. The PGNN feedforward controller reduces the tracking error of an interventional X-ray system by a factor of 5 compared to an optimally tuned physical model, successfully compensating the unmodeled parasitic dynamics.

关键词： Feedforward control physics-guided neural networks interventional X-ray

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Fair Artificial Currency Incentives in Repeated Weighted Congestion Games: Equity vs. Equality

Fair Artificial Currency Incentives in Repeated Weighted Con...

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IEEE Conference on Decision and control

作者： Leonardo Pedroso Andrea Agazzi W.P.M.H. Maurice Heemels Mauro Salazar Control Systems Technology section Eindhoven University of Technology The Netherlands Mathematics Department Universita di Pisa Pisa Italy INdAM (GNAMPA group)

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

When users access shared resources in a selfish manner, the resulting societal cost and perceived users’ cost is often higher than what would result from a centrally coordinated optimal allocation. While several contributions in mechanism design manage to steer the aggregate users choices to the desired optimum by using monetary tolls, such approaches bear the inherent drawback of discriminating against users with a lower income. More recently, incentive schemes based on artificial currencies have been studied with the goal of achieving a system-optimal resource allocation that is also fair. In this resource-sharing context, this paper focuses on repeated weighted congestion game with two resources, where users contribute to the congestion to different extents that are captured by individual weights. First, we address the broad concept of fairness by providing a rigorous mathematical characterization of the distinct societal metrics of equity and equality, i.e., the concepts of providing equal outcomes and equal opportunities, respectively. Second, we devise weight-dependent and time-invariant optimal pricing policies to maximize equity and equality, and prove convergence of the aggregate user choices to the system-optimum. In our framework it is always possible to achieve system-optimal allocations with perfect equity, while the maximum equality that can be reached may not be perfect, which is also shown via numerical simulations.

关键词： Costs Aggregates Simulation Mechanism design Games Pricing Resource management Currencies Incentive schemes Convergence

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Attitude Takeover control for Noncooperative Space Targets Based on Gaussian Processes with Online Model Learning

arXiv

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arXiv 2023年

作者： Liu, Yuhan Wang, Pengyu Lee, Chang-Hun Tóth, Roland The Control Systems Group Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands The Flight Dynamics and Control Laboratory Department of Aerospace Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of The Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

One major challenge for autonomous attitude takeover control for on-orbit servicing of spacecraft is that an accurate dynamic motion model of the combined vehicles is highly nonlinear, complex and often costly to identify online, which makes traditional model-based control impractical for this task. To address this issue, a recursive online sparse Gaussian Process (GP)-based learning strategy for attitude takeover control of noncooperative targets with maneuverability is proposed, where the unknown dynamics are online compensated based on the learnt GP model in a semi-feedforward manner. The method enables the continuous use of on-orbit data to successively improve the learnt model during online operation and has reduced computational load compared to standard GP regression. Next to the GP-based feedforward, a feedback controller is proposed that varies its gains based on the predicted model confidence, ensuring robustness of the overall scheme. Moreover, rigorous theoretical proofs of Lyapunov stability and boundedness guarantees of the proposed method-driven closed-loop system are provided in the probabilistic sense. A simulation study based on a high-fidelity simulator is used to show the effectiveness of the proposed strategy and demonstrate its high performance. © 2023, CC BY-NC-SA.

关键词： Maneuverability

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DeepRING: Learning Roto-translation Invariant Representation for LiDAR based Place Recognition

DeepRING: Learning Roto-translation Invariant Representation...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Sha Lu Xuecheng Xu Li Tang Rong Xiong Yue Wang State Key Laboratory of Industrial Control and Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Alibaba Group Hangzhou China

LiDAR based place recognition is popular for loop closure detection and re-localization. In recent years, deep learning brings improvements to place recognition by learnable feature extraction. However, these methods degenerate when the robot re-visits previous places with a large perspective difference. To address the challenge, we propose DeepRING to learn the roto-translation invariant representation from LiDAR scan, so that robot visiting the same place with a different perspective can have similar representations. There are two keys in DeepRING: the feature is extracted from sinogram, and the feature is aggregated by magnitude spectrum. The two steps keep the final representation with both discrimination and roto-translation invariance. Moreover, we state place recognition as a one-shot learning problem with each place being a class, leveraging relation learning to build representation similarity. Substantial experiments are carried out on public datasets, validating the effectiveness of each proposed component, and showing that DeepRING outperforms the comparative methods, especially in dataset level generalization.

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Robust Sliding Mode control of a Quadrotor With Disturbance Rejection for Enhanced Stability and Performance 6

Robust Sliding Mode Control of a Quadrotor With Disturbance ...

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6th International Conference on Robotics and Automation in Industry, ICRAI 2024

作者： Ullah, Hameed Zarin, Owais Ahmad, Irfan Ahmad, Sarfraz Abbas, Ansar Lippiello, Vincenzo University of Naples Federico II Department of Electrical Engineering and Information Technology Naples Italy Riphah International University Department of Electrical Engineering Islamabad Pakistan School of Bilbao - EIB/BIE University of the Basque Country - UPV/EHU Automatic Control Group ACG Department of Automatic Control and Systems Engineering Rafael Moreno 3 Bilbao48013 Spain CEME Department of Electrical Engineering Islamabad Pakistan

ISBN: (纸本)9798350355703

This research presents a comprehensive analysis of quadrotor stabilization and trajectory tracking control using Proportional-Integral-Derivative (PID) and Sliding Mode control (SMC) with integrated disturbance rejection. The nonlinear dynamics of the quadrotor are formulated using the Newton-Euler approach, accounting for six degrees of freedom. The PID controller is implemented as a baseline, while the SMC is designed for enhanced robustness to model uncertainties and external perturbations, with a disturbance observer for managing time-varying disturbances. A rigorous Lyapunov stability proof ensures global asymptotic stability of the SMC-controlled system. Extensive simulations evaluate both controllers in terms of tracking accuracy, control effort, and robustness to external disturbances. Results show that SMC outperforms PID in dis-turbance rejection, tracking accuracy, and robustness, making it ideal for UAV applications in complex environments. © 2024 IEEE.

关键词： Sliding mode control

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Adaptive control of Cover Wind Speed on Fume Hood 2

Adaptive Control of Cover Wind Speed on Fume Hood

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2022 2nd International Conference on Power System and Energy Internet, PoSEI 2022

作者： Zhou, Shengjie Yue, You Xu, Chen Ou, Chuangang NARI Group Corporation State Grid Electric Power Research Institute China Nari Technology Nanjing Control Systems Co. Ltd Nanjing China

During experiments carried out in the fume hood, the fluidity and viscosity of the air in the fume hood cause problems such as turbulence and gas viscosity, which affect the accuracy of wind speed measurement on the fume hood. As a solution to these problems, an adaptive control system of wind speed on the fume hood based on the Kalman filter is designed. The improved Kalman filter is used to reduce the influence of turbulence and gas viscosity on the measurement of cover wind speed, and improve the measurement accuracy. The adaptive PID control algorithm is used to adjust the air valve to improve the control accuracy of cover wind speed. Experiments revealed that our proposed system for hood control outperforms traditional alternatives: it can effectively improve the measurement accuracy and control accuracy of the cover wind speed, reduce the number of control times of the air valve, improve the service life of the fume hood, and ensure the safety of experimenters. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Kalman filters

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Direct Learning for Parameter-Varying Feedforward control: A Neural-network Approach

Direct Learning for Parameter-Varying Feedforward Control: A...

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IEEE Conference on Decision and control

作者： Johan Kon Jeroen van de Wijdeven Dennis Bruijnen Roland Tóth Marcel Heertjes Tom Oomen Departement of Mechanical Engineering Control Systems Technology Group Eindhoven University of Technology Eindhoven The Netherlands ASML Veldhoven The Netherlands Philips Engineering Solutions Eindhoven The Netherlands Control Systems Group Electrical Engineering Eindhoven University of Technology The Netherlands Institute for Computer Science and Control Budapest Hungary Delft University of Technology Delft The Netherlands

The performance of a feedforward controller is primarily determined by the extent to which it can capture the relevant dynamics of a system. The aim of this paper is to develop an input-output linear parameter-varying (LPV) feedforward parameterization and a corresponding data-driven estimation method in which the dependency of the coefficients on the scheduling signal are learned by a neural network. The use of a neural network enables the parameterization to compensate a wide class of constant relative degree LPV systems. Efficient optimization of the neural-network-based controller is achieved through a Levenberg-Marquardt approach with analytic gradients and a pseudolinear approach generalizing Sanathanan-Koerner to the LPV case. The performance of the developed feedforward learning method is validated in a simulation study of an LPV system showing excellent performance.

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Direct Learning for Parameter-Varying Feedforward control: A Neural-network Approach

arXiv

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arXiv 2023年

作者： Kon, Johan van de Wijdeven, Jeroen Bruijnen, Dennis Tóth, Roland Heertjes, Marcel Oomen, Tom Control Systems Technology Group Departement of Mechanical Engineering Eindhoven University of Technology Eindhoven Netherlands ASML Veldhoven Netherlands Philips Engineering Solutions Eindhoven Netherlands Control Systems Group Electrical Engineering Eindhoven University of Technology Netherlands The Institute for Computer Science and Control Budapest Hungary Delft University of Technology Delft Netherlands

关键词： Learning systems

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Prediction Horizon Requirements for Automated Driving: Optimizing Safety, Comfort, and Efficiency

Prediction Horizon Requirements for Automated Driving: Optim...

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IEEE Symposium on Intelligent Vehicle

作者： Manuel Muñoz Sánchez Chris van der Ploeg Robin Smit Jos Elfring Emilia Silvas Rene van de Molengraft Mechanical Engineering Department Robotics group Mechanical Engineering Department Dynamics and Control Group Integrated Vehicle Safety Department TNO Helmond The Netherlands VDL CropTeq Robotics Eindhoven The Netherlands Mechanical Engineering Department Control Systems Technology group Eindhoven University of Technology Eindhoven The Netherlands

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

Predicting the movement of other road users is beneficial for improving automated vehicle (AV) performance. However, the relationship between the horizon of these predictions and AV performance remains unclear. Despite the existence of numerous trajectory prediction algorithms, no studies have been conducted on how varying prediction horizons affect AV safety and other vehicle performance metrics, resulting in undefined horizon requirements for prediction methods. Our study addresses this gap by examining the effects of different prediction horizons on AV performance, focusing on safety, comfort, and efficiency. Through multiple experiments using a state-of-the-art, risk-based predictive trajectory planner, we simulated predictions with horizons up to 20 seconds. Based on our simulations, we propose a framework for specifying the minimum required and optimal prediction horizons depending on specific AV performance criteria and application needs. Our results indicate that a horizon of 1.6 seconds is required to prevent collisions with crossing pedestrians, horizons of 7-8 seconds yield the best efficiency, and horizons up to 15 seconds improve passenger comfort. We conclude that prediction horizon requirements are application-dependent, and recommend aiming for a prediction horizon of 11.8 seconds as a general guideline for applications involving crossing pedestrians.

关键词： Measurement Pedestrians Intelligent vehicles Roads Pipelines Predictive models Prediction algorithms

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