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22nd World Congress of the International Federation of Automatic control (IFAC)

作者： D'Amico, William La Bella, Alessio Dercole, Fabio Farina, Marcello Politecn Milan Dipartimento Elettron Informaz & Bioingn Via Ponzio 34-5 Milan Italy

ISBN: (纸本)9781713872344

This paper addresses the design of controllers for systems modelled as recurrent neural networks (RNNs). A novel data-based procedure for the design of RNN-based regulators is proposed, guaranteeing closed-loop stability properties and desired performances, conferred by virtual reference feedback tuning. The approach is tested on a realistic nonlinear system. Copyright (c) 2023 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Recurrent neural networks nonlinear system stability data-based control

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data-based control design for nonlinear systems with recurrent neural network-based controllers

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IFAC-PapersOnLine 2023年第2期56卷 6235-6240页

作者： William D'Amico Alessio La Bella Fabio Dercole Marcello Farina Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Via Ponzio 34/5 Milano Italy

关键词： Recurrent neural networks nonlinear system stability data-based control

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data-based control of Partially-Observed Robotic Systems

Data-based Control of Partially-Observed Robotic Systems

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Wang, Ran Goyal, Raman Chakravorty, Suman Skelton, Robert E. Texas A&M Univ Dept Aerosp Engn College Stn TX 77843 USA

ISBN: (纸本)9781728190778

This paper presents a data-based approach to control robotic systems with partially-observed feedback. First, an open-loop optimization problem is solved to generate the nominal trajectory and then a linear time-varying Autoregressive-Moving-Average (ARMA) model of the system is calculated along the trajectory from the output measurement data. The system is then described in information state, which contains input-output information of the past few steps. Finally, a feedback gain which is calculated by solving a specific LQG problem along the nominal trajectory. The separate design of the open-loop and the closed-loop problem is used following the Decoupled data-based control (D2C) approach. Simulation results are also shown for complex models with fluid-structure interaction in the presence of both process and measurement noise.

关键词： Robotic path planning learning and control data-based control partial-state feedback

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Towards data-driven and data-based control of wave energy systems: Classification, overview, and critical assessment

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RENEWABLE & SUSTAINABLE ENERGY REVIEWS 2023年 188卷

作者： Pasta, Edoardo Faedo, Nicolas Mattiazzo, Giuliana Ringwood, John V. Politecn Torino Marine Offshore Renewable Energy Lab Dept Mech & Aerosp Engn DIMEAS Corso Duca Abruzzi 24 I-10129 Turin TO Italy Maynooth Univ Dept Elect Engn Ctr Ocean Energy Res COER Co Kildare W23 F2H6 Ireland

Currently, a significant effort in the world research panorama is focused on finding efficient solutions to a carbon-free energy supply, wave energy being one of the most promising sources of untapped renewable energy. However, wave energy is not currently economic, though control technology has been shown to significantly increase the energy capture capabilities. Usually, the synthesis of a wave energy control strategy requires the adoption of control-oriented models, which are prone to error, particularly arising from unmodelled hydrodynamics, given the complexity of the hydrodynamic interactions between the device and the ocean. In this context, data-driven and data-based control strategies provide a potential solution to some of these issues, using real-time data to gather information about the system dynamics and performance. Thus motivated, this study provides a detailed analysis of different approaches to the exploitation of data in the design of control philosophies for wave energy systems, establishing clear definitions of data-driven and data -based control in this field, together with a classification highlighting the various roles of data in the control synthesis process. In particular, we investigate intrinsic opportunities and limitations behind the use of data in the process of control synthesis, providing a comprehensive review together with critical considerations aimed at directly contributing towards the development of efficient data-driven and data-based control systems for wave energy devices.

关键词： Adaptive control data-based control data-based modelling data-driven control Energy-maximising control Learning-based control Optimal control Wave energy

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data-based Predictive control for Power Congestion Management in Subtransmission Grids Under Uncertainty

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IEEE TRANSACTIONS ON control SYSTEMS TECHNOLOGY 2023年第5期31卷 2146-2158页

作者： Dkhili, Nouha Olaru, Sorin Iovine, Alessio Giraud, Guillaume Maeght, Jean Panciatici, Patrick Ruiz, Manuel Paris Saclay Univ Ctr Natl Rech Sci CNRS Lab Signals & Syst L2S Cent Supelec F-91192 Gif sur yvette France Reseau Transport Electr RTE F-92073 Paris France

The energy transition of power grids has spawned a large spectrum of new technical challenges at the design, deployment, and operation levels. From a control standpoint, the integration of renewable-energy-based power generation sources into the power grid translates into emerging uncertainties which compromise the system's safety, stability, and performance. This article proposes a model-based predictive controller (MPC) that incorporates the stochastic nature of these sources into its feedback decision-making policy. The overarching objective is to balance upholding operational constraints of power lines with smart power generation curtailment and energy storage strategies. The proposed method introduces a novel characterization of disturbance trajectory scenarios, and their incorporation into the optimization problem is detailed leading to a robust congestion management strategy. Simulation results are discussed with respect to a baseline of a trend-based disturbance estimation.

关键词： Congestion management convex optimization data-based control model-based predictive control power sys-tems relaxation robustness smart grids

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data-based Optimization control for Learning Systems

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2023年第7期70卷 2560-2564页

作者： Wang, Chenchao Meng, Deyuan Beihang Univ Res Div 7 Beijing 100191 Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China

This brief is devoted to establishing a novel data-based optimization control framework of achieving the perfect tracking performances for learning systems in the absence of any model information. A modified Willems' fundamental lemma is developed without the assumptions on the controllability and the input's persistency of excitation, under which a data-based iterative learning control (ILC) framework is further presented. It is shown that the proposed data-based ILC can be employed to realize the perfect tracking tasks without any explicit model knowledge. A simulation example is provided to demonstrate the effectiveness of the proposed data-based ILC strategy.

关键词： data-based control iterative learning control learning system Willems' fundamental lemma

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data-based Optimal control of Multiagent System A Reinforcement Learning Design Approach

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IEEE TRANSACTIONS ON CYBERNETICS 2019年第12期49卷 4441-4449页

作者： Zhang, Jilie Wang, Zhanshan Zhang, Hongwei Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 611756 Sichuan Peoples R China Northeastern Univ Sch Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China Southwest Jiaotong Univ Sch Elect Engn Key Lab Magnet Suspens Technol & Maglev Vehicle Minist Educ Chengdu 611756 Sichuan Peoples R China

This paper studies an optimal consensus tracking problem of heterogeneous linear multiagent systems. By introducing tracking error dynamics, the optimal tracking problem is reformulated as finding a Nash-equilibrium solution to multiplayer games, which can be done by solving associated coupled Hamilton-Jacobi equations. A data-based error estimator is designed to obtain the data-based control for the multiagent systems. Using the quadratic functional to approximate every agent's value function, we can obtain the optimal cooperative control by the input-output (I/O) Q-learning algorithm with a value iteration technique in the least-square sense. The control law solves the optimal consensus problem for multiagent systems with measured I/O information, and does not rely on the model of multiagent systems. A numerical example is provided to illustrate the effectiveness of the proposed algorithm.

关键词： Consensus data-based control optimal cooperative control reinforcement learning

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data-based Predictive control for Networked Nonlinear Systems with Packet Dropout and Measurement Noise

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Journal of Systems Science & Complexity 2017年第5期30卷 1072-1083页

作者： PANG Zhonghua LIU Guoping ZHOU Donghua SUN Dehui Key Laboratory of Fieldbus Technology and Automation of Beijing North China University of Technology School of Engineering University of South Wales CTGT Center Harbin Institute of Technology College of Electrical Engineering and Automation Shandong University of Science and Technology Department of Automation Tsinghua University

In this paper, the data-based control problem is investigated for a class of networked nonlinear systems with measurement noise as well as packet dropouts in the feedback and forward channels. The measurement noise and the number of consecutive packet dropouts in both channels are assumed to be random but bounded. A data-based networked predictive control method is proposed, in which a sequence of control increment predictions are calculated in the controller based on the measured output error, and based on the control increment predictions received by the actuator, a proper control action is obtained and applied to the plant according to the real-time number of consecutive packet dropouts at each sampling instant. Then the stability analysis is performed for the networked closedloop system. Finally, the effectiveness of the proposed method is illustrated by a numerical example.

关键词： data-based control measurement noise networked control systems （NCSs） packet dropout predictive control

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data-based Optimal Switching and control With Admissibility Guaranteed <inline-formula><tex-math notation="LaTeX">$Q$</tex-math></inline-formula>-Learning

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2025年第4期36卷 5963-5973页

作者： Xiang, Zhengrong Li, Pingchuan Zou, Wencheng Ahn, Choon Ki Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Peoples R China Korea Univ Sch Elect Engn Seoul 136701 South Korea

This article addresses the data-based optimal switching and control codesign for discrete-time nonlinear switched systems via a two-stage approximate dynamic programming (ADP) algorithm. Through offline policy improvement and policy evaluation, the proposed algorithm iteratively determines the optimal hybrid control policy using system input/output data. Moreover, a strict proof of the convergence is given for the two-stage ADP algorithm. Admissibility, an essential property of the hybrid control policy must be ensured for practical application. To this end, the properties of the hybrid control policies are analyzed and an admissibility criterion is obtained. To realize the proposed Q -learning algorithm, an actor-critic neural network (NN) structure that employs multiple NNs to approximate the Q -functions and control policies for different subsystems is adopted. By applying the proposed admissibility criterion, the obtained hybrid control policy is guaranteed to be admissible. Finally, two numerical simulations verify the effectiveness of the proposed algorithm.

关键词： control systems Switched systems Switches Heuristic algorithms Q-learning Mathematical models Performance analysis Approximate dynamic programming (ADP) data-based control neural networks (NNs) optimal control Q -learning switched system value iteration (VI)

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data-driven control of echo state-based recurrent neural networks with robust stability guarantees

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SYSTEMS & control LETTERS 2025年 195卷

作者： D'Amico, William La Bella, Alessio Farina, Marcello Politecn Milan Dipartimento Elettron Informaz & Bioingn Via Ponzio 34-5 I-20133 Milan Italy

In this work we propose a new data-based approach for robust controller design for a rather general class of recurrent neural networks affected by bounded measurement noise. We first identify the model set compatible with available data in a selected model class via set membership (SM). Then, incremental input-to-state stability and desired performances for the closed loop system are enforced robustly to all models in the identified model set via a linear matrix inequality (LMI) optimization problem. Numerical results show the effectiveness of the comprehensive method.

关键词： Recurrent neural networks Linear matrix inequalities data-based control

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