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31st Chinese Control And Decision Conference (CCDC)

作者： Shi, Xiaoyu Cheng, Yuhua Yin, Chun Shi, Han Huang, Xuegang Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Peoples R China XiHua Univ Sch Mech Engn Chengdu 610039 Peoples R China China Aerodynam Res & Dev Ctr Mianyang 621000 Sichuan Peoples R China

ISBN: (纸本)9781728101057

In this paper. an actuator fault tolerant controller for the quadrotor UAV is designed. The new method is based on the adaptive RBF neural network and sliding mode control. The dynamic equations which includes external disturbances are constructed by Netwon-Euler theorem. External disturbances of unknown upper bound are approximated by RBF neural network. The adaptive strategy realized the on-line estimation of the actuator fault. The integrated FTC design approach guarantee the state variables converge to the desired values at limited time. The Lyapunov function prove that the system is global asymptotically stable. Futherfore, the backstepping sliding mode control technique alleviates the chattering phenomenon which result from switching law and solve the "explosion of complexity". The simulation results testified the effectiveness and robustness of the presented methodology for the quadrotor UAV.

关键词： Quadrotor UAV radical basis function neural network Sliding mode control Adaptive control law

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radical basis neural network Based Anti-swing Control for 5-DOF Ship-Mounted Crane 5th

Radical Basis Neural Network Based Anti-swing Control for 5-...

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5th International Conference on neural Computing for Advanced Applications (NCAA)

作者： Li, Zhi Hou Chuanjing Liu, Can Shandong Jianzhu Univ Jinan 250101 Peoples R China

ISBN: (纸本)9789819770007;9789819770014

The autonomous anti-sway control of the ship-mounted crane systems is a tough issue due to the under-actuated characteristic, strong coupling and base excitation. Furthermore, the ship-mounted crane systems are also suffering from unknown dynamics and frictions etc. In some conditions, the ship-mounted cranes may exhibit spherical pendulum effects. These factors make the control problem even more challenging. To solve the above problems, this paper designed a radical basis function neural network (RBFNN) based feedback control method for a 5-DOF ship-mounted rotary crane. Specially, the adaptive RBFNN is established to approximate the unknown dynamics online. After that, eschewing any simplification of the dynamic model, a feedback anti-sway control is designed to ensure the cargo could reach to desired position and dampening the payload spherical swing simultaneously. The closed-loop stability is analyzed and the effectiveness of the control method is validated via simulation experiment results.

关键词： Anti-sway control Intelligent control radical basis function neural network Under-actuated system

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UAV based adaptive trajectory tracking control with input saturation and unknown time-varying disturbances

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IET INTELLIGENT TRANSPORT SYSTEMS 2023年第4期17卷 780-793页

作者： Zhou, Yimin Tian, Zengwu Lin, Huaxing Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Univ Chinese Acad Sci Beijing Peoples R China South Univ Sci & Technol Shenzhen Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China

This paper proposes an adaptive control method for an underactuated complex nonlinear system, that is, unmanned aerial vehicle (UAV), to realize high-precision trajectory tracking under input saturation constraint and other disturbances via artificial neural network and anti-saturation auxiliary system. First, the rigid body motion theory is applied to establish a simplified nonlinear dynamic model of the UAV. Then a Lyapunov function is designed with the error surface to form the preliminary control law based on dynamic surface control. Afterwards, a radical basis function neural network is developed and used to estimate and compensate the disturbance, while the input saturation is also addressed via the designed anti-saturation auxiliary system. Thus the stability and signal consistency of the closed-loop UAV control system can be proved via the Lyapunov stability theory with appropriately tuned parameters. Simulation experiments are performed to verify that the proposed control algorithm can be used for UAV high-precision trajectory tracking under time-varying disturbances.

关键词： trajectory control Nonlinear control systems error surface UAV based adaptive trajectory tracking control simplified nonlinear dynamic model artificial neural network Lyapunov methods Mobile robots Time-varying control systems control nonlinearities underactuated complex nonlinear system Lyapunov stability theory aircraft control unknown time-varying disturbances autonomous aerial vehicles unmanned aerial vehicle Telerobotics preliminary control law Aerospace control radial basis function networks dynamic surface control rigid body motion theory closed-loop UAV control system adaptive control input saturation constraint Self-adjusting control systems control system synthesis closed loop systems control algorithm UAV high-precision trajectory tracking nonlinear control systems Control system analysis and synthesis methods Lyapunov function designed anti-saturation auxiliary system radical basis function neural network adaptive control method Stability in control theory time-varying systems Spatial variables control stability

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WPD and DE/BBO-RBFNN for solution of rolling bearing fault diagnosis

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NEUROCOMPUTING 2018年 312卷 27-33页

作者： Zhang, Qing Gao, Junwei Dong, Honghui Mao, Yunlong Qingdao Univ Coll Automat & Elect Engn Qingdao 266071 Peoples R China Beijing Jiaotong Univ Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China

A new system of fault diagnosis which combines wavelet packet decomposition (WPD), radical basis function neural network (RBFNN) and a hybrid differential evolution with biogeography-based optimization (DE/BBO) algorithm is designed in this paper to improve the efficiency and accuracy of rolling bearing fault diagnosis. Biogeography-based optimization (BBO) is a new biogeography inspired algorithm, however it has the disadvantages of early-maturing and instable convergence. Combining BBO with differential evolution (DE) and making small modifications for the mutation strategy of DE, an improved DE/BBO algorithm is formed. Firstly, the fault feature vectors of original rolling bearing fault signals are effectively obtained by wavelet packet decomposition and reconstruction. Secondly, the RBF neural network is optimized by DE/BBO algorithm, the fault types of rolling bearing are trained and diagnosed next. The result of MATLAB simulation shows that in contrast to the traditional RBFNN, the fitness and precision of bearing fault diagnosis are higher and the root-mean-square error (RMSE) is lower because of the introduction of DE/BBO algorithm. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Wavelet packet decomposition Differential evolution Biogeography-based optimization algorithm radical basis function neural network Root-mean-square error Rolling bearing fault diagnosis

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A nonlinear prediction model for Chinese speech signal based on RBF neural network

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第4期81卷 5033-5049页

作者： Gao, Xiaohong Longdong Univ Sch Elect Engn Qingyang Gansu Peoples R China

A novel method for Chinese speech time series prediction model is proposed. In order to reconstruct the phase space of Chinese speech signal, the delay time and embedding dimension are calculated by C-C method and false nearest neighbor algorithm. The maximum lyapunov exponent and correlation dimension of Chinese speech phoneme are calculated by wolf algorithm and genetic programming algorithm. The numerical results show that there exists nonlinear characteristics in Chinese speech signal. Based on the analysis method of RBF neural network and the nonlinear characteristic parameters such as the delay time and embedding dimension, a nonlinear prediction model is designed. In order to further verify the prediction performance of the designed prediction model, waveform comparison and four evaluation indexes are used. It is shown that compared with the linear prediction model and back propagation neural network nonlinear prediction model, prediction error of the RBF neural network nonlinear prediction model is significantly reduced, and the model has higher prediction accuracy and prediction performance.

关键词： Chinese speech signal Nonlinear theory Prediction model radical basis function neural network

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Near infrared spectroscopic (NIRS) analysis of drug-loading rate and particle size of risperidone microspheres by improved chemometric model

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INTERNATIONAL JOURNAL OF PHARMACEUTICS 2014年第1-2期472卷 296-303页

作者： Song, Jia Xie, Jing Li, Chenliang Lu, Jia-hui Meng, Qing-fan Yang, Zhaogang Lee, Robert J. Wang, Di Teng, Le-sheng Jilin Univ Sch Life Sci Changchun 130023 Jilin Peoples R China Ohio State Univ Coll Pharm Div Pharmaceut Columbus OH 43210 USA

Microspheres have been developed as drug carriers in controlled drug delivery systems for years. In our present study, near infrared spectroscopy (NIRS) is applied to analyze the particle size and drug loading rate in risperidone poly(D,L-lactide-co-glycolide) (PLGA) microspheres. Various batches of risperidone PLGA microspheres were designed and prepared successfully. The particle size and drug-loading rate of all the samples were determined by a laser diffraction particle size analyzer and high performance liquid chromatography (HPLC) system. Monte Carlo algorithm combined with partial least squares (MCPLS) method was applied to identify the outliers and choose the numbers of calibration set. Furthermore, a series of preprocessing methods were performed to remove signal noise in NIR spectra. Moving window PLS and radical basis function neural network (RBFNN) methods were employed to establish calibration model. Our data demonstrated that PLS-developed model was only suitable for drug loading analysis in risperidone PLGA microspheres. Comparatively, RBFNN-based predictive models possess better fitting quality, predictive effect, and stability for both drug loading rate and particle size analysis. The correlation coefficients of calibration set (R-c(2)) were 0.935 and 0.880, respectively. The performance of optimum RBFNN models was confirmed by independent verification test with 15 samples. Collectively, our method is successfully performed to monitor drug-loading rate and particle size during risperidone PLGA microspheres preparation. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Risperidone Microspheres Near infrared spectroscopy Partial least squares radical basis function neural network

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Catalytic Cracking and PSO-RBF neural network Model of FCC Cycle Oil

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China Petroleum Processing & Petrochemical Technology 2013年第4期15卷 63-69页

作者： Liu Yibin Tu Yongshan Li Chunyi Yang Chaohe State Key Laboratory of Heavy Oil Processing China University of Petroleum

Catalytic cracking experiments of FCC cycle oil were carried out in a fixed fluidized bed reactor. Effects of reac- tion conditions, such as temperature, catalyst to oil ratio and weight hourly space velocity, were investigated. Hydrocarbon composition of gasoline was analyzed by gas chromatograph. Experimental results showed that conversion of cycle oil was low on account of its poor crackability performance, and the effect of reaction conditions on gasoline yield was obvi- ous. The paraffin content was very high in gasoline. Based on the experimental yields under different reaction conditions, a model for prediction of gasoline and diesel yields was established by radial basis function neural network （RBFNN）. In the model, the product yield was viewed as function of reaction conditions. Particle swarm optimization （PSO） algorithm with global search capability was used to obtain optimal conditions for a highest yield of light oil. The results showed that the yield of gasoline and diesel predicted by RBF neural network agreed well with the experimental values. The optimized reac- tion conditions were obtained at a reaction temperature of around 520 ~C, a catalyst to oil ratio of 7.4 and a space velocity of 8 h~. The predicted total yield of gasoline and diesel reached 42.2% under optimized conditions.

关键词： catalytic cracking cycle oil radical basis function neural network particle swarm optimization

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Intelligent Fault Diagnosis of Steer-By-Wire Automobile

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JOURNAL OF COMPUTERS 2012年第5期7卷 1204-1211页

作者： Wu, Fangyuan Kong, Feng Yao, Jiangyun Guangxi Univ Technol Liuzhou Peoples R China

This paper presents an intelligent fault diagnostic approach for a steer-by-wire (SBW) system. A rough set model is utilized to reduce the redundant information. On the base of the reduction, the classifying rules can be extracted. A radical basis function (RBF) neural network optimized by particle swarm optimization (PSO) algorithm is designed to learn the fault rules that are extracted from the reduction of the redundant information. The proposed approach is simulated in MATLAB. Simulation results show that the proposed intelligent fault diagnostic algorithm provides a higher level of diagnostic accuracy than the approach without any optimization.

关键词： fault diagnosis steer-by-wire rough set radical basis function neural network particle swarm optimization

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Intelligent Vehicle Trajectory Tracking Based on neural networks Sliding Mode Control

Intelligent Vehicle Trajectory Tracking Based on Neural Netw...

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International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS)

作者： Guo Lie Ge Ping-shu Yang Xiao-li Li Bing Dalian Univ Technol Sch Automot Engn Dalian 116024 Peoples R China Dalian Nationalities Univ Coll Electromech & Informat Engn Dalian 116600 Peoples R China

ISBN: (纸本)9781479947522

The problem of lateral control in intelligent vehicle trajectory tracking for automated highway system is studied. The article deduced the vehicle's desired yaw rate through real time planning virtual path between the vehicle mass center and prediction aiming point which is planned according to the vehicle's kinematic model and pose error model. Based on the lateral dynamic model of vehicle, radical basis function (RBF) neural networks based sliding mode variable structure trajectory tracking controller is designed. A multi-body dynamics model of vehicle is built in ADAMS/Car. The interactive combination control dynamic simulation between Matlab/Simulink and ADAMS is realized through designing the data interface between Matlab and ADAMS. Simulations were conducted and the results show that the proposed algorithm improves the control precision of the system and improves the tracking performance of the system.

关键词： control system synthesis intelligent transportation systems neurocontrollers path planning radial basis function networks road traffic control trajectory control variable structure systems vehicle dynamics ADAMS ADAMS/Car Matlab/Simulink RBF neural network based sliding mode variable structure trajectory tracking controller design automated highway system dynamic simulation intelligent vehicle trajectory tracking lateral dynamic model multi-body dynamics model pose error model prediction aiming point radical basis function neural network real time virtual path planning tracking performance vehicle desired yaw rate vehicle kinematic model vehicle mass center Intelligent vehicles neural networks Tires Trajectory Vehicle dynamics Vehicles Vehicle dynamics Intelligent vehicles Radial basis function networks vehicle Variable structure systems neural network visual tracking Dynamic simulation Neurocontrollers trajectory control control system synthesis Intelligent transportation systems Path planning trajectory control accuracy

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Nerual network Assisted Adaptive Unscented Kalman Filter for AUV

Nerual Network Assisted Adaptive Unscented Kalman Filter for...

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Global OCEANS Singapore - U.S. Gulf Coast Conference

作者： Chen, Xu Wu, Shuyi Zhang, Xin Mu, Xiaokai Yan, Tianhong He, Bo Ocean Univ China Sch Informat Sci & Engn Qingdao Peoples R China China Jiliang Univ Hangzhou Peoples R China

ISBN: (纸本)9781728154466

As one of the most important equipment for ocean exploration, Autonomous Underwater Vehicles (AUVs) has attracted wide attention. To collect the valid oceanic data in the complex underwater environment, autonomous navigation is an indispensable prerequisite for AUVs. Different from the traditional navigation algorithms, such as Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), the Adaptive Unscented Kalman Filter (AUKF) proposed by some scholars get better performance. However, there are only minor improvements for the navigation accuracy of AUKF. To improve the navigation accuracy, in this paper, we proposed a neural network assisted navigation method for AUV, which combines the AUKF with the neural networks. Firstly, the model proposed by this paper obtain the preliminary predictions of positions through AUKF. Secondly, the neural network is trained with the data calculated by AUKF and acquire the deviation. Finally, we correct the initial predictions of positions with the deviation. We compare the performance of the proposed methods to the AUKF using AUV real data. Experimental results show that neural network-assisted adaptive UKF filtering is more accurate for AUV navigation compared to the AUKF.

关键词： Autonomous Underwater Vehicle Adaptive Unscented Kalman Filter radical basis function neural network Extreme Learning Machine

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