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第35届中国控制与决策会议

作者： Jianyun Ni Helei Du Tie Wang Hao Li Chenyang Xue Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems School of Electronic Engineering and Automation Tianjin University of Technology Tianjin Climate Center Tianjin University of Technology

An improved artificial potential field method is proposed to address the problems of target unreachability and falling into local minima in the path planning process of the traditional artificial potential field ***,the distance factor between the robot and the target point is introduced to solve the target unreachability problem;secondly,the robot is guided out of the local minima point by setting the virtual target point through the double-circle strategy;finally,in order to satisfy the continuity of robot velocity and acceleration,the resulting path is smoothed by using three uniform B-sample *** experimental results show that the improved algorithm can effectively solve the problems of target unreachability and falling into local minima in the traditional algorithm,and the smoothness of the path after the spline treatment is better compared with the original path.

关键词： artificial potential field method target unreachable local minimum point bicircular strategy virtual target point B-sample curve

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Encrypted Finite-Horizon Energy-to-Peak State Estimation for Time-Varying systems Under Eavesdropping Attacks: Tackling Secrecy Capacity

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 985-996页

作者： Lei Zou Zidong Wang Bo Shen Hongli Dong Guoping Lu College of Information Science and Technology Donghua UniversityShanghai 201620 Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of EducationShanghai 201620China College of Electrical Engineering and Automation Shandong University of Science and TechnologyQingdao 266590China Department of Computer Science Brunel University LondonUxbridgeMiddlesex UB83PHUK Intelligence Energy Research Institute Northeast Petroleum UniversityDaqing 163318 Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum UniversityDaqing 163318 Sanya Offshore Oil&Gas Research Institute Northeast Petroleum UniversitySanya 572024China School of Electrical Engineering Nantong UniversityNantong 226019China

This paper is concerned with the problem of finitehorizon energy-to-peak state estimation for a class of networked linear time-varying *** to the inherent vulnerability of network-based communication,the measurement signals transmitted over a communication network might be intercepted by potential *** avoid information leakage,by resorting to an artificial-noise-assisted method,we develop a novel encryption-decryption scheme to ensure that the transmitted signal is composed of the raw measurement and an artificial-noise term.A special evaluation index named secrecy capacity is employed to assess the information security of signal transmissions under the developed encryption-decryption *** purpose of the addressed problem is to design an encryptiondecryption scheme and a state estimator such that:1)the desired secrecy capacity is ensured;and 2)the required finite-horizon–l_(2)-l_(∞)performance is *** conditions are established on the existence of the encryption-decryption mechanism and the finite-horizon state ***,simulation results are proposed to show the effectiveness of our proposed encryption-decryption-based state estimation scheme.

关键词： Artificial-noise-assisted technique eavesdropping encryption-decryption scheme energy-to-peak state estimation finitehorizon state estimation

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Displacement prediction of landslide using SARIMA model based on seasonal characteristic

Displacement prediction of landslide using SARIMA model base...

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第42届中国控制会议

作者： Ruoxi Wu Chengda Lu Wanke Yu Min Wu Xuzhi Lai School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

Landslide displacement prediction is an important and indispensable part of landslide monitoring and *** change of the displacement is always considered being related to inducing factors,which are aimed at improving accuracy of the predicted ***,the seasonal characteristic of the displacement,which has not been carefully analyzed,reveals the law of inducing *** order to gain a deeper understanding of characteristics,the Baijiabao landslide is taken as an *** variational mode decomposition(VMD) method,which can extract effective information well,is introduced to decompose the *** the seasonal parameters,the seasonal autoregressive integrated moving average(SARIMA) model is established to predict the displacement ***,accumulative displacement prediction values are obtained by superimposing the predicted *** higher accuracy and lower error,the VMD-SARIMA model proves a better option in application compared with VMD-ARIMA,SARIMA and ARIMA models.

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Energy-aware Coverage control of Multi-Agent systems with control Barrier Function

Energy-aware Coverage Control of Multi-Agent Systems with Co...

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第42届中国控制会议

作者： Chengkai Qu Chao Zhai School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

Geo-hazards have become one of the main disasters endangering the safety of people's lives and property in the *** order to improve the early warning of disasters, a persistent monitoring method of multi-agent systems is proposed in this *** ensure that the agent's energy is never exhausted, the set invariance constraint is included in the optimization problem. The goal is to minimize the difference between the actual control input of the robot and the nominal control input corresponding to the task to be performed. Moreover, the control barrier function(CBF) is used to transform the forward invariance of a subset of the robot state space into a control input constraint. The coverage control method in an uncertain environment is verified by numerical simulation. This work provides new insights into effective monitoring and early warning of geo-hazards.

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Frequency Domain Auto-tuning of Structured LPV controllers for High-Precision Motion control

arXiv

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arXiv 2024年

作者： Broens, Yorick Butler, Hans Tóth, Roland The Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands ASML Veldhoven Netherlands The Systems and Control Laboratory HUN-REN Institute for Computer Science and Control Hungary

Motion systems are a vital part of many industrial processes. However, meeting the increasingly stringent demands of these systems, especially concerning precision and throughput, requires novel control design methods that can go beyond the capabilities of traditional solutions. Traditional control methods often struggle with the complexity and position-dependent effects inherent in modern motion systems, leading to compromises in performance and a laborious task of controller design. This paper addresses these challenges by introducing a novel structured feedback control auto-tuning approach for multiple-input multiple-output (MIMO) motion systems. By leveraging frequency response function (FRF) estimates and the linear-parameter-varying (LPV) control framework, the proposed approach automates the controller design, while providing local stability and performance guarantees. Key innovations include norm-based magnitude optimization of the sensitivity functions, an automated stability check through a novel extended factorized Nyquist criterion, a modular structured MIMO LPV controller parameterization, and a controller discretization approach which preserves the continuous-time (CT) controller parameterization. The proposed approach is validated through experiments using a state-of-the-art moving-magnet planar actuator *** Codes 37M15 © 2024, CC BY.

关键词： Continuous time systems

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Stability analysis of delayed T-S fuzzy systems via the fuzzy line-integral method

Stability analysis of delayed T-S fuzzy systems via the fuzz...

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第42届中国控制会议

作者： Zhou-Zhou Liu Li Jin Yong He School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

This paper focuses on the problem of stability analysis for Takagi-Sugeno systems with time-varying delays. Firstly,a suitable Lyapunov-Krasovskii functional(LKF) containing fuzzy line-integral Lyapunov functional is constructed, which can introduce membership functions information while avoiding emerging the time-derivatives of membership functions. Then, a generalized free-matrix-based integral inequality is applied to estimate the derivative of the LKF. As a result, a less conservative stability criterion is obtained. Finally, a numerical example is carried out to illustrate the effectiveness and merits of our method.

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Design and implementation of Gaussian filter for nonlinear system with randomly delayed measurements and correlated noises

Design and implementation of Gaussian filter for nonlinear s...

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第42届中国控制会议

作者： Weijie Cui Xinmei Wang Qian Hu Fei Du School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

A new Gaussian approximate(GA) filter for nonlinear systems with one-step randomly delayed measurement and correlated noise is proposed in this ***,a general framework of Gaussian filter is designed under Gaussian assumption on the conditional ***,the implementation of Gaussian filter is transfomed into the approximation of the Gaussian weighted integral in the proposed ***,a new cubature Kalman filtering(CKF)algorithm is developed on the basis of the spherical-radial cubature *** efficiency and superiority of the proposed method are illustrated in the numerical examples.

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Exponential Stability Analysis for Delayed Neural Networks via A Cubic Function Negative-Determination Lemma

Exponential Stability Analysis for Delayed Neural Networks v...

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第42届中国控制会议

作者： Wen-Xuan Zhao Yun-Fan Liu Yu-Long Fan Chuan-Ke Zhang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

This paper concerns with the exponential stability of delayed neural networks via Lyapunov-Krasovskii functional(LKF) method. Initially, an improved augmented delay-product-type LKF containing an additional double integral state is established, which introduces more delayed states and has less conservatism. In the LKF's derivative, the function has high order of delay due to the existence of exponent. Thus, in order to obtain tractable linear matrix inequalities, three state vectors are used to reduce the order of the function to cubic. Secondly, to achieve the negative-definiteness requirement, a negative-determination lemma for cubic functions with less conservatism is employed. Then, a less conservative delay-dependent stability criterion for neural networks with time-varying delays is established. Finally, the validity of the proposed delay-dependent stability criterion is illustrated by two numerical examples.

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Deep Learning control for Autonomous Robot

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Computers, Materials & Continua 2022年第8期72卷 2811-2824页

作者： Rihem Farkh Saad Alhuwaimel Sultan Alzahrani Khaled Al Jaloud Mohammad Tabrez Quasim College of Engineering Muzahimiyah BranchKing Saud UniversityRiyadh11451Saudi Arabia Laboratory for Analysis Conception and Control of SystemsLR-11-ES20Department of Electrical Engineering National Engineering School of TunisTunis El Manar University1002Tunisia King Abdulaziz City for Science and Technology Saudi Arabia College of Computing and Information Technology University of BishaBisha67714Saudi Arabia

Several applications of machine learning and artificial intelligence,have acquired importance and come to the fore as a result of recent advances and improvements in these *** cars are one such *** is expected to have a significant and revolutionary influence on *** with smart cities,new infrastructure and urban planning with sophisticated cyber-security are some of the current ramifications of self-driving *** autonomous automobile,often known as selfdriving systems or driverless vehicles,is a vehicle that can perceive its surroundings and navigate predetermined routes without human *** are on the verge of evolving into autonomous robots,thanks to significant breakthroughs in artificial intelligence and related technologies,and this will have a wide range of socio-economic ***,in order for these automobiles to become a reality,they must be endowed with the perception and cognition necessary to deal with high-pressure real-life events and make proper judgments and take appropriate *** majority of self-driving car technologies are based on computer systems that automate vehicle control *** forward-collision warning and antilock brakes to lane-keeping and adaptive drive control,to fully automated driving,these technological components have a wide range of capabilities.A self-driving car combines a wide range of sensors,actuators,and *** researches on computer vision and deep learning are used to control autonomous driving *** self-driving automobiles,lane-keeping is *** study presents a deep learning approach to obtain the proper steering angle to maintain the robot in the *** propose an advanced control for a selfdriving robot by using two controllers *** neural networks(CNNs)are employed,to predict the car’and a proportionalintegral-derivative(PID)controller is designed for speed and steering *** stu

关键词： Autonomous car cascade PID control deep learning convolutional neural network differential drive system raspberry PI road lane detector

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Data mining techniques for estimating instantaneous NO X emissions from Euro 6 diesel passenger cars

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Transportation Research Procedia 2024年 78卷 444-451页

作者： Paulo Fernandes Margarida C. Coelho Department of Mechanical Engineering / Centre for Mechanical Technology and Automation (TEMA) University of Aveiro Campus Universitário de Santiago 3810-193 Aveiro Portugal LASI – Intelligent Systems Associate Laboratory Portugal

Nitrogen oxides (NO X ) emissions that are caused by road traffic diesel engines affects public health. The existing instantaneous emissions models are often imprecise due to the lack of knowledge of highly non-linear processes behind real-world emissions and they do not include meteorological and driving volatility variables. This paper applied data mining techniques based on the Cross Industry Standard Process for Data Mining (CRISP-DM) method to a dataset of four diesel Euro 6 passenger cars tested in real-world driving conditions to: a) model stabilised hot NO X emissions based on kinematic (speed), internal engine (engine coolant temperature, engine load, engine speed, intake air temperature, manifold absolute pressure and mass air flow), meteorological (humidity) and driving volatility (acceleration and vehicular jerk); b) compare the performance of different machine learning (ML) techniques in predicting NO X emission rates, namely: Artificial Neural Networks (ANN), Random Forest (RF), and Gradient-Boosted Trees (GBT). The model that utilizes a set of detailed variables, particularly engine coolant temperature, engine load, engine speed, intake air temperature, humidity, acceleration and vehicular jerk, and using ANN technique was better able to deal with variability in emission data than models based on a single set of these variables. It was also found that models produced high Root Mean Square Error due to their inability in predicting high peaks in measured emission data. The presented models rely on fast inference times and can therefore be deployed for engine control units to inform drivers about their NO X emissions during driving.

关键词： NOx emissions Instantaneous models, Data mining CRISP-DM Machine Learning

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