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Convergence of energy-based learning in linear resistive networks

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arXiv 2025年

作者： Huijzer, Anne-Men Chaffey, Thomas Besselink, Bart van Waarde, Henk J. Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Groningen Netherlands CogniGron - Groningen Cognitive Systems and Materials Center Netherlands Control Group Department of Engineering University of Cambridge United Kingdom School of Electrical and Computer Engineering University of Sydney Australia

Energy-based learning algorithms are alternatives to backpropagation and are well-suited to distributed implementations in analog electronic devices. However, a rigorous theory of convergence is lacking. We make a first step in this direction by analysing a particular energy-based learning algorithm, Contrastive Learning, applied to a network of linear adjustable resistors. It is shown that, in this setup, Contrastive Learning is equivalent to projected gradient descent on a convex function, for any step size, giving a guarantee of convergence for the *** Codes 65K10, 68T05, 93B30, 93D99 © 2025, CC BY-SA.

关键词： Contrastive Learning

Non-overshooting output shaping for switched linear systems under arbitrary switching using eigenstructure assignment

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arXiv 2024年

作者： Wulff, K. Honecker, M.C. Schmid, R. Reger, J. Control Systems Group Technische Universtät Ilmenau P.O. Box 10 05 65 IlmenauD-98684 Germany Control Engineering Group Technische Universtät Ilmenau P.O. Box 10 05 65 IlmenauD-98684 Germany Dept. of Electrical and Electronic Engineering University of Melbourne Grattan Street ParkvilleVIC3010 Australia

We consider the analytical control design for a pair of switched linear multiple-input multiple-output (MIMO) systems that are subject to arbitrary switching signals. A state feedback controller design method is proposed to obtain an eigenstructure assignment that ensures that the closed-loop switched system is globally asymptotically stable, and the outputs achieve the non-overshooting tracking of a step reference. Our analysis indicates whether non-overshooting or even monotonic tracking is achievable for the given system and considered outputs and provides a choice of possible eigenstructures to be assigned to the constituent subsystems. We derive a structural condition that verifies the feasibility of the chosen assignment. A constructive algorithm to obtain suitable feedback matrices is provided, and the method is illustrated with numerical examples. © 2024, CC BY-NC-ND.

关键词： State feedback

Variational message passing for online polynomial NARMAX identification

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arXiv 2022年

作者： Kouw, Wouter M. Podusenko, Albert Koudahl, Magnus T. Schoukens, Maarten The Bayesian Intelligent Autonomous Systems Lab The Department of Electrical Engineering TU Eindhoven Eindhoven Netherlands The Control Systems Group The Department of Electrical Engineering TU Eindhoven Eindhoven Netherlands

We propose a variational Bayesian inference procedure for online nonlinear system identification. For each output observation, a set of parameter posterior distributions is updated, which is then used to form a posterior predictive distribution for future outputs. We focus on the class of polynomial NARMAX models, which we cast into probabilistic form and represent in terms of a Forney-style factor graph. Inference in this graph is efficiently performed by a variational message passing algorithm. We show empirically that our variational Bayesian estimator outperforms an online recursive least-squares estimator, most notably in small sample size settings and low noise regimes, and performs on par with an iterative least-squares estimator trained offline. © 2022, CC BY.

关键词： Message passing

Learning For Predictive control: A Dual Gaussian Process Approach

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arXiv 2022年

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

An important issue in model-based control design is that an accurate dynamic model of the system is generally nonlinear, complex, and costly to obtain. This limits achievable control performance in practice. Gaussian process (GP) based estimation of system models is an effective tool to learn unknown dynamics directly from input/output data. However, conventional GP-based control methods often ignore the computational cost associated with accumulating data during the operation of the system and how to handle forgetting in continuous adaption. In this paper, we present a novel Dual Gaussian Process (DGP) based model predictive control (MPC) strategy that enables efficient use of online learning based predictive control without the danger of catastrophic forgetting. The bio-inspired DGP structure is a combination of a long-term GP and a short-term GP, where the long-term GP is used to keep the learned knowledge in memory and the short-term GP is employed to rapidly compensate unknown dynamics during online operation. Furthermore, a novel recursive online update strategy for the short-term GP is proposed to successively improve the learnt model during online operation. Effectiveness of the proposed strategy is demonstrated via numerical simulations. © 2022, CC BY-NC-SA.

关键词： Model predictive control

System Level Synthesis for Affine control Policies: Model Based and Data-Driven Settings

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arXiv 2025年

作者： Schüepp, Lukas De Pasquale, Giulia Dörfler, Florian Alonso, Carmen Amo Automatic Control Laboratory Department of Electrical Engineering and Information Technology ETH Zurich Physikstrasse 3 Zurich8092 Switzerland Computer Science Department of Stanford University Palo AltoCA United States Control Systems Group Department of Electrical Engineering TU Eindhoven Flux Groene Loper 19 Eindhoven5612AP Netherlands

There is an increasing need for effective control of systems with complex dynamics, particularly through data-driven approaches. System Level Synthesis (SLS) has emerged as a powerful framework that facilitates the control of large-scale systems while accounting for model uncertainties. SLS approaches are currently limited to linear systems and time-varying linear control policies, thus limiting the class of achievable control strategies. We introduce a novel closed-loop parameterization for time-varying affine control policies, extending the SLS framework to a broader class of systems and policies. We show that the closed-loop behavior under affine policies can be equivalently characterized using past system trajectories, enabling a fully data-driven formulation. This parameterization seamlessly integrates affine policies into optimal control problems, allowing for a closed-loop formulation of general Model Predictive control (MPC) problems. To the best of our knowledge, this is the first work to extend SLS to affine policies in both model-based and data-driven settings, enabling an equivalent formulation of MPC problems using closed-loop maps. We validate our approach through numerical experiments, demonstrating that our model-based and data-driven affine SLS formulations achieve performance on par with traditional model-based MPC. © 2025, CC BY.

关键词： Distributed parameter control systems

Time-Optimal Trajectory Planning in Highway Scenarios using Basis-Spline Parameterization

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arXiv 2023年

作者： Dorpmüller, Philip Schmitz, Thomas Bejagam, Naveen Bertram, Torsten TU Dortmund University Institute of Control Theory and Systems Engineering Dortmund44227 Germany ZF Group Automated Driving and Integral Cognitive Safety Düsseldorf40547 Germany

Basis splines enable a time-continuous feasibility check with a finite number of constraints. Constraints apply to the whole trajectory for motion planning applications that require a collision-free and dynamically feasible trajectory. Existing motion planners that rely on gradient-based optimization apply time scaling to implement a shrinking planning horizon. They neither guarantee a recursively feasible trajectory nor enable reaching two terminal manifold parts at different time scales. This paper proposes a nonlinear optimization problem that addresses the drawbacks of existing approaches. Therefore, the spline breakpoints are included in the optimization variables. Transformations between spline bases are implemented so a sparse problem formulation is achieved. A strategy for breakpoint removal enables the convergence into a terminal manifold. The evaluation in an overtaking scenario shows the influence of the breakpoint number on the solution quality and the time required for optimization. Copyright © 2023, The Authors. All rights reserved.

关键词： Trajectories

Model predictive control of hemodynamics during intravenous general anesthesia

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Model predictive control of hemodynamics during intravenous ...

International Conference on System Theory, control, and Computing (ICSTCC)

作者： Hamed Farbakhsh Erhan Yumuk Ghada Ben Othman Robin De Keyser Dana Copot Isabela Birs Clara M. Ionescu Department of Electromechanics Systems and Metal Engineering Research Group on Dynamical Systems and Control Tech Lane Science Park 125 Ghent University Zwijnaarde Belgium Department of Control and Automation Engineering Istanbul Technical University Istanbul Turkey Department of Automation Technical University of Cluj-Napoca Memorandumului no. 28 Cluj-Napoca Romania

In the operating rooms and the intensive care unit, it is crucial to manage the patient’s hemodynamic status, which includes factors like cardiac output and mean arterial pressure. Anesthesiologists confront a difficult task while monitoring high-risk patients. Cardiac output optimization has been found to enhance the result of high-risk patients in terms of hospital stay, mortality rate, post-operative problems, etc. The application of standard control approaches is restricted because the mean arterial pressure response of a patient using vasoactive medicines is modeled by a first-order dynamical system with time-varying parameters and a time-varying delay in the control input. In order to circumvent implementation challenges, this work develops an approximation technique that describes the system using a higher-order model. Predictive control is therefore used to comprehend the practical application of higher-order hemodynamic systems. The effectiveness of this strategy is demonstrated by the simulations and outcomes that are given.

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Sensorless force estimation on fingertip with gravitational compensation for heavy-duty pneumatic tri-grasper robot

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Sensorless force estimation on fingertip with gravitational ...

engineering Technology International Conference (ETIC 2022)

作者： A. Irawan M. I. Putra Azahar M. A. Hashimi Robotics Intelligent Systems & Control Engineering (RISC) Research Group Faculty of Electrical & Electronics Engineering Technology Universiti Malaysia Pahang Pahang Malaysia

The paper presents the proposed sensorless force estimator design for pneumatic robot fingertip by using gravitational compensation and pressure changed in pneumatic cylinder piston. The approach is done to replace the commercial force sensor that may be expensive for heavy-duty configuration. The formulation was done by considering the torque of robot's finger joint, finger dimension as well as its actuator and the different pressures in cylinder piston. The gravitational force is calculated from the geometry of the robot's finger as dynamic gain for the force of pneumatic cylinder. The proposed method is validated on a heay-duty pneumatic Tri-grasper Robot with the simple basic movement and blocked randomly by human barehand. The results show that the force output by the estimator is almost identical to the loadcell sensor that attached on the fingertip at about 2% error in average. The sensitivity is a bit low for small and fragile material but enough for heavy-duty application that generally with hard and rough surfaces.

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Direct Data-Driven State-Feedback control of Linear Parameter-Varying systems

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arXiv 2022年

作者： Verhoek, Chris Tóth, Roland Abbas, Hossam S. Control Systems group Dept. of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands Systems and Control Lab Institute for Computer Science and Control Budapest Hungary Institute for Electrical Engineering in Medicine Universität zu Lübeck Lübeck Germany

The framework of linear parameter-varying (LPV) systems has shown to be a powerful tool for the design of controllers for complex nonlinear systems using linear tools. In this work, we derive novel methods that allow to synthesize LPV state-feedback controllers directly from only a single sequence of data and guarantee stability and performance of the closed-loop system. We show that if the measured open-loop data from the system satisfies a persistency of excitation condition, then the full open-loop and closed-loop input-scheduling-state behavior can be represented using only the data. With this representation we formulate data-driven analysis and synthesis problems, where the latter yields controllers that guarantee stability and performance in terms of infinite horizon quadratic cost, generalized H2-norm and 2-gain of the closed-loop system. The controllers are synthesized by solving a semi-definite program. Additionally, we provide a synthesis method to handle noisy measurement data. Competitive performance of the proposed data-driven synthesis methods is demonstrated w.r.t. model-based synthesis in multiple simulation studies, including a nonlinear unbalanced disc system. © 2022, CC BY-NC-SA.

关键词： State feedback