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

作者： Wei, Yao Ma, Weiyuan Xie, Haotian Ke, Dongliang Wang, Fengxiang Quanzhou Institute of Equipment Manufacturing Haixi Institutes Chinese Academy of Sciences Jinjiang362200 China College of Mechanical and Electronic Engineering Fujian Agriculture and Forestry University Fuzhou350002 China

ISBN: (纸本)9798350361674

The accuracy of a data-driven model directly affects the control performance of model-free predictive control (MFPC). To take into account the affine motion of the plant, a model-free predictive current control (MF-PCC) strategy is proposed in this paper and applied to a permanent magnet synchronous motor (PMSM) driving system. To enhance model accuracy, we adopt an affine ultra-local model, which is designed to accurately represent the system. Online estimation of all affine operators is performed using the recursive least squares (RLS) algorithm to ensure a robust fit. To improve control performance, we integrate a state compensation technique with the model. The compensation gain is selected using the ϵ varepsilon-approximation approach to meet terminal constraints. The stability of the proposed method is theoretically analyzed, and its effectiveness is verified through experimental results. The proposed method offers improved model accuracy and current quality, along with enhanced robustness compared to conventional methods. © 2024 ieee.

关键词： Predictive control systems

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Rigid Motion Gaussian Processes With SE(3) Kernel and Application to Visual Pursuit control

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ieee control systems LETTERS 2023年 7卷 2665-2670页

作者： Omainska, Marco Yamauchi, Junya Lederer, Armin Hirche, Sandra Fujita, Masayuki Univ Tokyo Dept Informat Phys & Comp Tokyo 1130033 Japan Tech Univ Munich Dept Elect & Comp Engn D-80333 Munich Germany

We address in this letter the learning of unknown rigid body motions in the Special Euclidian Group SE(3) based on Gaussian Processes. A new covariance kernel for SE(3) is presented and proven to be a valid kernel for Gaussian Process Regression. The learning error of the proposed Gaussian Process model is extended to a high-probability statement on SE(3). We employ it in a visual pursuit scenario of a moving target with unknown velocity in 3D space. Our approach is validated in a simulated 3D environment in Unity, and shows significant better prediction accuracy than the most commonly used Gaussian kernel. When compared to other covariance kernels proposed on SE(3), its advantages are a natural extension of covering numbers to SE(3), that it is computationally more efficient, and that stability of target pursuit can be guaranteed without limiting the target rotational space to SO(2).

关键词： Autonomous systems data driven control machine learning robotics uncertain systems

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data driven Verification of Positive Invariant Sets for Discrete, Nonlinear systems 6

Data Driven Verification of Positive Invariant Sets for Disc...

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6th Annual learning for Dynamics and control conference

作者： Strong, Amy K. Bridgeman, Leila J. Duke Univ Dept Mech Engn & Mat Sci Durham NC 27708 USA

Invariant sets are essential when establishing safety of nonlinear systems. However, certifying the existence of a positive invariant set for a nonlinear model is difficult and often requires knowledge of the system's dynamic model. This paper presents a data driven method to certify a positive invariant set for an unknown, discrete, nonlinear system. A triangulation of a subset of the state space is used to query data points. Then, a convex optimization problem is used to create a continuous piecewise affine (CPA) function that fulfills the criteria of the Extended Invariant Set Principle by leveraging an inequality error bound that uses the system's Lipschitz constant. Numerical results demonstrate the program's ability to certify positive invariant sets from sampled data.

关键词： LaSalle's invariance principle stability of nonlinear systems invariant sets data driven

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Machine learning-Based control of Electric Vehicle Charging for Practical Distribution systems With Solar Generation

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ieee TRANSACTIONS ON SMART GRID 2024年第3期15卷 3098-3113页

作者： Calero, Ivan Canizares, Claudio A. Farrokhabadi, Mostafa Bhattacharya, Kankar Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada Univ Calgary Elect & Software Engn Dept Calgary AB T2N 1N4 Canada

The adoption of Electric Vehicles (EVs) and solar Photovoltaic (PV) generation by households is rapidly and significantly increasing. Utilities are facing the challenge of efficiently managing EV and PV resources to help mitigate the undesirable effects on grid operation. Existing approaches to solve these issues depend on accurate but hard to predict behavior of EVs and PVs, detailed knowledge of customers, and grid infrastructure, all of which complicate the effective deployment of these resources. Motivated by these practical challenges and in collaboration with industry partners working on addressing these issues, this paper proposes a two-level data-driven smart controller for EV charging in distribution systems. The controller is modeled as a Deep Reinforcement learning (DRL) agent, which coordinates the charging rates of multiple EVs connected to a realistic residential feeder with high penetration of PV generation. The first level coordinates the aggregated EV load at distribution Medium Voltage (MV) level to provide Demand Response (DR) services;at the Low Voltage (LV) level it aims to maximize the EVs' state of charge at departure while avoiding the overloading of the MV/LV distribution transformers. The controller is verified through simulations on an actual utility grid facing the aforementioned challenges, demonstrating the effectiveness and practicality of the proposed DRL-based smart charging approach.

关键词： Deep deterministic policy gradient deep reinforcement learning demand response distribution system electric vehicle machine learning photovoltaic

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On Prescribed Performance ADRC for a Class of Nonlinear systems with Uncertain control Input Gain 13

On Prescribed Performance ADRC for a Class of Nonlinear Syst...

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

作者： Chen, Zhixiang Xue, Wenchao Yao, Ruiqiao Chen, Sen Song, Xiaojing Qingzhou High-tech Institute Weifang262500 China Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematics and Statistics Shaanxi Normal University Xi'an710119 China Beijing Institute of Control and Electronic Technology Beijing100089 China

ISBN: (纸本)9798350361674

This paper considers the prescribed performance tracking problem for nonlinear systems with uncertain control input gain. A novel prescribed performance active disturbance rejection control design featured with low computational complexity and large feasible range of the nominal control input gain is proposed. To deal with the tracking error constraint, an output error transformation is introduced to transform the constrained variable into an unconstrained one. A high-gain observer and sliding surface technique are combined to stabilize the converted unconstrained control system. To reduce the computational complexity, a reduced-order extended state observer is designed to estimate the system uncertainties and the high-order derivatives terms of error transformation, which arise from the 'explosion of terms'. Moreover, compared with the existing prescribed performance ADRC, the proposed ADRC method needs only the control direction, relaxing the strong assumption on the nominal control input gain. It can be proved that the resulting closed-loop system is semi-globally uniformly ultimately bounded (SGUUB), which ensures the prescribed performance of the tracking error. Finally, simulation results demonstrate the effectiveness of the proposed control scheme. © 2024 ieee.

关键词： Disturbance rejection

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data-driven control with Inherent Lyapunov Stability 62

Data-Driven Control with Inherent Lyapunov Stability

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62nd ieee conference on Decision and control (CDC)

作者： Min, Youngjae Richards, Spencer M. Azizan, Navid MIT Lab Informat & Decis Syst Cambridge MA 02139 USA Stanford Univ Autonomous Syst Lab ASL Stanford CA 94305 USA

ISBN: (纸本)9798350301243

Recent advances in learning-based control leverage deep function approximators, such as neural networks, to model the evolution of controlled dynamical systems over time. However, the problem of learning a dynamics model and a stabilizing controller persists, since the synthesis of a stabilizing feedback law for known nonlinear systems is a difficult task, let alone for complex parametric representations that must be fit to data. To this end, we propose control with Inherent Lyapunov Stability (CoILS), a method for jointly learning parametric representations of a nonlinear dynamics model and a stabilizing controller from data. To do this, our approach simultaneously learns a parametric Lyapunov function which intrinsically constrains the dynamics model to be stabilizable by the learned controller. In addition to the stabilizability of the learned dynamics guaranteed by our novel construction, we show that the learned controller stabilizes the true dynamics under certain assumptions on the fidelity of the learned dynamics. Finally, we demonstrate the efficacy of CoILS on a variety of simulated nonlinear dynamical systems.

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A Machine learning-Assisted Distributed Optimization Method for Inverter-Based Volt-VAR control in Active Distribution Networks

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ieee TRANSACTIONS ON POWER systems 2024年第2期39卷 2668-2681页

作者： Li, Boda Xu, Qianwen KTH Royal Inst Technol Div Elect Power & Energy Syst S-10044 Stockholm Sweden

The number of smart inverters in active distribution networks is growing rapidly, making it challenging to realize a fast, distributed Volt/Var control (VVC). This work proposes a machine learning-assisted distributed algorithm to accelerate the solution of the VVC strategy. We first observe the convergence process of the Alternating Direction Method of Multipliers (ADMM)-based VVC problem and explore the potential relationships between the convergence and time-series regression. Then, the long short-term memory (LSTM) technique is applied to learn the convergence process and regress the converged values of the dual and global variables with previous ADMM observations. After that, the LSTM-assisted ADMM algorithm is proposed, where the regressions are used for ADMM parameter updates. In this algorithm, the inputs of the LSTM-model are carefully designed since the complementary conditions implied in the conventional ADMM should be considered. Unlike existing methods, the proposed method does not use the LSTM to determine the VVC strategy directly, indicating that it is non-intrusive and can satisfy all safety constraints during operations. The proof of its optimality and convergence is also given. The numerical simulations on the 33-bus distribution system demonstrate the effectiveness and efficiency of the proposed method.

关键词： Active distribution system Volt/Var control reactive power control long short-term memory alternating direction method of multipliers data-driven optimization

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Intelligent Load Forecasting and Renewable Energy Integration for Enhanced Grid Reliability

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ieee TRANSACTIONS ON INDUSTRY APPLICATIONS 2024年第6期60卷 8403-8417页

作者： Saxena, Abhishek Shankar, Ravi El-Saadany, Ehab F. Kumar, Manish Al Zaabi, Omar Al Hosani, Khalifa Muduli, Utkal Ranjan Khalifa Univ Adv Power & Energy Ctr Dept Elect Engn Abu Dhabi 127788 U Arab Emirates NIT Patna Dept Elect Engn Patna 800005 Bihar India

The integration of Renewable Energy Resources (RERs) into electrical grids introduces significant challenges concerning the reliability and stability of the grid. This paper focuses on these challenges, particularly the issues of real-time load forecasting and adaptive inertia control in renewable integrated grids. A data-driven, deep learning-based approach is proposed to dynamically forecast real-time load and renewable energy generation, using the New England ieee 39-Bus Power System as a case study. To enhance the dynamic performance of the microgrid, the paper introduces an enhanced fractional extended state observer-based linear active disturbance rejection control mechanism coupled with a feedback architecture. This control scheme aims to provide adaptive inertia to the system, thus improving its ability to handle fluctuations and intermittencies inherent in RERs. The effectiveness of the proposed controller is rigorously compared with existing approaches through simulation studies, validating its superior performance for the ieee 39-Bus Power System under examination. To further substantiate the findings, a hardware-in-loop real-time experimental analysis is conducted using OPAL-RT hardware. This hardware-based analysis serves as a functional validation of the proposed data-driven forecasting algorithm confirming its viability to improve the grid reliability.

关键词： Real-time systems Power system stability Renewable energy sources Reliability Microgrids Power system reliability Load forecasting Load frequency control load forecasting renewable energy resources system dynamic response

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Hand It to Me Formally! data-driven control for Human-Robot Handovers With Signal Temporal Logic

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ieee ROBOTICS AND AUTOMATION LETTERS 2024年第10期9卷 9039-9046页

作者： Khanna, Parag Fredberg, Jonathan Bjorkman, Marten Smith, Christian Linard, Alexis KTH Royal Inst Technol Div Robot Percept & Learning RPL EECS S-11428 Stockholm Sweden

To facilitate human-robot interaction (HRI), we aim for robot behavior that is efficient, transparent, and closely resembles human actions. Signal Temporal Logic (STL) is a formal language that enables the specification and verification of complex temporal properties in robotic systems, helping to ensure their correctness. STL can be used to generate explainable robot behaviour, the degree of satisfaction of which can be quantified by checking its STL robustness. In this letter, we use data-driven STL inference techniques to model human behavior in human-human interactions, on a handover dataset. We then use the learned model to generate robot behavior in human-robot interactions. We present a handover planner based on inferred STL specifications to command robotic motion in human-robot handovers. We also validate our method in a human-to-robot handover experiment.

关键词： Handover Robots Robot kinematics Behavioral sciences Trajectory Logic Robustness Human-robot handovers Signal Temporal Logic (STL)

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Interval Observer Based Fault Diagnosis for Wastewater Treatment Biological Processes 13

Interval Observer Based Fault Diagnosis for Wastewater Treat...

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

作者： Zhou, Meng Wu, Yan Zhu, Zhicheng Wang, Jing Xue, Tonglai School of Electrical and Control Engineering North China University of Technology Beijing100144 China Inner Mongolia Ecological Environment Supervision Technical Support Center Hohhot010011 China

ISBN: (纸本)9798350361674

This paper proposes an interval observer based fault diagnosis method for the biological growth process in wastewater treatment. First, the nonlinear microbial growth model is transformed into a Takagi-Sugeno (T-S) fuzzy model with unknown but bounded disturbances and measurement noise. Then, considering H∞ disturbance attenuation conditions, a T-S interval observer is designed for the obtained T-S fuzzy system to obtain interval boundaries of the states. Furthermore, an interval residual can be calculated by the interval states. Next, the sensor fault signals can be detected by examing that whether zero belongs to the residual interval. Finally, simulation results are presented to validate the feasibility of the proposed fault diagnosis method in wastewater treatment biological processes. © 2024 ieee.

关键词： Fuzzy systems

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