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An Improved Model-Free Active Disturbance Rejection Deadbeat Predictive Current control Method of PMSM Based on data-driven

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ieee TRANSACTIONS ON POWER ELECTRONICS 2023年第8期38卷 9606-9616页

作者： Wang, Yicheng Fang, Shuhua Huang, Demin Southeast Univ Sch Elect Engn Nanjing 210096 Peoples R China

This article proposes an improved model-free active disturbance rejection deadbeat predictive current control(ADRDPCC) method for permanent magnet synchronous motor (PMSM) used in more electric aircraft (MEA) based on the data driven method, which is used to solve the parameter mismatch problem of deadbeat predictive current control (DPCC) and improve the performance of PMSM control system. DPCC model applied to the current loop of the MEA motor is established as the main control strategy of the system. The principle of active disturbance rejection control is combined with DPCC, and the ADRDPCC structure is formed to optimize the control strategy. A specific extended state observer (ESO) of ADRDPCC is designed to track the internal disturbance caused by parameter mismatch and the external disturbance in real time. DPCC is used as the control law of the ADRDPCC structure to predict the current and output the reference voltage based on the observation of ESO. A deep reinforcement learning model based on ADRDPCC is designed and trained based on the data-driven method. The trained model can compensate and optimize ADRDPCC based on the disturbance observed by ESO and the observed control state of PMSM. The simulated and experimental results show the superiority of the proposed method.

关键词： Predictive models Mathematical models Current control Voltage control Real-time systems Permanent magnet motors Observers Active disturbance rejection control deadbeat predictive current control (DPCC) deep reinforcement learning (DRL) more electric aircraft (MEA) permanent magnet synchronous motor (PMSM)

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data-driven control for Nonlinear Multi-Agent systems Using Reinforcement learning 39

Data-Driven Control for Nonlinear Multi-Agent Systems Using ...

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39th Youth Academic Annual conference of Chinese-Association-of-Automation (YAC)

作者： Liu, Renzhi Ren, Hongru Ma, Hui Li, Hongyi Lu, Renquan Guangdong Univ Technol Sch Automat Guangdong Hong Kong Joint Lab Intelligent Decis & Guangzhou 510006 Peoples R China Guangdong Univ Technol Guangdong Prov Key Lab Intelligent Decis & Cooper Guangzhou 510006 Peoples R China Guangdong Univ Technol Sch Math & Stat Guangzhou Peoples R China Southwest Univ Sch Elect & Informat Engn Chongqing 400715 Peoples R China Southwest Univ Key Lab Gener Technol & Syst Serv Robots Chongqing 400715 Peoples R China

ISBN: (纸本)9798350390780;9798350379228

In this paper, a new reinforcement learning-based model-free adaptive control algorithm is introduced for discrete-time nonlinear multi-agent systems with unknown dynamics, while the equivalent dynamic linearization algorithm is applied to design the optimal controller. The strategy for Q-learning and the actor-critic neural network are specifically redesigned to achieve consensus control in multi-agent systems. The proposed reinforcement learning algorithm can adjust the dynamic linearization parameters in real-time only based on input and output data. The stability of the closed-loop system is proven by Lyapunov theorem. Furthermore, the method's effectiveness is verified by a numerical simulation.

关键词： equivalent dynamic linearization multi-agent systems model-free adaptive control reinforcement learning

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Microgrid Economic Dispatch Problem Based on Distributed Algorithm in V2G Background 13

Microgrid Economic Dispatch Problem Based on Distributed Alg...

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

作者： Ren, Lina Li, Shuai Hu, Zhihan Yanshan University Key Lab of Industrial Computer Control of Hebei Province Qinhuangdao066004 China

ISBN: (纸本)9798350361674

With the emergence of increasingly complex modern energy networks, there is a need for flexible and reliable methods to solve economic dispatch problems in smart grids. For this purpose, a broadcast gossip algorithm is proposed for solving the economic dispatch problem in a smart grid environment populated with distributed generators and vehicle to grid. First, with the objective of minimizing the system cost, the cost model of each part of the microgrid is developed under various constraints. At the same time, considering the peak-to-valley difference in the process of electric energy dispatching, this paper proposes an electric vehicle incentive and compensation cost model to achieve the purpose of making the grid peak and valley reduction. Then, an event triggering mechanism is introduced to save communication resources while economical scheduling. Finally, the proposed model and algorithm are simulated and analyzed by using the microgrid structure of ieee 11 - bus standard, and the effectiveness of the proposed model and algorithm are verified. © 2024 ieee.

关键词： Vehicle-to-grid

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Finite-Time Vehicle Platoon control for CPVS Under DoS Attacks

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ieee TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2025年

作者： Guo, Zhiyuan Fan, Sha Fu, Yukai Zhang, Dan Dong, Lu Deng, Chao Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210003 Peoples R China Nanjing Univ Posts & Telecommun Coll Artificial Intelligence Nanjing 210003 Peoples R China Chinese Acad Sci Shenyang Inst Automat State Key Lab Robot Shenyang 110016 Peoples R China Chinese Acad Sci Key Lab Networked Control Syst Shenyang 110016 Peoples R China Zhejiang Univ Technol Dept Automat Hangzhou 310023 Peoples R China Southeast Univ Sch Cyber Sci & Engn Nanjing 211189 Peoples R China Nanjing Univ Posts & Telecommun Inst Adv Technol Nanjing 210023 Peoples R China

This paper addresses the problem of vehicle platoon control for third-order nonlinear cyber physical vehicle systems (CPVSs) within finite-time under denial-of-service (DoS) attacks. Unlike existing approaches that assume known systematic matrix of the leader vehicle, this study proposes a data-driven learning algorithm to learn unknown systematic matrix of the leader vehicle. Additionally, a finite-time distributed observer is introduced, thereby enabling follower vehicles to achieve finite-time state observation of the leader vehicle under DoS attacks. Moreover, a novel low-pass filter chain is designed to construct a new variable with high-order derivatives. Utilizing the new variable, a finite-time resilient decentralized controller is formulated, incorporating fuzzy adaptive methods and backstepping techniques to achieve finite-time vehicle platoon control under DoS attacks. Finally, simulation experiments validate the effectiveness of the proposed method.

关键词： Observers Denial-of-service attack Vehicle dynamics Vectors Low-pass filters Asymptotic stability Stability analysis Resilience Protocols Heuristic algorithms Vehicle platoon DoS attacks data-driven learning algorithm finite-time resilient decentralized controller

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Mobile Robot Navigation with Incremental Self-Organizing Developmental Network Learned Based on Modified RRT 13

Mobile Robot Navigation with Incremental Self-Organizing Dev...

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

作者： Shi, Mengchao Wang, Dongshu School of Electrical & Information Engineering Zhengzhou University Zhengzhou450001 China

ISBN: (纸本)9798350361674

When a mobile robot performs tasks, it may encounter changeable unknown environments. When navigating in the changing unknown environment, mobile robot should be able to learn incrementally to gradually improve their navigation capabilities. Considering that the fixed network structure will limit the continuous learning ability of the robot, this paper proposes an incremental self-organized developmental network, which does not need to define the number of neurons in advance, and its memory capacity adaptively increases with the learned knowledge. The network is employed to learn continuously in multiple complex environments without forgetting previously learned knowledge and without neuronal redundancy or insufficiency. In addition, this paper integrates the Rapidly Exploring Random Tree (RRT) algorithm into autonomous continuous learning process of incremental self-organizing developmental network, using the modified RRT algorithm to perform a global search of the environment to obtain a feasible path that the network can learn. This method can target the information in the specific environment, reduce the sample data for training the developmental network, improve the learning efficiency, and the network can show better generalization ability after continuous learning. The experimental results validate the effectiveness and potential of the proposed network as well as its learning method. © 2024 ieee.

关键词： Adversarial machine learning

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Sparse Bayesian learning-Based Interval Type-2 Fuzzy Logic control for Electrospinning Processes

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ieee TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2024年第8期71卷 9489-9499页

作者： Sun, Hongwei Zhang, Hai-Tao Xing, Ning Wang, Yasen Shi, Yang Huazhong Univ Sci & Technol MOE Engn Res Ctr Autonomous Intelligent Unmanned S Guangdong Engn Technol Res Ctr Fully Autonomous Un Sch Artificial Intelligence & AutomatState Key La Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China Univ Victoria Dept Mech Engn Victoria BC V8W 2Y2 Canada

This article develops a closed-loop electrospinning process control system composed of a high-speed industrial camera, an interval type-2 (IT2) fuzzy logic controller (FLC) and a high-precision programmable micropump. A pure data-driven IT2 T-S fuzzy model with a micropump flow input and a fiber diameter output is established by a sparse Bayesian learning (SBL) method, and the closed-loop IT2 FLC is thereby proposed to finely tune the electrospinning fiber diameter according to the technical requirement of the circuit electrospinning process suffered by external disturbances and system uncertainties. Sufficient conditions are derived to guarantee the asymptotical stability of the closed-loop system with the assistance of Lyapunov theory. Experiments on bead-chain structure electrospinning process are conducted to show the effectiveness and superiority of the present SBL-based fuzzy controller.

关键词： control systems fuzzy control learning control systems

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Lateral Trajectory Tracking of Unmanned Vehicles Based on Active Disturbance Rejection Model Predictive control 13

Lateral Trajectory Tracking of Unmanned Vehicles Based on Ac...

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

作者： Ai, Wei Zhang, Tong Wang, Yang Li, Xiangyang College of Automation Science and Technology South China University of Technology Guangzhou510640 China

ISBN: (纸本)9798350361674

In addition to internal state constraints such as lateral velocity and cross-swing angular velocity, external factors, such as a complex time-varying environment, directly impact the tracking performance. For a typical underdriven incomplete constraint system of a vehicle, it is possible to explicitly handle the physical constraints of the system while achieving high accuracy and stable trajectory tracking in the presence of parameter uncertainties and internal/external disturbances. This paper proposes a lateral trajectory tracking control algorithm based on active disturbance rejection model predictive control. By employing the concept of disturbance compensation, a multivariate extended state observer is designed to real time observe and estimate the system state and unknown disturbances, effectively compensating for them to enhance the system's robustness. Furthermore, adopting the principle of optimal control, the optimal control input is determined using a partially known adaptation model, considering the physical constraints of the system, thereby ensuring the stability of vehicle driving. Finally, comparative simulation experiments are conducted to validate the effectiveness of the proposed algorithm. © 2024 ieee.

关键词： Predictive control systems

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Confidence-driven Adaptive Optimal Impedance control Base on Ultrasound Robot System 11

Confidence-Driven Adaptive Optimal Impedance Control Base on...

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11th ieee International conference on Cybernetics and Intelligent systems (CIS) / 11th ieee International conference on Robotics, Automation and Mechatronics (RAM)

作者： Lia, Jinkun Zhang, Li Zheng, Wenxu Huang, Haohui Guo, Jing Chen, Shouyan Guangdong Univ Technol Sch Automat Guangzhou Peoples R China Agr Dev Ctr Kaizhou Dist Chongqing Peoples R China

ISBN: (纸本)9798350364200;9798350364194

The ultrasound robotic system (URS) is of tremendous importance for assisting sonographers to diagnose various diseases. Generally, the quality of ultrasound image which is evaluated through confidence map has a high dependency on the experience of sonographers. In order to obtain high-quality ultrasound image under URS, we take confidence map into consideration of control frame and propose confidence-driven adaptive optimal impedance learning scheme. By introducing an appropriate performance function which blends both confidence value and contacting force, an iterative learning strategy is conducted to achieve favorable scanning performance. Finally, we show the experiment results on the human volunteer.

关键词： Ultrasound robot confidence map impedance control

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Robust data-driven Tube-Based Zonotopic Predictive control with Closed-Loop Guarantees 63

Robust Data-Driven Tube-Based Zonotopic Predictive Control w...

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63rd ieee conference on Decision and control, CDC 2024

作者： Farjadnia, Mahsa Fontan, Angela Alanwar, Amr Molinari, Marco Johansson, Karl Henrik Kth Royal Institute of Technology Department of Energy Technology Sweden Kth Royal Institute of Technology Division of Decision and Control Systems Sweden Technical University of Munich School of Computation Information and Technology Germany

ISBN: (纸本)9798350316339

This work proposes a robust data-driven tube-based zonotopic predictive control (TZPC) approach for discrete-time linear systems, designed to ensure stability and recursive feasibility in the presence of bounded noise. The proposed approach consists of two phases. In an initial learning phase, we provide an over-approximation of all models consistent with past input and noisy state data using zonotope properties. Subsequently, in a control phase, we formulate an optimization problem, which by integrating terminal ingredients is proven to be recursively feasible. Moreover, we prove that implementing this data-driven predictive control approach guarantees robust exponential stability of the closed-loop system. The effectiveness and competitive performance of the proposed control strategy, compared to recent data-driven predictive control methods, are illustrated through numerical simulations. © 2024 ieee.

关键词： Predictive control systems

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Supervised learning of Lyapunov Functions Using Laplace Averages of Approximate Koopman Eigenfunctions

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ieee control systems LETTERS 2023年 7卷 3072-3077页

作者： Deka, Shankar A. A. Dimarogonas, Dimos V. V. KTH Royal Inst Technol Dept Elect Engn & Comp Sci S-10044 Stockholm Sweden

Modern data-driven techniques have rapidly progressed beyond modelling and systems identification, with a growing interest in learning high-level dynamical properties of a system, such as safe-set invariance, reachability, input-to-state stability etc. In this letter, we propose a novel supervised Deep learning technique for constructing Lyapunov certificates, by leveraging Koopman Operator theory-based numerical tools (Extended Dynamic Mode Decomposition and Generalized Laplace Analysis) to robustly and efficiently generate explicit ground truth data for training. This is in stark contrast to existing Deep learning methods where the loss functions plainly penalize Lyapunov condition violation in the absence of labelled data for direct regression. Furthermore, our approach leads to a linear parameterization of Lyapunov candidate functions in terms of stable eigenfunctions of the Koopman operator, making them more interpretable compared to standard DNN-based architecture. We demonstrate and validate our approach numerically using 2-dimensional and 10-dimensional examples.

关键词： data-driven modeling Koopman operator Lyapunov function machine learning neural networks

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