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learning-based control for deployment and retrieval of a spinning tethered satellite formation system

ACTA ASTRONAUTICA 2024年 225卷 788-800页

作者： Luo, Caoqun Chen, Ti Wen, Hao Jin, Dongping Nanjing Univ Aeronaut & Astronaut State Key Lab Mech & Control Mech Struct 29 Yudao St Nanjing 210016 Jiangsu Peoples R China

This paper investigates the nonlinear dynamics and control of the deployment and retrieval for a spinning tethered satellite formation system via artificial intelligent method. A dynamic model of the spinning tethered formation system is developed to describe the attitude motions of the system, involving the relative rotations of the tethers to the central main satellite. Considering the system with symmetric and asymmetric configurations, a learning-based control strategy with low time cost is proposed to achieve the stable deployment and retrieval of tethers. In the strategy, a nonlinear model predictive control law accounting for the control constraints and nonlinear dynamics is developed to achieve the control goal. based on a deep learning method, a dataset including control input and state output obtained offline is trained to form deep neural networks. An online feedback control of the system can be achieved by conducting real-time mapping from the system state to the control input using the neural networks. Finally, numerical simulations for deployment and retrieval of the system with different configurations are presented to demonstrate the computational efficiency and to validate the effectiveness of the control strategy.

关键词： Tether Spin learning-based control Deployment Retrieval

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learning-based control for Soft Robot-Environment Interaction with Force/Position Tracking Capability

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SOFT ROBOTICS 2024年第5期11卷 767-778页

作者： Tang, Zhiqiang Xin, Wenci Wang, Peiyi Laschi, Cecilia Natl Univ Singapore Dept Mech Engn Singapore Singapore Beijing Jiaotong Univ Robot Res Ctr Beijing Peoples R China Natl Univ Singapore Dept Mech Engn EA-03-059 Engn Dr 1 Singapore 117575 Singapore

Soft robotics promises to achieve safe and efficient interactions with the environment by exploiting its inherent compliance and designing control strategies. However, effective control for the soft robot-environment interaction has been a challenging task. The challenges arise from the nonlinearity and complexity of soft robot dynamics, especially in situations where the environment is unknown and uncertainties exist, making it difficult to establish analytical models. In this study, we propose a learning-based optimal control approach as an attempt to address these challenges, which is an optimized combination of a feedforward controller based on probabilistic model predictive control and a feedback controller based on nonparametric learning methods. The approach is purely data-driven, without prior knowledge of soft robot dynamics and environment structures, and can be easily updated online to adapt to unknown environments. A theoretical analysis of the approach is provided to ensure its stability and convergence. The proposed approach enabled a soft robotic manipulator to track target positions and forces when interacting with a manikin in different cases. Moreover, comparisons with other data-driven control methods show a better performance of our approach. Overall, this work provides a viable learning-based control approach for soft robot-environment interactions with force/position tracking capability.

关键词： learning-based control soft robot-environment interaction force/position tracking

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Deep Reinforcement learning-based control of Energy Storage for Interarea Oscillation Damping

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2025年第5期21卷 3736-3745页

作者： Hasan, Abu Shouaib Fan, Rui Wu, Di Univ Denver Denver CO 80208 USA Pacific Northwest Natl Lab Richland 99352 WA USA

With the increasing electricity consumption and lack of transmission investment, today's power systems are operated much closer to their limits, raising concerns of inter-area oscillations that deteriorate the system stability. This article presents a novel energy storage placement and control approach for enhanced damping of interarea oscillations. Combining the residual analysis and dominant mode analysis, we are able to identify the advantageous locations for placing energy storage that achieve improved damping performance. To overcome the challenges, such as fixed control parameters and insufficient damping, we propose to use a deep reinforcement learning-based approach for energy storage control. A state-of-the-art guided surrogate-gradient-based evolutionary strategy is used to train a learning agent in a robust, efficient, and reproducible manner. Parallel computing is also adopted to speed up the training process. The proposed strategy has been tested on both medium and large-scale systems. The proposed methods have demonstrated their effectiveness in mitigating various interarea oscillations within a timeframe of 20 s, thereby averting system collapse and enhancing power grid stability effectively.

关键词： Energy storage Oscillators Damping Power system stability Generators Training Heuristic algorithms Contingency management Power system dynamics Stability criteria interarea oscillation learning-based control optimal placement power system stability

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Active learning-based control for Resiliency of Uncertain Systems Under DoS Attacks

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IEEE control SYSTEMS LETTERS 2024年 8卷 3297-3302页

作者： Chakraborty, Sayan Gao, Weinan Vamvoudakis, Kyriakos G. Jiang, Zhong-Ping NYU Brooklyn NY 11201 USA Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang Peoples R China Georgia Inst Technol Daniel Guggenheim Sch Aerosp Engn Atlanta GA 30332 USA

In this letter, we present an active learning-based control method for discrete-time linear systems with unknown parameters under denial-of-service (DoS) attacks. For any DoS duration parameter, using switching systems theory and adaptive dynamic programming, an active learning-based control technique is developed. A critical DoS average dwell-time is learned from online input-state data, guaranteeing stability of the equilibrium point of the closed-loop system in the presence of DoS attacks with average dwell-time greater than or equal to the critical DoS average dwell-time. The effectiveness of the proposed methodology is illustrated via a numerical example.

关键词： Denial-of-service attack Symmetric matrices Resilience Regulation Eigenvalues and eigenfunctions Convergence Vectors Trajectory Standards Regulators learning-based control resiliency DoS attacks output regulation

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A Computationally Efficient learning-based control of a Three-Phase AC/DC Converter for DC Microgrids

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ENERGIES 2025年第9期18卷 2383-2383页

作者： Li, Ran Feng, Wendong Qie, Tianhao Liu, Yulin Fernando, Tyrone Iu, Herbert HoChing Zhang, Xinan Univ Western Australia Dept Elect Elect & Comp Engn Perth WA 6009 Australia

This paper presents a novel learning-based control algorithm for three-phase AC/DC converters, which are key components in DC microgrids, for reliable power conversion. In contrast with conventional model-based nonlinear controllers that rely on detailed system modeling and manual gain tuning, the proposed method is model-free and eliminates such dependencies. By integrating a recurrent equilibrium network (REN), the controller achieves an enhanced dynamic response and robust steady-state performance, while maintaining a low computational complexity. Moreover, its closed-loop stability can be rigorously verified based on contraction theory and incremental quadratic constraints. To facilitate practical implementation, a design guideline is provided. Experimental results confirm that the proposed method outperforms conventional PI and model predictive controllers in terms of response speed, harmonic suppression, and robustness under parameter variations. Additionally, the algorithm is lightweight enough for real-time execution on embedded platforms, such as a TI DSP.

关键词： three-phase AC/DC converter learning-based control low computational complexity stability

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Safe learning-based control for an aerial robot with manipulator arms 8

Safe learning-based control for an aerial robot with manipul...

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8th IFAC Workshop on Lagrangian and Hamiltonian Methods for Nonlinear control (LHMNC)

作者： Yago Nieto, Omayra Colombo, Leonardo J. Univ Politecn Madrid UPM Jose Gutierrez Abascal 2 Madrid 28006 Spain Ctr Automat & Robot CSIC UPM Ctra M300 Campo RealKm 0200 Madrid 28500 Spain

Aerial manipulation aims at combining the versatility and the agility of some aerial platforms as multirotor unmanned aerial vehicles (UAVs) with the manipulation capabilities of robotic arms. Trajectory tracking control of these vehicles is a substantial aspect for an increasing range of application domains. However, external disturbances and parts of the internal dynamics are often unknown or very time-consuming to model. To overcome this issue, we present a tracking control law using an learning-based oracle based on Gaussian process models. The presented approach guarantees a bounded tracking error with high probability where the bound is explicitly given. A numerical example highlights the effectiveness of the proposed control law. Copyright (C) 2024 The Authors.

关键词： learning-based control Gaussian process Aerial robots Multi-body systems

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In Vivo learning-based control of Microbial Populations Density in Bioreactors 6

In Vivo Learning-Based Control of Microbial Populations Dens...

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6th Annual learning for Dynamics and control Conference

作者： Brancato, Sara Maria Salzano, Davide De Lellis, Francesco Fiore, Davide Russo, Giovanni di Bernardo, Mario Univ Naples Federico II Naples Italy Scuola Super Merid Naples Italy Univ Salerno Salerno Italy

A key problem in using microorganisms as bio-factories is achieving and maintaining cellular communities at the desired density and composition to efficiently convert their biomass into useful compounds. Bioreactors are promising technological platforms for the real-time, scalable control of cellular density. In this work, we developed a learning-based strategy to expand the range of available control algorithms capable of regulating the density of a single bacterial population in bioreactors. Specifically, we used a sim-to-real paradigm, where a simple mathematical model, calibrated using a single experiment, was adopted to generate synthetic data for training the controller. The resulting policy was then exhaustively tested in vivo using a low-cost bioreactor known as ***, assessing performance and robustness. Additionally, we compared the performance with more traditional controllers (namely, a PI and an MPC), confirming that the learning-based controller exhibits similar performance in vivo. Our work demonstrates the viability of learning-based strategies for controlling cellular density in bioreactors, making a step forward toward their use in controlling the composition of microbial consortia.

关键词： control Applications learning-based control In Vivo Validation Sim-To-Real Synthetic Biology

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Safe learning-based control for an aerial robot with manipulator arms

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IFAC-PapersOnLine 2024年第6期58卷 36-41页

作者： Omayra Yago Nieto Leonardo J. Colombo O.Yago Nieto is with Universidad Politécnica de Madrid (UPM) José Gutiérrez Abascal 2 28006 Madrid Spain L.Colombo is with Centre for Automation and Robotics (CSIC-UPM) Ctra. M300 Campo Real Km 0200 Arganda del Rey - 28500 Madrid Spain

关键词： learning-based control Gaussian process Aerial robots Multi-body systems

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Contraction theory for nonlinear stability analysis and learning-based control: A tutorial overview

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ANNUAL REVIEWS IN control 2021年 52卷 135-169页

作者： Tsukamoto, Hiroyasu Chung, Soon-Jo Slotine, Jean-Jaques E. CALTECH Div Engn & Appl Sci Pasadena CA 91125 USA CALTECH NASA Jet Prop Lab Pasadena CA USA MIT Nonlinear Syst Lab 77 Massachusetts Ave Cambridge MA 02139 USA

Contraction theory is an analytical tool to study differential dynamics of a non-autonomous (i.e., time-varying) nonlinear system under a contraction metric defined with a uniformly positive definite matrix, the existence of which results in a necessary and sufficient characterization of incremental exponential stability of multiple solution trajectories with respect to each other. By using a squared differential length as a Lyapunov-like function, its nonlinear stability analysis boils down to finding a suitable contraction metric that satisfies a stability condition expressed as a linear matrix inequality, indicating that many parallels can be drawn between well-known linear systems theory and contraction theory for nonlinear systems. Furthermore, contraction theory takes advantage of a superior robustness property of exponential stability used in conjunction with the comparison lemma. This yields much-needed safety and stability guarantees for neural network-based control and estimation schemes, without resorting to a more involved method of using uniform asymptotic stability for input-to-state stability. Such distinctive features permit systematic construction of a contraction metric via convex optimization, thereby obtaining an explicit exponential bound on the distance between a time-varying target trajectory and solution trajectories perturbed externally due to disturbances and learning errors. The objective of this paper is therefore to present a tutorial overview of contraction theory and its advantages in nonlinear stability analysis of deterministic and stochastic systems, with an emphasis on deriving formal robustness and stability guarantees for various learning-based and data-driven automatic control methods. In particular, we provide a detailed review of techniques for finding contraction metrics and associated control and estimation laws using deep neural networks.

关键词： Contraction theory learning-based control Data-driven control Nonlinear stability Robust control and estimation Optimal control and estimation Adaptive control

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Online End-to-End learning-based Predictive control for Microgrid Energy Management

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IEEE TRANSACTIONS ON control SYSTEMS TECHNOLOGY 2025年第2期33卷 463-478页

作者： Casagrande, Vittorio Ferianc, Martin Rodrigues, Miguel R. D. Boem, Francesca UCL Dept Elect & Elect Engn London WC1E 7JE England Helsing London W1T 3BL England

This article proposes an innovative Online learning (OL) algorithm designed for efficient microgrid energy management, integrating Recurrent Neural Networks (RNNs), and Model Predictive control (MPC) in an End-to-End (E2E) learning-based control architecture. The algorithm leverages the RNN capabilities to predict uncertain and possibly evolving profiles of electricity price, load demand, and renewable generation. These are then exploited in an integrated MPC optimization problem to minimize the overall microgrid electricity consumption cost while guaranteeing operation constraints. The proposed methodology incorporates a specifically designed online version of the Stochastic Weight Averaging (O-SWA) and Experience Replay (ER) methods to enhance OL capabilities, ensuring more robust and adaptive learning in real-time scenarios. In addition, to address the challenge of model uncertainty, a task-based loss approach is proposed by integrating the MPC optimization as a differentiable optimization layer within the Neural Network (NN), allowing the OL architecture to jointly optimize prediction and control performance. The performance of the proposed methodology is evaluated through extensive simulation results, showcasing its Transfer learning (TL) capabilities across different microgrid sites, which are crucial for deployment in real microgrids. We finally show that our OL algorithm can be used to estimate the prediction uncertainty of the unknown profiles.

关键词： Microgrids Energy management Uncertainty Renewable energy sources Prediction algorithms Real-time systems Predictive models Predictive control Electricity Adaptation models End-to-End (E2E) learning Energy Management System (EMS) learning-based control microgrid Model Predictive control (MPC) online learning (OL)

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