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machine learning control FOR EXPERIMENTAL TURBULENT FLOW TARGETING THE REDUCTION OF A RECIRCULATION BUBBLE

ASME Fluids Engineering Division Summer Meeting

作者： Chovet, Camila Lippert, Marc Keirsbulck, Laurent Noack, Bernd R. Foucaut, Jean-Marc Univ Valenciennes & Hainaut Cambresis LAMIH UMR CNRS 8201 Campus Mt Houy F-59300 Famars France LIMSI CNRS UPR 3251 Campus Univ Orsay F-91405 Orsay France Tech Univ Carolo Wilhelmina Braunschweig Inst Stromungsmech D-38108 Braunschweig Germany Ecole Cent Lille LML UMR CNRS 8107 Lille France

ISBN: (纸本)9780791858066

We experimentally control the turbulent flow over backward facing step (Re-H = 31500). The goal is to modify the internal (Xr) and external (Lr) recirculation points and consequently the recirculation zone (Ar). A model-free machine learning control (MLC) is used as control logic. As benchmark, an optimized periodic forcing is employed. MLC generalizes periodic forcing by a multi frequency actuation. In addition, a sensor-based control and a non-autonomous feedback, open- and closed-loop laws, were use to optimize the control. The MLC multi-frequency forcing outperforms, as expected, periodic forcing. The non autonomous feedback brings a further improvement. The unforced and actuated flows have been investigated in real-time with a TSI particle image velocinzetry (NV) system. The current study shows that a generalization of multi-frequency forcing and sensor feedback significantly reduces the turbulent recirculation zone, far beyond optimized periodic forcing. The study suggests that MLC can effectively explore and optimize new feedback actuation mechanisms and we anticipate MLC to be a game changer in turbulence control.

关键词： machine learning control experimental flow control recirculation zone

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machine learning control for experimental shear flows targeting the reduction of a recirculation bubble

Machine learning control for experimental shear flows target...

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20th World Congress of the International-Federation-of-Automatic-control (IFAC)

作者： Chovet, C. Keirsbulck, L. Noack, B. R. Lippert, M. Foucaut, J-M. Univ Valenciennes & Hainaut Cambresis LAMIH UMR CNRS 8201 F-59300 Valenciennes France Univ Orsay LIMSI UPR CNRS 3251 F-91405 Orsay France Tech Univ Carolo Wilhelmina Braunschweig Inst Stromungsmech D-38108 Braunschweig Germany Ecole Cent Lille LML UMR CNRS 8107 Lille France

The goal is to experimentally reduce the recirculation zone of a turbulent flow (Re-H = 31500). The flow is manipulated by a row of micro-blowers (pulsed jets) that are able to generate unsteady jets proportional to any variable DC. Already, periodic jet injection at a forcing frequency of St(H) = 0.226 can effectively reduce the reattachment length and thus the recirculation zone. A model-free machine learning control (MLC) is used to improve performance. MLC optimizes a control law with respect to a cost function and applies genetic programming as regression technique. The cost function is based on the recirculation length and penalizes actuation. MLC is shown to outperform periodic forcing. The current study demonstrates the efficacy of MLC to reduce the recirculation zone in a turbulent flow regime. Given current and past successes, we anticipate numerous experimental MLC applications. (C) 2017, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： machine learning control experimental flow control recirculation zone

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Comparative performance analysis of fractional-order nonlinear PID controller for complex surge tank system: tuning through machine learning control approach

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第33期83卷 78923页

作者： Gupta, Devbrat Goyal, Vishal Kumar, Jitendra GLA Univ Dept Elect & Commun Engn Mathura 281406 India

In this research article, a fractional-order nonlinear Proportional plus Integral plus Derivative (FONPID) controller is incorporated into a complex surge tank system where the gains of the controller are tuned through a machine learning control approach to control the nonlinear variations in level setpoints. Obtaining a proper output response from a non-linear system is challenging and demanding for researchers. In this case, nonlinear Proportional plus Integral plus Derivative (NPID) and conventional Proportional plus Integral plus Derivative (PID) controllers are not sufficient for obtaining desired output robustness in the system performances. Hence, to fulfill the need for an adaptive controller for a spherical surge tank system, FONPID can be a better choice. The machine learning control is applied to the gains of the FONPID controller with the Cuckoo Search Optimization Algorithm (CSA), a swarm-intelligence algorithm mostly known for its levy flights and searching pattern for best quality eggs. The whole idea of using machine learning control is to tune the gains to make the controller adaptive towards parametric variation and uncertainties. The machine learning control uses the Integral of Absolute Error (IAE) performance index criteria as the minimum objective function of CSA for tuning of gain constraints of the controllers. The proposed FONPID controller is then compared with NPID and conventional PID controllers to stabilize level setpoint variations. The results demonstrate that the FONPID controller gives better, robust, and optimum results over NPID and PID controllers. In comparison to NPID and PID controllers, the FONPID controller performs significantly better, with gains ranging from 11.68% to 215.31% across various operational modes and system parametric variations.

关键词： Fractional-order Nonlinear PID Surge Tank IAE Cuckoo search algorithm machine learning control

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machine learning control - explainable and analyzable methods

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PHYSICA D-NONLINEAR PHENOMENA 2020年 412卷 132582-132582页

作者： Quade, Markus Isele, Thomas Abel, Markus Ambrosys GmbH David Gilly Str 1 D-14469 Potsdam Germany 4Cast GmbH & Co KG David Gilly Str 1 D-14469 Potsdam Germany Univ Potsdam Inst Phys & Astron Karl Liebknecht Str 24-25 D-14476 Potsdam Germany

Recently, the term explainable AI came into discussion as an approach to produce models from artificial intelligence which allow interpretation. For a long time, symbolic regression has been used to produce explainable and mathematically tractable models. In this contribution, we extend previous work on symbolic regression methods to infer the optimal control of a dynamical system given one or several optimization criteria, or cost functions. In earlier publications, network control was achieved by automated machine learning control using genetic programming. Here, we focus on the subsequent path continuation analysis of the mathematical expressions which result from the machine learning model. In particular, we use AUTO to analyze the solution properties of the controlled oscillator system which served as our model. As a result, we show that there is a considerable advantage of explainable symbolic regression models over less accessible neural networks. In particular, the roadmap of future works may be to integrate such analyses into the optimization loop itself to filter out robust solutions by construction. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Explainable AI machine learning control Dynamical systems Synchronization control Genetic programming

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Turbulent flow manipulation using sliding mode and machine learning control: from the flow over a backward-facing step to a real-world car

Turbulent flow manipulation using sliding mode and machine l...

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作者： Camila Chovet Universite de Valenciennes et du Hainaut-Cambresis

学位级别：博士

The present work aims to pre-evaluate flow control parameters to reduce the drag in a real vehicle. Two different actuation mechanisms (Murata's micro-blower, and air-knives) are characterized and compared to define their advantages and limitations. Murata micro-blowers energized the boundary layer to directly perturb the vortex structures formed in the shear layer region. The air-knife has a rounded surface, adjacent to the slit exit, that could be considered as an active boat-tail (Coanda effect) for drag reduction. Different open-loop and closed-loop control strategies are examined, such as continuous blowing, periodic forcing, sliding mode control (SMC) and machine learning control (MLC). SMC is a robust closed-loop algorithm to track, reach and maintain a predefined set-point; this approach has on-line adaptivity in changing conditions. machine learning control is a model-free control that learns an effective control law that is judged and optimized with respect to a problem-specific cost/objective function. A hybrid between MLC and SMC may provide adaptive control exploiting the best non-linear actuation mechanisms. Finally, all these parameters are brought together and tested in real experimental applications representative of the mean wake and shear-layer structures related to control of real cars. For the backward-facing step, the goal is to experimentally reduce the recirculation zone. The flow is manipulated by a row of micro-blowers and sensed by pressure sensors. Initial measurements were carried out varying the periodic forcing. MLC is used to improve performance optimizing a control law with respect to a cost function. MLC is shown to outperform periodic forcing. For the Ahmed body, the goal is to reduce the aerodynamic drag of the square-back Ahmed body. The flow is manipulated by an air-knife placed on the top trailing edge and sensed by a force balance. Continuous blowing and periodic forcing are used as open-loop strategies. SMC and MLC algorithms

关键词： Turbulent flow active flow control micro-blower air-knife sliding mode control machine learning control

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Large language model-based interpretable machine learning control in building energy systems

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ENERGY AND BUILDINGS 2024年 313卷

作者： Zhang, Liang Chen, Zhelun Univ Arizona Tucson AZ 85721 USA Natl Renewable Energy Lab Golden CO USA Drexel Univ Philadelphia PA USA

The potential of machine learning control (MLC) in HVAC systems is hindered by its opaque nature and inference mechanisms, which is challenging for users and modelers to fully comprehend, ultimately leading to a lack of trust in MLC-based decision -making. To address this challenge, this paper investigates and explores Interpretable machine learning (IML), a branch of machine learning (ML) that enhances transparency and understanding of models and their inferences, to improve the credibility of MLC and its industrial application in HVAC systems. Specifically, we developed an innovative framework that combines the principles of Shapley values and the in -context learning feature of Large Language Models (LLMs). While the Shapley values are instrumental in dissecting the contributions of various features in ML models, LLM provides an in-depth understanding of the non -data -driven or rule -based elements in MLC;combining them, LLM further packages these insights into a coherent, human -understandable narrative. The paper presents a case study to demonstrate the feasibility of the developed IML framework for model predictive control -based precooling under demand response events in a virtual testbed. The results indicate that the developed framework generates and explains the control signals in accordance with the rule -based rationale.

关键词： Building control machine learning control Interpretable machine learning Shapley value Large language model

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Artificial Bee Colony Optimization Algorithm Incorporated With Fuzzy Theory for Real-Time machine learning control of Articulated Robotic Manipulators

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IEEE ACCESS 2020年 8卷 192481-192492页

作者： Huang, Hsu-Chih Chuang, Chun-Chia Natl Ilan Univ Dept Elect Engn Yilan 260 Taiwan

This paper presents a real-time machine learning control (MLC) of articulated robotic manipulators using artificial bee colony optimization (ABC) algorithm incorporated with fuzzy theory. The modified ABC with dynamic weight is used to optimize the fuzzy structure and fractional order. The fractional parameters, fuzzy membership functions and rule base are determined by means of the ABC computation. This ABC-fuzzy hybrid learning algorithm is applied to real-time MLC of robotic manipulators by including fractional order proportional-integral-derivative (FOPID) control strategy. The MLC's control gain parameters are online tuned via the ABC-fuzzy optimization. With the kinematics analysis of a six-degree-of-freedom (DOF) articulated arm via reverse coordinates approach, an ABC-fuzzy MLC is developed to achieve motion control. A real-time operating system (RTOS) on a microprocessor collaborates with the ABC-fuzzy MLC to meet critical timing constraint by considering the dynamics of actuators. Finally, the mechatronic design and experimental setup of a six-DOF articulated robotic manipulator are constructed. Experimental results and comparative works are provided to demonstrate the merit of the proposed methods. Compared with the conventional control schemes, the proposed ABC-fuzzy MLC has theoretical and practice significance in term of real-time capability, online parameter tuning, convergent behavior and hybrid MLC. The proposed MLC methodologies are applicable to designing real-time modern controllers in both industry and academia.

关键词： Artificial bee colony optimization fuzzy theory machine learning control robotic arm

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Stabilization of shear flows with gradient-enriched machine learning control

Stabilization of shear flows with gradient-enriched machine ...

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第十二届全国流体力学学术会议

作者： Cornejo Maceda Guy Y. 李逸清 Lusseyran Fran?ois Morzyński Marek Noack Bernd R. 哈尔滨工业大学(深圳) 机电工程与自动化学院 Laboratoire Interdisciplinaire des Sciences du Numérique Paris-Saclay UniversityCNRS Department of Virtual Engineering Poznań University of Technology

We propose a gradient-enriched machine learning control(gMLC) for fast model-free self-learning of multiple-input multiple-output feedback control directly in the plant. Gradientenriched MLC is demonstrated directly in numerical and experimental shear flows. We optimize feedback control laws for the stabilization of a flow past a cluster of three rotating cylinders—the fluidic pinball—and the open cavity flow experiment. The fluidic pinball is stabilized in increasingly richer control law spaces employing 9 sensors downstream and 3 rotating cylinders. As for the open cavity, two regimes are controlled in single-input single-output manner: a single-mode driven regime and a mode-switching regime. Key enablers are automated machine learning algorithms augmented with gradient search: explorative gradient method(EGM, Li. et al., 2022 J. Fluid Mech.) for the openloop parameter optimization and a gradient-enriched machine learning control(gMLC, Cornejo Maceda et al., 2021 J. Fluid Mech.) for the feedback optimization. For both plants, the need of feedback for control is demonstrated. Gradient-enriched MLC learns control laws significantly faster than previously employed genetic programming control in DNS and experiment.

关键词： machine learning control Flow control Fluidic Pinball Cavity flow Genetic programming

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Kinematics-informed neural network control on SO(3)☆

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AUTOMATICA 2025年 174卷

作者： Reis, Joel Silvestre, Carlos Univ Macau Fac Sci & Technol Taipa Macao Peoples R China Univ Lisbon Inst Syst & Robot Inst Super Tecn P-1049001 Lisbon Portugal

This paper presents an adaptive geometric control method for dynamic-model-free attitude tracking on the manifold of 3D rotations (SO(3)). Utilizing well-established definitions of attitude errors on SO(3), we develop a general control-affine linear error system. The input to this system is implicitly approximated by a kinematics-informed neural network (NN), which serves as the controller. The weights of this NN, designed to be inherently bounded, are adjusted online using a modified gradient- descent strategy that relies solely on system kinematics. We demonstrate the effectiveness and online learning capability of our proposed method through comprehensive simulation results, using a satellite attitude control system as an example. A comparative analysis is also provided to validate our approach. (c) 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

关键词： Attitude control systems Neural networks machine learning control Special orthogonal group

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machine learning controller for data rate management in science DMZ networks

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COMPUTER NETWORKS 2024年 242卷

作者： Vega, Christian Kfoury, Elie F. Gomez, Jose Pezoa, Jorge E. Figueroa, Miguel Crichigno, Jorge Univ Concepcion Dept Ingn Elect Concepcion Chile Univ South Carolina Coll Engn & Comp Columbia SC USA Univ Cesmag Fac Ingn Pasto Colombia Adv Ctr Elect & Elect Engn AC3E Valparaiso Chile

This article presents a machine learning controller (MLC) supported by a P4 switch for improving rate control in non -dedicated Science Demilitarized Zone (Science DMZ) cyberinfrastructures. The proposed scheme utilizes passive data plane measurements such as Round Trip Time (RTT), throughput, queuing delay, and active flow count to regulate campus network output and achieve a desired Data Transfer Node (DTN) target rate. We evaluated our solution through a testbed using a bare -metal data plane switch, legacy router, and emulated hosts. Results show that including a rate controller based on data plane programmable devices on a nondedicated Science DMZ cyberinfrastructure can effectively improve the completion time of scientific big data flows, while having a low impact on the campus network traffic and bottleneck link utilization. Specifically, the proposed controller achieved an average improvement of 21.72% in Flow Completion Time (FCT) compared to a trivial fixed-rate solution when the DTN uses BBR2 as a Congestion control Algorithm (CCA). The results highlight the potential of machine learning techniques in conjunction with data plane measurements for optimizing the performance of non -dedicated networks.

关键词： Science DMZ machine learning control Rate control Programmable data planes P4 language Passive measurement

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