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第33届中国控制与决策会议

作者： Huijun Guo Jintao Liang Key Laboratory of Shaanxi Province for Complex System Control and Intelligent Information Processing Guilin University of Electronic Technology

This paper investigates the adaptive tracking control problem for active suspension system(ASS) with time-varying constraints via the neural network(NN) *** nonlinear terms in the spring and damper coefficients,input time delay of the actuator and the suspension performances are also considered *** NNs are used to approximate the unknown function caused by the uncertain car-body *** is known to all that ensuring the vertical displacement and speed states in the constraint regions can guarantee the stability and safety of the ***,by introducing the symmetric barrier lyapunov function from the time-varying constraints,an adaptive controller based on the NNs and nonlinear backstepping technique is developed to guarantee the states within *** the stability of the whole system,including the semi-global ultimately uniformly boundedness of the all signals of the ASSs,is analyzed by the Lyapunov stability ***,simulation results are provided to verify the feasibility of the adaptive learning controller.

关键词： Active suspension systems adaptive control input delay neural networks

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Direct symbol decoding using GA-SVM in chaotic baseband wireless communication system

arXiv

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arXiv 2021年

作者： Yin, Hui-Ping Ren, Hai-Peng Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China

To retrieve the information from the serious distorted received signal is the key challenge of communication signal processing. The chaotic baseband communication promises theoretically to eliminate the inter-symbol interference (ISI), however, it needs complicated calculation, if it is not impossible. In this paper, a genetic algorithm support vector machine (GA-SVM) based symbol detection method is proposed for chaotic baseband wireless communication system (CBWCS), by this way, treating the problem from a different viewpoint, the symbol decoding process is converted to be a binary classification through GA-SVM model. A trained GA-SVM model is used to decode the symbols directly at the receiver, so as to improve the bit error rate (BER) performance of the CBWCS and simplify the symbol detection process by removing the channel identification and the threshold calculation process as compared to that using the calculated threshold to decode symbol in the traditional methods. The simulation results show that the proposed method has better BER performance in both the static and time-varying wireless channels. The experimental results, based on the wireless open-access research platform, indicate that the BER of the proposed GA-SVM based symbol detection approach is superior to the other counterparts under a practical wireless multipath channel. Copyright © 2021, The Authors. All rights reserved.

关键词： Bit error rate

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Research and application of Gaussian coupled control bistable stochastic resonance system with independent parameter regulation

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Results in Physics 2025年 73卷

作者： Hou, Tiantian Jiao, Shangbin Wang, Yi Song, Nianlong Li, Jianghua Cui, Wenchuan Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an 710048 China

The theory of Stochastic Resonance (SR) has drawn significant attention due to its exceptional ability to detect faint signals. Despite this, research to date indicates that for SR systems, whether they are monostable, bistable, or multi-stable, modifications to the system parameters lead to concurrent alterations in the depth and breadth of the potential wells when analyzing engineering signals, which results in suboptimal detection outcomes. To address these issues, a two-dimensional Gaussian bistable coupled SR (GBCSR) system has been proposed that can individually adjust the potential well characteristics. This innovative system facilitates the separate adjustment of shape characteristics of potential, allowing for more precise manipulation of the system's dynamic response. The system's non-linear dynamic traits are explicated through an analysis of the steady-state probability density (SPD) function and the mean first passage time (MFPT), substantiating the effectiveness of the new model. In practical scenarios, a variety of bearing defect signals serve as the objects of detection. The structural parameters of the GBCSR system are co-optimized using the Brain Storm Optimization (BSO) algorithm. This optimization approach leverages the algorithm's ability to enhance population diversity and improve convergence accuracy, thereby optimizing the system's performance. The experimental outcome results show that the proposed system can accurately detect the frequency of bearing fault signals. When compared with traditional SR systems such as the traditional bistable stochastic SR (TBSR), the traditional Gaussian SR system (TGSR), and the cascade stochastic resonance system, the proposed coupled system demonstrates superior performance. This is achieved through the transfer of energy or information between subsystems, which enables more efficient utilization of noise energy. The system can trigger the resonance effect over a broader range of noise intensities and signi

关键词： Bearing fault detection Coupled stochastic resonance Independent parameter regulation MFPT SPD

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A Cell Potential and Motion Pattern Driven Multi-robot Coverage Path Planning Algorithm 14th

A Cell Potential and Motion Pattern Driven Multi-robot Cover...

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14th International Conference on Bio-inspired Computing: Theories and Applications, BIC-TA 2019

作者： Xu, Meng Xin, Bin Dou, Lihua Gao, Guanqiang School of Automation State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9789811534249

This paper proposes an intelligent "Cell Potential and Motion Pattern driven Coverage (CPMPC)" algorithm to solve a cooperative coverage path planning problem for multiple robots in two-dimensional target environment. The target environment is divided into cell areas according to the detection range of robot, and the cell matrix is given correspondingly. The values in the cell matrix are defined as cell potential, which represents the number of times each cell is detected by robots. The priority of the robot’s neighbor cell is called the motion pattern. At different moments, robots can choose within different motion patterns. Genetic algorithm (GA) is used to optimize the combination of motion patterns. By taking account obstacle avoidance and collision avoidance into consideration, the CPMPC algorithm adopts a double-layer choice strategy driven by cell potential and motion pattern to generate the next waypoint. Furthermore, this algorithm contains two optimal strategies: avoiding collision and jumping out of the detected area. Compared with the pattern-based genetic algorithm, the results obtained by us show that the CPMPC algorithm could solve the multi-robot coverage path planning (MCPP) problem effectively with guarantee of complete coverage, and improved makespan. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Multipurpose robots

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Temperature Rising Model of Tubular Permanent Magnet Linear Synchronous Motor Based on Transfer Learning-Deep Neural Network

Temperature Rising Model of Tubular Permanent Magnet Linear ...

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Chinese control Conference (CCC)

作者： YiFei Wang Tao Wu School of Automation China University of Geosciences Wuhan P. R. China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan P. R. China System Integration and Industrial Robotics Laboratory Wuhan P. R. China

High temperature rise of permanent magnet linear synchronous motor can lead to irreversible demagnetization of the motor permanent magnet, which can negatively affect the motor performance. To address this problem, a thermal modeling analysis method based on Transfer learning-Deep neural network (TL-DNN) was proposed in this paper. Its specific implementation steps include (1) corresponding to different heat source inputs, the equivalent thermal circuit method and the finite element analysis was adopted based on the structure and main parameters of PMLSM including overall average temperature rise, coil temperature rise and permanent magnet temperature rise from the data sets of the motor; (2) TL-DNN was used to fit the functional relationship between the input source features and the output targets based on the characteristics of the sample data sets. In order to verify the accuracy of the prediction model with small sample data sets, this paper divided the proportion of the data set and compared the results with other classic nonparametric models (random forest, support vector machine, and deep neural network). The results show that the TL-DNN model outperforms other machine learning models and has better robustness and generalization ability when the training datasets are insufficient, and achieves an organic combination of physical field path model and data-driven model, which provides a feasible solution for PMLSM modeling in the case of small samples.

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Isochronous Temporal Metric for Neighbourhood Analysis in Classification Tasks

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SN Computer Science 2023年第6期4卷 1-12页

作者： Kumar, Amit Krishan Mai, Nguyễn Ngọc Tian, Ke Xia, Yuanqing Institute of Research and Development Duy Tan University Da Nang Vietnam Faculty of Electrical-Electronic Engineering Duy Tan University Da Nang Vietnam Faculty of Chinese Language School of Foreign Language Duy Tan University Da Nang Vietnam State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing China

Machine learning, classification, and clustering techniques use the distance functions to evaluate the proximity between data entries and deduce the best neighbouring element and the closest matching entry. The best neighbour is not only the closest neighbour but a neighbour that is quick to respond. In view of that, a time-based isochronous metric is introduced to evaluate the best neighbours and form linkages by grouping similar entities. The proposed method uses parametric equations of the fastest descent and solves the time variables for attributes localised in curved space–time. The time metric is compared with commonly used distance metrics for accuracy in classification and clustering using benchmark and commonly used datasets. The nearest-neighbour technique is used for evaluating classification accuracy, and an adjusted random index (ARI) is used to evaluate clustering. The proposed method shows better accuracy and ARI in comparison to distance functions. It also assigns better weights to attributes of the dataset and easily identifies repeated patterns in noisy time series data.

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A Weighting Factors Autotuning Strategy for Model Predictive Torque control of Permanent Magnet Synchronous Motor Based on Adaptive Multi-Objective Black Hole Algorithm

A Weighting Factors Autotuning Strategy for Model Predictive...

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Chinese control Conference (CCC)

作者： Zixiang Zhou Tao Wu School of Automation China University of Geosciences Wuhan P. R. China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan P. R. China System Integration and Industrial Robotics Laboratory Wuhan P. R. China

The rational design of weighting factors in the cost function for finite control set model predictive torque control (FCS-MPTC) has been a matter of great interest in power electronics and electrical drives. In order to solve this problem, a weighting factors autotuning strategy for FCS-MPTC of permanent magnet synchronous motor (PMSM) based on the adaptive multi-objective black hole algorithm (AMOBH) is proposed. In this paper, the design process of the FCS-MPTC algorithm is first analyzed in detail. Then, an AMOBH algorithm that can take into account both population convergence and population diversity is introduced, and based on this algorithm, the design problem of the weighting factors is successfully transformed into a multi-objective optimization problem by means of reconstructing the cost function and designing the motor operation information collected in real time as the objective functions of the multi-objective optimization algorithm. Simulation results show that the proposed method can find a set of weighting factor combinations suitable for different working condition requirements, and these weighting factors can effectively improve the operation performance of the PMSM system.

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Parametric Transformation of Timed Weighted Marked Graphs:Applications in Optimal Resource Allocation

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IEEE/CAA Journal of Automatica Sinica 2021年第1期8卷 179-188页

作者： Zhou He Ziyue Ma Zhiwu Li Alessandro Giua the College of Mechanical and Electrical Engineering Shaanxi University of Science and TechnologyXi’an 710021China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of TechnologyXi’an 710048China the School of Electro-Mechanical Engineering Xidian UniversityXi’an 710071China Institute of Systems Engineering Macao University of Science and TechnologyTaipa 999078MacaoChina the Department of Electrical and Electronic Engineering University of CagliariCagliari 09124Italy

Timed weighted marked graphs are a subclass of timed Petri nets that have wide applications in the control and performance analysis of flexible manufacturing *** to the existence of multiplicities(i.e.,weights)on edges,the performance analysis and resource optimization of such graphs represent a challenging *** this paper,we develop an approach to transform a timed weighted marked graph whose initial marking is not given,into an equivalent parametric timed marked graph where the edges have unitary *** order to explore an optimal resource allocation policy for a system,an analytical method is developed for the resource optimization of timed weighted marked graphs by studying an equivalent ***,we apply the proposed method to a flexible manufacturing system and compare the results with a previous heuristic *** analysis shows that the developed approach is superior to the heuristic approach.

关键词： Discrete event system resource allocation timed Petri net weighted marked graph

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Coordinated Motion control for a Six-wheel-drive Skid-steering Mobile Robot

Coordinated Motion Control for a Six-wheel-drive Skid-steeri...

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Chinese Automation Congress (CAC)

作者： Yongkang Xu Shoukun Wang Hongqiang Zhao Zhihua Chen State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing China State Key Laboratory of High Performance and Complex Manufacturing Central South University Changsha China

ISBN: (纸本)9781665426480

A speed consensus control strategy for a six-wheel-drive skid-steering mobile robot was proposed based on a hierarchical controller in a complex environment with unknown disturbances. The effective method of reliable speed consensus control strategy should consider some disturbances, such as the internal-robot assembly error, which including instability of motor control system and external physical interference in robot dynamic system. In this paper, a driving anti-slip control system with PID and the stability control system with sliding mode by variable structure is designed and integrated to elevate the stability of the robot. And, a G-vector control algorithm based on sliding mode control is proposed to reduce the yaw angle as much as possible during the process of differential steering of robots. The effectiveness of the proposed control strategy is verified by an example of a robot. The results show that the method has good control performance in control accuracy and stability.

关键词： Uncertainty Robot kinematics Heuristic algorithms Velocity control Torque control Wheels Consensus control

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An Improved Task Planning Method for Unmanned Swarms with Coupled Physical and Logical Constraints

An Improved Task Planning Method for Unmanned Swarms with Co...

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IEEE International Conference on Unmanned systems (ICUS)

作者： Xiubin Chen Lei Zhang Fang Xu Weiran Yao School of Astronautics Harbin Institude of Technology Harbin China Wuhan 2nd Ship Design and Research Institute Wuhan China Marine Military Intelligence Innovation Institute CSSC Systems Engineering Research Institute Beijing China National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Beijing China

ISBN: (数字)9798350384185

ISBN: (纸本)9798350384192

Traditional task allocation methods for unmanned swarm systems ignore the effects of actual paths, resulting in estimation accuracy reduction. This paper formulates task planning problem by incorporating physical and logical constraints, and establishes an integrated framework of task allocation and path planning. Conflict-based search method is used to address path planning with physical constraints. A genetic algorithm is employed to solve multi-traveling salesman allocation problem. A bounded suboptimal optimization, a data dictionary, and an island model are introduced to accelerate the convergence speed of the genetic algorithm. The experiments verify that compared to the decoupled task planning methods, the proposed method improves task execution efficiency and remains adaptability to various complex spatial maps.

关键词： Dictionaries Search methods Estimation Path planning Data models Planning Resource management Optimization Genetic algorithms Convergence

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