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12th International conference on Smart Grid (ICSmartGrid)

作者： Zhang, Zhongtian Khazaei, Javad Blum, Rick S. Lehigh Univ Elect & Comp Engn Bethlehem PA 18015 USA

ISBN: (纸本)9798350361612;9798350361629

This paper introduces a novel statistical learning method using adaptive regulated sparsity promotion for data-driven modeling and control of solar photovoltaic (PV) generation in smart grids. Unlike traditional data-driven modeling approaches that may encounter computational challenges with an expanding pool of candidate functions, we propose an innovative algorithm called adaptive regulated sparse regression (ARSR). The proposed ARSR dynamically adjusts the hyperparameter weights of candidate functions to effectively capture the dynamics of PV systems. Leveraging this algorithm, we derive open-loop and closed-loop models of single-stage PV systems from measurements, facilitating a data-driven control design for PVs in smart grid.

关键词： Photovoltaic (PV) systems Single-stage PV Closed-Loop data-driven Modeling Adaptive Regulated Sparse Regression

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Composite Multi-Vector Model Predictive control for Permanent Magnet Synchronous Motor 12

Composite Multi-Vector Model Predictive Control for Permanen...

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ieee 12th data driven control and learning systems conference (DDCLS)

作者： Gao, Lin Pan, Tianhong Anhui Univ Sch Elect Engn & Automat Hefei 230601 Peoples R China

ISBN: (纸本)9798350321050

Model Predictive control (MPC) has been widely used in the permanent magnet synchronous motor. However, in the finite control set MPC, only one voltage vector is applied, which leads to high current harmonics and torque fluctuations. Meanwhile, three-vector MPC inevitably increases the switching frequency of inverter. In this article, a multi-vector switching control approach is established. Based on the location information of the created reference voltage vector, the relevant control technique is implemented. The proposed control method with single-vector, two-vector and three-vector composite modes of action is designed to achieve low switching frequency with excellent steady-state performance. The proposed method's effectiveness is confirmed by the experimental results.

关键词： Model predictive control Multi-vector Current harmonics

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Dynamic Optimization of Transportation Networks Using Big data-driven Reinforcement learning

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ieee TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2025年

作者： Wang, Xin Rani, Shalli Cao, Xia Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Chitkara Univ Inst Engn & Technol Rajpura 140401 Punjab India China Med Univ Shengjing Hosp Dept Data Ctr Shenyang 110004 Peoples R China Northeastern Univ Sch Comp Sci & Engn Shenyang 110169 Peoples R China

The dynamic optimization of large-scale transportation networks presents significant challenges due to their complexity, stochasticity, and the need for real-time decision-making. In conventional methods, there is often a failure to employ fully the resources of big data present in cities, thus not being able to respond appropriately to fluctuations in traffic conditions. This paper introduces a novel enhanced big data-driven reinforcement learning (EBD-RL) algorithm for dynamic optimization of transportation networks, addressing these challenges by leveraging advanced machine learning techniques and heterogeneous data sources. We propose a hierarchical control framework that decomposes the global optimization problem into manageable sub-problems while maintaining network-wide coordination. The EBD-RL algorithm incorporates prioritized experience replay and adaptive exploration strategies to improve learning efficiency and stability in high-dimensional state spaces. Experiments on a realistic urban network show that our method is superior to six state-of-the-art methods. Results show that EBD-RL reduces total travel time by up to 30% compared to the best baseline under various traffic demands and connected autonomous vehicle penetration rates. Furthermore, the algorithm exhibits enhanced resilience to traffic incidents, achieving up to 33% faster network recovery time in severe disruption scenarios. These findings highlight the potential of big data-driven reinforcement learning approaches to significantly improve the efficiency, adaptability, and resilience of modern urban transportation systems.

关键词： Transportation Optimization Vehicle dynamics Big data Reinforcement learning Real-time systems Heuristic algorithms Roads Traffic control Streams Dynamic optimization transportation networks big data reinforcement learning

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Predictive finite-time ADRC based longitudinal control for hypersonic aircraft with parametric uncertainties and unmodeled dynamics 12

Predictive finite-time ADRC based longitudinal control for h...

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ieee 12th data driven control and learning systems conference (DDCLS)

作者： Yang, Jinwei China West Normal Univ Sch Elect Informat Engn Nanchong 637009 Peoples R China

ISBN: (纸本)9798350321050

It is a significant issue to achieve the fast maneuverability and the strong robustness of hypersonic aircraft. Based on the thoughts of finite-time control and active disturbance rejection control (ADRC), this paper proposes a finite-time ADRC for controlling the longitudinal dynamics of hypersonic aircraft. The proposed design consists of three parts: finite-time feedback, finite-time estimation and estimating predictive modules. To avoid discontinuously changing of control input, the presented finite-time design is in a continuous form. Moreover, to overcome the delay phenomenon of estimation in the conventional ADRC, the predictive values of total disturbance and angular velocity are calculated based on Taylor expansion. The simulations for nonlinear uncertainties validate the effectiveness of the proposed method.

关键词： hypersonic aircraft finite-time control active disturbance rejection control

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RBFNN-Based Event-Triggered data-driven Formation control for a Connected Heterogeneous Vehicle Platoon 13

RBFNN-Based Event-Triggered Data-Driven Formation Control fo...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Zhu, Panpan Jin, Shangtai Yin, Chenkun School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9798350361674

This paper presents a novel distributed data-driven control scheme for the longitudinal formation control of a connected heterogeneous vehicle (CHV) platoon. Initially, an event-triggered mechanism is devised to alleviate the computational burden on the distributed controller. Subsequently, the future pseudo-gradient (PG) vector is predicted using the radial basis function neural network (RBFNN) method, and a data-driven control scheme is developed for the coordinated formation control of the CHV platoon. Ultimately, the simulation results demonstrate that the proposed distributed RBFNN-based event-triggered data-driven control (RBFNN-ETDDC) approach effectively achieves the objective of maintaining low-spacing error formation control for the CHV platoon. © 2024 ieee.

关键词： Radial basis function networks

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Reinforcement learning based data-driven Optimal control Strategy for systems with Disturbance 12

Reinforcement Learning based Data-driven Optimal Control Str...

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ieee 12th data driven control and learning systems conference (DDCLS)

作者： Fan, Zhong-Xin Li, Shihua Liu, Rongjie Southeast Univ Sch Automat Nanjing 210096 Peoples R China Minist Educ Key Lab Measurement & Control Complex Syst Engn Nanjing 210096 Peoples R China Florida State Univ Dept Stat Tallahassee FL 32306 USA

ISBN: (纸本)9798350321050

This paper proposes a partially model-free optimal control strategy for a class of continuous-time systems in a datadriven way. Although a series of optimal control have achieving superior performance, the following challenges still exist: (i) The controller designed based on the nominal system is difficult to cope with sudden disturbances. (ii) Feedback control is highly dependent on system dynamics and generally requires full state information. A novel composite control method combining output feedback reinforcement learning and input-output disturbance observer for these two challenges is concluded in this paper. Firstly, an output feedback policy iteration (PI) algorithm is given to acquire the feedback gain iteratively. Simultaneously, the observer continuously provides estimates of the disturbance. System dynamic information and states information are not needed to be known in advance in our approach, thus offering a higher degree of robustness and practical implementation prospects. Finally, an example is given to show the effectiveness of the proposed controller.

关键词： Reinforcement learning data-driven input-output data disturbance observer adaptive dynamic programming output feedback

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The Q-Fractionalism Reasoning learning Method

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ieee TRANSACTIONS ON NEURAL NETWORKS AND learning systems 2025年第1期36卷 1568-1582页

作者： Mazandarani, Mehran Jianfei, Pan Shenzhen Univ Dept Mechatron & Control Engn Shenzhen 518060 Peoples R China

As the title suggests, in this work, a modern machine learning method called the Q-fractionalism reasoning is introduced. The proposed method is founded upon a synergy of the Q-learning and fractional fuzzy inference systems (FFISs). Unlike other approaches, the Q-fractionalism reasoning not only incorporates the knowledge base to understand how to perform but also explores a reasoning mechanism from the fractional order to justify what it has performed. This method suggests that the agent choose actions aimed at the characterization of reasoning. In fact, the agent deals with states termed as primary and secondary fuzzy states. The primary fuzzy states are unobservable and uncertain, for which the agent chooses actions. However, the projection of primary fuzzy states onto the knowledge base results in secondary fuzzy states, which are observable by the agent, allowing it to detect primary fuzzy states with degrees of detectability. With a practical experiment implemented on a linear switched reluctance motor (LSRM), the results demonstrate that the application of the Q-fractionalism reasoning in the real-time position control of the LSRM leads to a remarkable improvement of about 70% in the accuracy of the control objective compared with a typical fuzzy inference system (FIS) under the same setting.

关键词： Artificial intelligence computational intelligence data-driven control systems fuzzy systems reinforcement learning

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Dual Timescales Voltages Regulation in Distribution systems Using data-driven and Physics-based Optimization

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ieee TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第2期20卷 1259-1271页

作者： Zhang, Jian Cui, Mingjian He, Yigang Hefei Univ Technol Sch Elect & Automat Engn Hefei 230009 Peoples R China Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Wuhan Univ Sch Elect Engn & Automat Wuhan 430072 Peoples R China

A large number of electric vehicles (EVs), distributed solar and/or wind turbine generators (WTGs) connected to distribution systems lead to frequent and sharp voltages fluctuations. The action rates of conventional adjustable devices and smart inverters are very different. In this context, a novel dual-timescale voltage control scheme is proposed by organically combining data-driven with physics-based optimization. On fast timescale, a quadratic programming (QP) for balanced and unbalanced distribution systems is developed based on branch flow equations. The optimal reactive power of renewable distributed generators (DGs) and static VAR compensators (SVCs) is configured on several minutes or seconds. Whereas, on slow timescale, a data-driven Markovian decision process (MDP) is developed, in which the charge/discharge power of energy storage systems (ESSs), statuses/ratios of switchable capacitors reactors (SCRs), and voltage regulators (VRs) are configured hourly to minimize long-term discounted squared voltages magnitudes deviations using an adapted deep deterministic policy gradient (DDPG) deep reinforcement learning (DRL) algorithm. The capabilities of the proposed method are validated with ieee 33-bus balanced and 123-bus unbalanced distribution systems.

关键词： Distribution systems voltage control DRL QP dual-timescale.

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Trajectory Tracking by an Adaptive controller for High-Speed Train Based on Neural Network and Sliding Mode control 12

Trajectory Tracking by an Adaptive Controller for High-Speed...

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ieee 12th data driven control and learning systems conference (DDCLS)

作者： Cen, Zhou Zhi, Li Yuan, Wangqing Fei, Sunpeng Xing, Guoyou Southwest Jiaotong Univ Sch Elect Engn Chengdu Peoples R China

ISBN: (纸本)9798350321050

This paper proposes an adaptive sliding mode controller (ASMC) for trajectory tracking of high-speed trains (HST) with uncertainties. The ASMC incorporates a radial basis function neural network (RBFNN) to approximate the model uncertainties and uses a sigmoid function to reduce chattering. The Lyapunov function is employed to prove the stability of the controller. Numerical simulations using the data of China Railway High-Speed (CRH) 380 train demonstrate the effectiveness of the controller in ensuring the train follows the tracking target under different prior knowledge conditions.

关键词： High-speed trains trajectory tracking control multibody dynamics model adaptive sliding mode control radial basis function neural network

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Distributed Model Free Adaptive Iterative learning control of Multiple HSTs Under DoS Attacks 12

Distributed Model Free Adaptive Iterative Learning Control o...

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ieee 12th data driven control and learning systems conference (DDCLS)

作者： Yu, Wei Cheng, Jungiang Huang, Deging Southwest Jiaotong Univ Sch Elect Engn Chengdu 610031 Peoples R China Europe Aisa Hitech & Digital Technol Co Ltd Zhengzhou 450000 Peoples R China

ISBN: (纸本)9798350321050

This paper studies the distributed model free adaptive iterative learning control (MFAILC) of multiple high-speed trains (MHSTs) under malicious denial-of-service (DoS) attacks. By using the equivalent linearization technique, the discrete-time dynamic model of MHSTs is firstly converted into a linear data-based one. Then, the strategy of DoS attacker is introduced, which is represented by a Bernoulli variable with unknown mathematical expectation. Next, the distributed MFAILC scheme is conducted, which belongs to the scope of data-driven approach. Finally, the stability of MHSTs is studied and the validity of the MFAILC is confirmed by a numerical test.

关键词： Distributed control model free adaptive iterative learning control multiple high-speed trains denial-of-service attacks

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