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Adaptive tracking control of high-order MIMO nonlinear systems with prescribed performance

Frontiers of Information Technology & Electronic engineering 2021年第7期22卷 986-1001页

作者： Xuerao WANG Qingling WANG Changyin SUN School of Automation Southeast UniversityNanjing 210096China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of EducationSoutheast UniversityNanjing 210096China

In this paper,an observer-based adaptive prescribed performance tracking control scheme is developed for a class of uncertain multi-input multi-output nonlinear systems with or without input saturation.A novel finite-time neural network disturbance observer is constructed to estimate the system uncertainties and external *** guarantee the prescribed performance,an error transformation is applied to transfer the time-varying constraints into a constant ***,by employing a barrier Lyapunov function and the backstepping technique,an observer-based tracking control strategy is *** is proven that using the proposed algorithm,all the closedloop signals are bounded,and the tracking errors satisfy the predefined time-varying performance ***,simulation results on a quadrotor system are given to illustrate the effectiveness of the proposed control scheme.

关键词： Adaptive tracking control Prescribed performance Input saturation Disturbance observer Neural network

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MFANet: Mixed Feature Attention Network for Port Lane Detection

MFANet: Mixed Feature Attention Network for Port Lane Detect...

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Chinese control Conference (CCC)

作者： Jinwei Zhang Jinya Su Dewei Yi Jun Yang Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education and the School of Automation Southeast University Nanjing China Department of Computing Science University of Aberdeen Aberdeen U.K.

With the advancement of industrial automation and artificial intelligence technology, unmanned port autonomy has gained increasing attention. Port automatic driving technology is a critical component of unmanned autonomous systems, enabling rubber tire gantry cranes (RTGs) to autonomously navigate by detecting port lanes and implementing control commands. In this study, a deep learning-based method is proposed for detecting port lanes in complex port scenes. The method employs the Inception module and attention mechanism to enhance lane feature extraction, and a structural loss is introduced to explicitly constrain the detection results and incorporate the prior characteristics of port lanes. A novel Mixed Feature Attention Network (MFANet) is proposed to implement the method for port lane detection. Experimental results demonstrate that MFANet effectively improves the accuracy of port lane detection while maintaining high computational efficiency. Furthermore, the performance of MFANet is evaluated in a real-world application scenario.

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A Nonsmooth Observer Based on Friction Model Compensation for Sensorless Permanent Magnet Synchronous Motor

A Nonsmooth Observer Based on Friction Model Compensation fo...

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Chinese control Conference (CCC)

作者： Zaishan Zhang Wenwu Zhu Ke Yu Xiangyu Wang Shihua Li School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China School of Electrical Engineering and Automation Hefei University of Technology Hefei China

In this paper, a nonsmooth observer considering friction compensation is proposed for the sensorless permanent magnet synchronous motor (PMSM) to estimate the rotor position and speed. Friction is one of the main factors affecting the tracking accuracy of the motor at low speed, especially in the back and forth motion of the motor. Firstly, based on the Stribeck friction model, a nonlinear friction model of the motor is established. Secondly, a nonsmooth observer with friction compensation is designed to estimate the position and speed of the rotor, and the lumped disturbance of the system at the same time. Further, the stability analysis of this nonsmooth observer is given. Finally, the effectiveness of the proposed method is verified by the simulation results.

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Finite Time Disturbance Observer-Based Tube Model Predictive control for Vehicle Platoon systems with Guaranteed L∞ String Stability

Finite Time Disturbance Observer-Based Tube Model Predictive...

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Chinese control and Decision Conference, CCDC

作者： Qian Chen Jinhao Liu Jun Yang Shihua Li School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China

ISBN: (纸本)9781665478977

This paper investigates optimal longitudinal control problems for a vehicle platoon in presence of parameter uncertainties and external disturbances. First, a multi-constraint multi-objective optimization model is developed, where physical limits, safety constraints, driving comfort, and fuel economy are taken into account. To redu.e communication burden and avoid network congestion, the preceding vehicle’s acceleration is obtained by employing a finite time disturbance observer (FTDO). As for the parameter uncertainties as well as external disturbances, they are estimated as a lumped disturbance by exploiting a FTDO. Then, under a predecessor following communication topology, a FTDO-based tube model predictive control method with explicit consideration of string stability is contrived. Finally, numerical simulations illustrate the effectiveness and superiority of the proposed control approach.

关键词： Uncertain systems Network topology Fuel economy Numerical simulation Stability analysis Topology Safety

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Modeling of Photovoltaic modules under shading condition and an error evaluation criterion 4

Modeling of Photovoltaic modules under shading condition and...

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2022 4th International Conference on Energy systems and Electrical Power, ICESEP 2022

作者： Lai, GuangZhi Wang, Dong Li, HaoRan Zhao, Yi Ni, WeiChen Wen, JiaHao State Grid e-commerce Co. Ltd Beijing100053 China State Grid Tianjin Electric Power Company Tianjin300392 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing210096 China

Energy is the focus of recent years. As one of the most representative new energy sources, solar energy has the characteristics of large reserves and no pollution. The main way to use solar energy is photovoltaic (PV) power generation, and the PV module in the PV power generation system is the component that converts solar energy into electric energy. In the actual power generation process, PV modules often receive uneven solar illumination due to the shadow caused by clouds, trees, buildings, etc., resulting in changes in the output characteristics of PV modules and redu.ed output efficiency. Therefore, modeling of PV modules under shading condition is very important. This paper presents two simple methods for solving the parameters of PV module models, which require few parameters and are easy to obtain. A modeling method of PV modules under shading condition is also presented. And then the modeling method is verified with the experiment result. Finally, an error criterion for PV module modeling under shading condition is proposed to measure the goodness of the modeling. Combining with the two parameter solving methods, the errors of this PV module modeling method under shading condition of the error criterion proposed in this paper are 3.24% and 2.51%, respectively, which meet the requirements for engineering use. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Solar energy

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Pinr: A Physics-Integrated Neural Representation for Dynamic Fluid Scenes

SSRN

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SSRN 2024年

作者： Zhao, Ziyu Zhou, Sifan Lu, Xiaobo Zeng, Weili Qian, Jian School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China College of Civil Aviation Nanjing University of Aeronautics and Astronautics Nanjing China Key Laboratory of ASIC and System Fudan University Shanghai China

Recent research based on neural representation has achieved impressive results in dynamic reconstruction. However, reconstructing high-fidelity dynamic fluid scenes from multi-view videos remains challenging due to the complex fluid phenomena such as translucency, scattering, occlusion, and visual ambiguity. In this paper, we present a novel neural representation that simultaneously exploits physics knowledge and differentiable volume rendering techniques to construct a physically plausible neural field. We first propose a simple yet effective state field to separate fluid and non-fluid contents apart, which essentially redu.es the amount of computation and appearance ambiguity. To achieve robust optimization and fast convergence, we introduce intermediate velocity in neural representation based on Helmholtz Decomposition to decouple physical quantities (velocity, pressure and density) that are tightly coupled in physical equations. In contrast to previous physics-informed reconstruction methods, we explore a novel solution by introducing a fast and stable fluid solver to update the intermediate velocity to a physically conserved incompressible velocity. This solver is based on a parallelizable multi-scale architecture, which can effectively avoid degenerate solutions. By adopting the proposed physics-aware loss, we achieved better convergence and superior performance in dynamic fluid scenes. Extensive experiments show that the proposed method can achieve state-of-the-art results for reconstructing dynamic fluid scenes, and the performance and efficiency have been significantly improved in different scenarios. © 2024, The Authors. All rights reserved.

关键词： Computer vision

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A nested constraints based anti-disturbance collision-free formation control scheme for multi-mobile robot systems

A nested constraints based anti-disturbance collision-free f...

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Chinese Automation Congress (CAC)

作者： Guodong Wang Xiangyu Wang Shihua Li Dan Niu School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing People’s Republic of China

This paper investigates distributed formation control of multi-mobile robot systems with collision avoidance. A novel nested constraints based anti-disturbance formation control scheme is established, which contains two parts. In the first part, a formation signal generator is constructed on the communication network by using proper distributed cooperative control approaches and artificial potential field-based methods, such that it "completes" the desired formation control task in its outputs. In the second part, by combining proper disturbance estimations, some nonlinear tracking controllers are designed for the robots to track the corresponding outputs of the generator asymptotically. A "nested constraint mechanism" is put forward accordingly and applied to limit the generator’s outputs and the robots’ tracking errors. In this way, the studied multi-mobile robot systems realize the formation control goal with collision avoidance. Compared with existing results, the proposed method has a better plug-and-play feature to deal with the tight encapsulations of the practical robot products, and has better convergence performances with the existence of disturbances. Some experiments are given to validate the effects of the proposed methods.

关键词： Feedback loop Estimation Signal generators Formation control Generators Real-time systems Trajectory

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Distributed Estimation of Sensor Networks Based on LSTM-Kalman Filter*

Distributed Estimation of Sensor Networks Based on LSTM-Kalm...

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International Conference on control, Automation, Robotics and Vision (ICARCV)

作者： Si-peng Kuang Ya Zhang Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing P.R. China

ISBN: (纸本)9781665476881

This paper studies the distributed estimation problem of sensor networks where the noise parameters are unknown and some sensors cannot obtain the measurements. The expectation maximization (EM) algorithm integrating Kalman filtering algorithm, which is called the EM-KF algorithm, is adopted to sensor networks. Simulation examples are given to illustrate the effectiveness of the algorithm. By using LSTM network, and LSTM-KF algorithm is further proposed to improve the accuracy of EM-KF algorithm.

关键词： Automation Estimation Process control Filtering algorithms Robot sensing systems Noise measurement Kalman filters

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Hotspot detection of photovoltaic modules in infrared thermal image based on saliency analysis

Hotspot detection of photovoltaic modules in infrared therma...

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Chinese control and Decision Conference, CCDC

作者： Yiye Wang Yu Shen Chenxi Li Kanjian Zhang Haikun Wei Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing P.R. China

ISBN: (纸本)9781665478977

With the increasing awareness of environmental protection, the solar photovoltaic (PV) industry has been developing dramatically in recent years. PV module is an important part of PV power generation system. In all the defect types of PV modules, hotspots occur more frequently and cause more serious damage. Therefore, it is necessary to detect hotspots. An approach is proposed using saliency analysis method for hotspot detection, combing the cognitive property about visual saliency and the temperature property of hotspots. Not only does this approach not require manual threshold setting, but also it does not require a large number of learning samples. This approach can also achieve the defect diagnosis and hotspot location when compared with the electrical characterization method. The infrared thermal (IRT) images trained in experiments are obtained using the thermographic camera FLIR Vue Pro with the unmanned aerial vehicle from a PV plant in Jiangsu, China. Altogether 135 IRT images are collected and 1020 PV modules are extracted from these images. Experiments have proved great accuracy and robustness when using the proposed method to detect hotspots.

关键词： Photovoltaic systems Industries Visualization Temperature Image edge detection Manuals Cameras

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PVTimesNet: A Hybrid Deep Learning Method for Enhancing Ultra-Short-Term PV Power Forecasting

PVTimesNet: A Hybrid Deep Learning Method for Enhancing Ultr...

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Chinese Automation Congress (CAC)

作者： Ji Wu Kai Wang Wenwen Ma Jingxin Zhang Shibo Wang China Electric Power Research Institute Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing China State Grid Shandong Electric Power Research Institute Jinan China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Accurate ultra-short-term photovoltaic (PV) power forecasting is crucial for the real-time schedu.ing of grid systems. However, the inherent variability of solar energy makes this task extremely challenging. To enhance PV power forecasting, this paper introduces a novel hybrid model named PVTimesNet, designed to harness the strengths of Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) for effective feature extraction. PVTimesNet consists of two parallel branches: the CNN1d branch, which captures correlations between multiple variables and adjacent time steps, and the LSTM branch, which learns the temporal dependencies within the PV power sequences. The features extracted by both branches are then concatenated and passed through a fully connected layer to generate multi-step PV power forecasts. Experimental results demonstrate that for forecast horizons of 1 to 4 hours, the proposed model significantly outperforms individual models and other CNN-LSTM hybrid structures. Additionally, it exhibits superior performance compared to related methods for longer forecast horizons.

关键词： Photovoltaic systems Deep learning Wind speed Predictive models Feature extraction Hybrid power systems Convolutional neural networks Forecasting Wind forecasting Long short term memory

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