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On the Design of Persistently Exciting Inputs for data-driven control of Linear and Nonlinear Systems

IEEE control SYSTEMS LETTERS 2023年 7卷 2629-2634页

作者： Alsalti, Mohammad Lopez, Victor G. Muller, Matthias A. Leibniz Univ Hannover Inst Automat Control D-30167 Hannover Germany

In the context of data-driven control, persistence of excitation (PE) of an input sequence is defined in terms of a rank condition on the Hankel matrix of the input data. For nonlinear systems, recent results employed rank conditions involving collected input and state/output data, for which no guidelines are available on how to satisfy them a priori. In this letter, we first show that a set of discrete impulses is guaranteed to be persistently exciting for any controllable LTI system. Based on this result, for certain classes of nonlinear systems, we guarantee persistence of excitation of sequences of basis functions a priori, by design of the physical input only.

关键词： data-driven control persistence of excitation identification for control

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A MIMO Self-Tuning data-driven control Application for Power Electronic Converters Robustness Operation 9

A MIMO Self-Tuning Data-Driven Control Application for Power...

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9th Southern Power Electronics Conference

作者： dos Santos, Lucas E. Huaman, Jesus F. Queiroz, Eliabe D. Dotta, Daniel Pomilio, Jose A. Univ Estadual Campinas Campinas Brazil Univ Campinas UNICAMP BR-13083852 Campinas SP Brazil

ISBN: (纸本)9798350351163;9798350351156

Islanded microgrids, characterized by limited energy generation capacity and low inertia due to renewable sources based on power electronic converters, face significant challenges. These challenges include significant frequency and voltage deviations that can lead to microgrid collapse. This work proposes an enhanced approach to the data-driven control algorithm using the traditional multivariable (MIMO) Virtual Reference Feedback Tuning (VRFT) technique applied to power electronic converter sources. This method focuses on optimizing proportional and integral control with a reduced set of measured data samples to follow the dynamic performance prescription defined by the designer. The effectiveness of the data-driven control feasibility is certified through control hardware-in-the-loop (CHIL) simulation.

关键词： data-driven control power electronic converter microgrids self-tuning VRFT

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data-driven control framework using fractional order singular optimal control and optimized metaheuristic algorithms

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COMPUTERS & ELECTRICAL ENGINEERING 2024年 120卷

作者： Dehnavi, Vahid Safari Shafiee, Masoud Amirkabir Univ Technol Dept Elect Engn Tehran Polytech Tehran Iran

As the demand for advanced healthcare systems increases with the aging population, this paper introduces a novel data-driven control framework for constrained systems. The framework integrates signal processing algorithms with the optimal control of fractional order singular systems. data collection was performed using a master-slave structure, while the classification process included preprocessing, window selection, feature extraction, and feature selection conducted through a genetic algorithm. We used machine learning algorithms, fuzzy wavelet neural networks using optimized metaheuristic algorithm, and convolutional neural network-long short-term memory (CNN-LSTM) for classification. We first decomposed both time-invariant and time-varying systems for the controller design to simplify the control process. This was followed by eliminating infinite modes, allowing for more efficient system control. We developed a novel linear method based on orthogonal functions to address the presence of both left and right fractional-order derivatives. The proposed framework's practicality was validated through its application in a rehabilitation system. Results indicated that electromyography (EMG) signals effectively classified movement states when combined with machine learning algorithms. In contrast, electroencephalogram (EEG) signals were better suited for classifying mental states. For movement classification using EEG signals, the fuzzy wavelet neural network and optimized CNNLSTM emerged as the most effective methods. Among the orthogonal functions, the Chebyshev polynomial delivered the best performance, further confirming the robustness of our approach.

关键词： data-driven control Optimal control Fractional order singular systems Optimized metaheuristic algorithm Optimized CNN-LSTM

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data-driven control of Unknown Linear Systems via Quantized Feedback 4

Data-driven Control of Unknown Linear Systems via Quantized ...

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4th Annual Conference on Learning for Dynamics and control (L4DC)

作者： Zhao, Feiran Li, Xingchen You, Keyou Tsinghua Univ Dept Automat Beijing Peoples R China Tsinghua Univ BNRist Beijing Peoples R China

control using quantized feedback is a fundamental approach to system synthesis with limited communication capacity. In this paper, we address the stabilization problem for unknown linear systems with logarithmically quantized feedback, via a direct data-driven control method. By leveraging a recently developed matrix S-lemma, we prove a sufficient and necessary condition for the existence of a common stabilizing controller for all possible dynamics consistent with data, in the form of a linear matrix inequality. Moreover, we formulate a semi-definite programming problem to solve the coarsest quantization density. By establishing its connections to unstable eigenvalues of the state matrix, we further prove a necessary rank condition on the data for quantized feedback stabilization. Finally, we validate our theoretical results by numerical examples.

关键词： Quantized control data-driven control linear systems linear matrix inequalities

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data-driven control of input saturated systems: a LMI-based approach

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IFAC-PapersOnLine 2024年第15期58卷 205-210页

作者： F. Porcari V. Breschi L. Zaccarian S. Formentin Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milano Italy Department of Electrical Engineering Eindhoven University of Technology Eindhoven The Netherlands LAAS-CNRS University of Toulouse Toulouse France and Dipartimento di Ingegneria Industriale Università di Trento Trento Italy

This paper addresses three complex control challenges related to input-saturated systems from a data-driven perspective. Unlike the traditional two-stage process involving system identification and model-based control, the proposed approach eliminates the need for an explicit model description. The method combines data-based closed-loop representations, Lyapunov theory, instrumental variables, and a generalized sector condition to formulate data-driven linear matrix inequalities (LMIs). These LMIs are applied to maximize the origin’s basin of attraction, minimize the closed-loop reachable set with bounded disturbances, and introduce a new data-driven ℓ 2 -gain minimization problem. Demonstrations on benchmark examples highlight the advantages and limitations of the proposed approach compared to an explicit identification of the system, emphasizing notable benefits in handling nonlinear dynamics.

关键词： data-driven control saturated systems linear matrix inequalities

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A data-driven control method for ground locomotion on sloped terrain of a hybrid aerial-ground robot

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Journal of Automation and Intelligence 2024年第4期3卷 219-229页

作者： Xinhang Xu Yizhuo Yang Muqing Cao Thien-Minh Nguyen Kun Cao Lihua Xie School of Electrical and Electronic Engineering Nanyang Technological UniversityNanyang Avenue639798Singapore

In this work,we present a data-driven solution for the attitude control of DoubleBee on *** is a novel hybrid aerial-ground robot with two rotors and two active *** by the physics modeling of the system,we add a channel-separated attention head to a deep ReLU neural network to predict disturbances from ground effects,motor torques and rotation axis *** proposed neural network is Lipschitz continuous,has fewer parameters and performs better for disturbance estimation than the baseline deep ReLU neural ***,we design a sliding mode controller using these predictions and establish its input-to-state stability and error *** show improvements of the proposed neural network in training speed and robustness over a baseline ReLU network,and a 40%reduction in tracking error compared to a baseline PID controller.

关键词： data-driven control Hybrid aerial-ground robot Adaptive control Machine learning Robotics Nonlinear control systems

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Reinforcement Learning-based data-driven control Design for Motion control Systems 36

Reinforcement Learning-based Data-driven Control Design for ...

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36th Chinese control and Decision Conference (CCDC)

作者： Deng, Zhengqi Huo, Xin Du, Qinlong Liu, Qingquan Harbin Inst Technol Control & Simulat Ctr Harbin 150080 Peoples R China

ISBN: (纸本)9798350387780;9798350387797

Motion control systems are widely used in many fields of industry. Conventional control schemes are highly dependent on the system model to be designed. The performance of design would be greatly reduced, when the system exists unknown disturbances or uncertainty. Therefore, some scholars pointed out that the dependency on the system models can be eliminated by data-driven design schemes. In this paper, the reinforcement learning-based methods are included, which appeal to attentions gradually. The disturbances rejection problem for motion control systems is studied based on reinforcement learning. Considering the continuity of state space and action space, a method based on deep reinforcement learning algorithm is proposed to reject the periodic disturbances. Proposed deep deterministic policy gradient (DDPG) and twin delayed deep deterministic policy gradient (TD3) based algorithms are compared in simulation. The simulation results show that the periodic disturbances of the motion control systems can be rejected effectively with the proposed reinforcement learning controller.

关键词： Motion control systems data-driven control Reinforcement learning (RL) Deep deterministic policy gradient (DDPG) Twin delayed deep deterministic policy gradient (TD3)

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A bias-variance perspective of data-driven control 20

A bias-variance perspective of data-driven control

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20th IFAC Symposium on System Identification (SYSID)

作者： Colin, Kevin Ju, Yue Bombois, Xavier Rojas, Cristian R. Hjalmarsson, Hakan KTH Royal Inst Technol Ctr Adv Bio Prod AdBIOPRO Stockholm Sweden KTH Royal Inst Technol Div Decis & Control Syst Stockholm Sweden Univ Lyon Ecole Cent Lyon UMR CNRS 5005 Lab Ampere Lyon France CNRS Paris France

data-driven control, the task of designing a controller based on process data, finds application in a wide range of disciplines and the topic has been intensively studied over more than half a century. The main purpose of this contribution is to elucidate on the commonalities between data-driven control and parameter estimation. In particular, we discuss the bias-variance trade-of, i.e. rather than aiming for the optimal controller one should aim for a constrained version, that may be characterized by tunable parameters, corresponding to hyperparameters in parameter estimation. As a result we shift attention from indirect vs direct data driven control by highlighting the important role played by (complete) minimal sufficient statistics. To keep technicalities at a minimum, still capturing the essential features of the problem, we consider the problem of minimizing the expected control cost for a quadratic open loop control problem applied to a finite impulse response system. In a Gaussian white noise setting, the maximum-likelihood parameter estimate constitutes a complete minimal sufficient statistic which allows us to focus on controllers that are functions of this model estimate without loss of statistical accuracy. We make a systematic study of three different controller structures and two different tuning techniques and illustrate their behaviours numerically. Copyright (c) 2024 The Authors.

关键词： data-driven control Bayes control Regularization and Kernel Methods

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Pseudo-spectral optimization and data-driven control of vehicle start process with dual-clutch transmission

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MECHANISM AND MACHINE THEORY 2022年 172卷

作者： Feng, Jihao Qin, Datong Liu, Yonggang Wang, Xin Lv, Hao Chongqing Univ State Key Lab Mech Transmiss Chongqing 400044 Peoples R China Chongqing Univ Coll Mech & Vehicle Engn Chongqing 400044 Peoples R China Chongqing Changan Autombile Co Ltd Chongqing 401133 Peoples R China

In this study, a dual-clutch transmission start control strategy based on pseudo-spectral optimization and data-driven control is proposed to respond to the time-varying start intentions, to reduce friction and jerk, and to improve the start quality. First, taking the jerk, start time, and friction work as the optimization indexes, the adaptive pseudo-spectral method is used to accurately determine the optimal clutch engagement target trajectory. Then, a real-time planning method for the target clutch engagement trajectory is proposed based on a dual deep gated recurrent unit network. Second, through partial least squares identification, a data-driven predictive control (DDPC) method based on the autoregressive with exogenous model is proposed. The model parameters are updated online by a multi-model filtering algorithm based on input and output data, allowing real-time adaptation to the system uncertainty and nonlinear characteristics. Simulations and experiments show that the proposed target clutch trajectory planning method can effectively respond to time-varying start intentions in real-time. In addition, the DDPC controller can adapt to the uncertainty and nonlinear characteristics of the system and, hence, improve the control accuracy.

关键词： Dual-clutch transmission Start control Pseudo-spectral optimization Trajectory planning data-driven control

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Security data-driven control for Nonlinear Systems Subject to Deception and False data Injection Attacks

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2022年第4期9卷 2910-2921页

作者： Yu, Wei Bu, Xuhui Hou, Zhongsheng Henan Polytech Univ Sch Elect Engn & Automat Jiaozuo 454003 Henan Peoples R China Qingdao Univ Sch Automat Qingdao 266071 Peoples R China

This paper studies the security data-driven control problem for a class of nonlinear systems with completely unknown dynamics under the deception attacks (DA) and false data injection attacks (FDIA) via faded channels. Firstly, the fading phenomenon is formulated by the Rice fading model, which obeys the Gaussian distribution with a known mathematical expectation and variance. DA could invert the direction of I/O data and FDIA may result in false injection data with zero-mean Gaussian white noise. Then, by employing the compact form dynamic linearization method, the security model free adaptive controller (MFAC) is designed, which does not involve the model or structure information of the system. Next, the system stability is analyzed, and the compensation scheme with an increasing gain is proposed to counteract the adverse effect brought by faded channels. A numerical simulation and a load frequency control (LFC) example for multi-area power systems illustrate the validity of the proposed schemes.

关键词： Fading channels Security Power system stability Numerical stability Mathematical models Analytical models Wireless sensor networks data-driven control security control deception attacks false data injection attacks fading channels

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