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2015 34th Chinese Control Conference (CCC)最新文献

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State of health estimation combining robust deep feature learning with support vector regression 结合鲁棒深度特征学习和支持向量回归的健康状态估计
2015 34th Chinese Control Conference (CCC) Pub Date : 2015-09-14 DOI: 10.1109/CHICC.2015.7260613
Liu Qiao, L. Xun
{"title":"State of health estimation combining robust deep feature learning with support vector regression","authors":"Liu Qiao, L. Xun","doi":"10.1109/CHICC.2015.7260613","DOIUrl":"https://doi.org/10.1109/CHICC.2015.7260613","url":null,"abstract":"Combining Stacked Contractive Auto-Encoders (SCAE) with Support Vector Regression (SVR) method based on mass of data, a novel state of health estimation method is proposed in this paper. With the development of SCAE-SVR, SCAE could learn features automatically for SVR instead of extracting hand-designed features. SCAE is a deep machine learning method of unsupervised statistical algorithm that makes the learned features more robust and efficient. Then Support Vector Regression machine is used to estimate quantitative values dealing with the new feature representations. The composite structure of network not only remedies not enough features abstracted by a simplex shallow machine learning net, but also effectively avoid over-fitting in data regression. State of health estimation for Fuel cell systems from Prognostics and Health Management (PHM) 2014 Data Challenge demonstrates that the proposed method outperforms than other state of health estimation methods based on data-driven.","PeriodicalId":421276,"journal":{"name":"2015 34th Chinese Control Conference (CCC)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125025821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 6
Iterative learning control for networked nonlinear systems using latest information 基于最新信息的网络非线性系统迭代学习控制
2015 34th Chinese Control Conference (CCC) Pub Date : 2015-09-14 DOI: 10.1109/CHICC.2015.7260114
D. Shen, Yun Xu
{"title":"Iterative learning control for networked nonlinear systems using latest information","authors":"D. Shen, Yun Xu","doi":"10.1109/CHICC.2015.7260114","DOIUrl":"https://doi.org/10.1109/CHICC.2015.7260114","url":null,"abstract":"The iterative learning control (ILC) algorithm is constructed for networked nonlinear systems with random measurement losses modeled by a stochastic sequence. The algorithm updates regularly when the corresponding measurement is available, while updates with the latest available packet from previous iterations if the corresponding one is lost. The almost sure convergence is strictly proved, and illustrative simulations verify the effectiveness of the proposed algorithm.","PeriodicalId":421276,"journal":{"name":"2015 34th Chinese Control Conference (CCC)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125162052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Neural network based load prediction model for an ultra-supercritical turbine power unit 基于神经网络的超超临界汽轮发电机组负荷预测模型
2015 34th Chinese Control Conference (CCC) Pub Date : 2015-09-14 DOI: 10.1109/CHICC.2015.7259954
Ma Liang-yu, Cheng Lei
{"title":"Neural network based load prediction model for an ultra-supercritical turbine power unit","authors":"Ma Liang-yu, Cheng Lei","doi":"10.1109/CHICC.2015.7259954","DOIUrl":"https://doi.org/10.1109/CHICC.2015.7259954","url":null,"abstract":"Widespread implementation of the regional power grid centered automatic generation control (AGC) proposes higher demands on the unit load control precision, rate and response time of a large-scale ultra-supercritical power unit. To improve the unit load control quality with advanced intelligent control strategies, it is of great significance to establish an accurate load prediction model for the steam turbine unit. A 1000MW ultra-supercritical turbine power unit is taken as the object investigated in this work. By taking its regenerative cycle system, hot-side and cold-side steam parameters into consideration, a BP neural network with time-delay inputs and output time-delay feedbacks is adopted to establish a nonlinear dynamic load prediction model for the steam turbine unit. By optimizing the neural network model structure and the inputs/output time-delay orders through elaborate real-time simulation tests, the optimal model structure is determined, which is with higher load prediction accuracy, good generalization ability and fit for intelligent coordinated controller design to improve the unit load control.","PeriodicalId":421276,"journal":{"name":"2015 34th Chinese Control Conference (CCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125848760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Distributed event-triggered control for coupled nonholonomic mobile robots 耦合非完整移动机器人的分布式事件触发控制
2015 34th Chinese Control Conference (CCC) Pub Date : 2015-09-14 DOI: 10.1109/CHICC.2015.7259816
Yilong Qiu, Chen Fei, Linying Xiang
{"title":"Distributed event-triggered control for coupled nonholonomic mobile robots","authors":"Yilong Qiu, Chen Fei, Linying Xiang","doi":"10.1109/CHICC.2015.7259816","DOIUrl":"https://doi.org/10.1109/CHICC.2015.7259816","url":null,"abstract":"In this paper, a coordinated tracking problem for coupled nonholonomic mobile robots is considered. An event-triggered control strategy is proposed to guarantee that the robots can form a prespecified geometric pattern while the centroid of the geometric pattern can track a reference signal. The stability of the system is proved with the aid of Lyapunov techniques. Finally, a simulation example is presented to verify the theoretical results.","PeriodicalId":421276,"journal":{"name":"2015 34th Chinese Control Conference (CCC)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123976751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Fitness feedback based particles swarm optimization 基于适应度反馈的粒子群优化
2015 34th Chinese Control Conference (CCC) Pub Date : 2015-09-14 DOI: 10.1109/CHICC.2015.7260047
Ren Huifeng, Xie Jun, Hu Guyu
{"title":"Fitness feedback based particles swarm optimization","authors":"Ren Huifeng, Xie Jun, Hu Guyu","doi":"10.1109/CHICC.2015.7260047","DOIUrl":"https://doi.org/10.1109/CHICC.2015.7260047","url":null,"abstract":"Inertia weight w and acceleration coefficients c are the most effective ways of improving the performance of particle swarm optimization (PSO). A improved PSO was proposed, in which w and c were set to be the function of fitness value and adapted itself in the way of fitness feedback at each iteration. In order to reduce the probability of trapping into a local minimum value, w was recalculated according to the number of iterations, when w equaled to zero during successive M iterations. The proposed adaptive strategy has been implemented and compares with fixed inertia weight PSO (FIWPSO), linearly decreasing inertia weight PSO (LDIWPSO) and nonlinearly decreasing inertia weight PSO (NDIWPSO) employing three global minimum problems. The experimental results establish the supremacy of the proposed variants over the existing ones in terms of convergence speed, robustness and computational precision.","PeriodicalId":421276,"journal":{"name":"2015 34th Chinese Control Conference (CCC)","volume":"169 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124451826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
The study of speech training and learning method based on DIVA model 基于DIVA模型的语音训练与学习方法研究
2015 34th Chinese Control Conference (CCC) Pub Date : 2015-09-14 DOI: 10.1109/CHICC.2015.7260240
Zhang Shaobai, Hu Chenhong
{"title":"The study of speech training and learning method based on DIVA model","authors":"Zhang Shaobai, Hu Chenhong","doi":"10.1109/CHICC.2015.7260240","DOIUrl":"https://doi.org/10.1109/CHICC.2015.7260240","url":null,"abstract":"DIVA (Directions Into of Articulators) model is a kind of self-adaptive neutral network model which controls movements of a simulated vocal tract in order to produce words, syllables or phonemes. However, there exist poor classification ability, out of consideration of overlap and other deficiencies among multiple modeling primitives in current Hidden Markov(HMM) training algorithm. It impacts speech recognition rate of the model. Therefore, this paper proposes a hybrid model HMM/PNN, which is to use Predictive Neural Network (PNN) in ANN(Artificial neutral network) to calculate station posterior distribution of Hidden Markov Model. The acoustic model of DIVA is reconstructed through extracting acoustic parameter, choosing modeling unit and other methods. The simulations show that after training and learning the pronunciation of compound vowel by using new HMM/PNN model, there' s not huge difference between the waveform of the acquired speech and that of real person, in addition, the recognition rate is also improved. All these verify the effectiveness and accuracy of this method.","PeriodicalId":421276,"journal":{"name":"2015 34th Chinese Control Conference (CCC)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133319456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Neural network based constrained optimal guidance for Mars entry vehicles 基于神经网络的火星飞行器约束最优制导
2015 34th Chinese Control Conference (CCC) Pub Date : 2015-09-14 DOI: 10.1109/CHICC.2015.7260015
Qiu Tenghai, Luo Biao, Wu Huai-Ning, Guo Lei
{"title":"Neural network based constrained optimal guidance for Mars entry vehicles","authors":"Qiu Tenghai, Luo Biao, Wu Huai-Ning, Guo Lei","doi":"10.1109/CHICC.2015.7260015","DOIUrl":"https://doi.org/10.1109/CHICC.2015.7260015","url":null,"abstract":"In this paper, an approximate constrained optimal guidance law is proposed for Mars entry vehicles guidance. Firstly, the original guidance of Mars entry vehicle is transformed into a fixed-time optimal tracking control problem, which depends on the solution of the Hamilton-Jacobi-Bellman (HJB) equation. Considering the case the control input is constrained, a generalized non-quadratic performance index is defined. In general, the HJB equation is a nonlinear partial differential equation that is difficult or even impossible to be solved analytically. To overcome the difficulty, neural network (NN) is used to solve the HJB equation approximately. Subsequently, the Monte-Carlo integration method and Latin Hypercube Sampling (LHS) are introduced to compute the integrals on multi-dimensional domains. Finally, the Monte-Carlo simulation results on the Mars entry vehicle demonstrate the effectiveness of the proposed method.","PeriodicalId":421276,"journal":{"name":"2015 34th Chinese Control Conference (CCC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132506618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Performance enhancement of supervisory control for largely mismatched processes 对大量不匹配过程的监督控制的性能增强
2015 34th Chinese Control Conference (CCC) Pub Date : 2015-09-14 DOI: 10.1109/CHICC.2015.7260915
S. Li, F. Gao
{"title":"Performance enhancement of supervisory control for largely mismatched processes","authors":"S. Li, F. Gao","doi":"10.1109/CHICC.2015.7260915","DOIUrl":"https://doi.org/10.1109/CHICC.2015.7260915","url":null,"abstract":"Even through being an effective approach to handle robustness, the single H∞ controller is challenging to meet strict performance requirement when the modeling mismatch is relatively large. This paper presents a supervisory method to enhance the robust stability and performance of H∞ control using a multiple model switching configuration. A new scheduling logic which selects the most proper controller into loop is proposed to ensure bounded exponentially weighted H∞ norm of the closed loop system. The candidate controllers are obtained by solving a set of linear matrix inequalities (LMIs). The effectiveness of this supervisory H∞ control method is validated with a fist-order inertial process with pure time delay.","PeriodicalId":421276,"journal":{"name":"2015 34th Chinese Control Conference (CCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132649145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Lyapunov function method for linear fractional order systems 线性分数阶系统的Lyapunov函数方法
2015 34th Chinese Control Conference (CCC) Pub Date : 2015-09-14 DOI: 10.1109/CHICC.2015.7259848
Zhao Yige, Wang Yuzhen, Liu Zhi
{"title":"Lyapunov function method for linear fractional order systems","authors":"Zhao Yige, Wang Yuzhen, Liu Zhi","doi":"10.1109/CHICC.2015.7259848","DOIUrl":"https://doi.org/10.1109/CHICC.2015.7259848","url":null,"abstract":"Using the Lyapunov function method, this paper investigates the stability and the stabilization of linear fractional order systems, and presents a number of new results. First, some new properties of Caputo fractional derivative are presented. Then, a sufficient condition of asymptotical stability for fractional order systems is obtained by the Lyapunov function method. Furthermore, by designing state feedback controller for fractional order linear systems, asymptotical stabilization for closed-loop systems is considered. Finally, two illustrative examples are provided to illustrate main results.","PeriodicalId":421276,"journal":{"name":"2015 34th Chinese Control Conference (CCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115036349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 12
Panoramic sea-sky line extraction based on improved hough circle transform 基于改进霍夫圆变换的全景海天线提取
2015 34th Chinese Control Conference (CCC) Pub Date : 2015-09-14 DOI: 10.1109/CHICC.2015.7260210
Su Li, P. Di, Liu Zhilin
{"title":"Panoramic sea-sky line extraction based on improved hough circle transform","authors":"Su Li, P. Di, Liu Zhilin","doi":"10.1109/CHICC.2015.7260210","DOIUrl":"https://doi.org/10.1109/CHICC.2015.7260210","url":null,"abstract":"Sea-sky line extraction is very important for the detection of dim targets appeared near the area of sea-sky line. Aiming at the complexity of panoramic image and the characteristic that the sea-sky-line in panoramic image shows as a nearly circle, a novel sea-sky line extraction algorithm based on improved Hough circle transform is proposed in this paper. Firstly, the edge of image is detected and the interference points in binary edge image are removed by calculating the fractal dimension of the image, then the panoramic sea-sky line can be extracted by finding the optical center and radius. Experimental results show that this algorithm can effectively extract the sea-sky lines in panoramic images.","PeriodicalId":421276,"journal":{"name":"2015 34th Chinese Control Conference (CCC)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130439187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
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