Pub Date : 2013-01-01DOI: 10.3182/20130902-3-CN-3020.00110
Wei Wang, G. Liao, Dong Li, Qing Xu, Ke Yang
Abstract With the ability to resolve the ambiguity and higher azimuth resolution, bistatic forward-looking synthetic aperture radar (BFLSAR) imaging is strongly desirable in both navigation and safety landing for small platforms. However, due to the Doppler centroid variation along the range direction, azimuth spectrum will cross into the neighboring pulse repetition frequency (PRF) band causing severe azimuth ambiguity. Conventional frequency focusing method cannot directly used in this case. In this paper, deramping technique is employed to realize the coarse focusing and reduce the azimuth bandwidth, thus spectrum folding is mitigated. Incorporated secondary range compression, fine focusing is obtained. Simulation results validate the effectiveness of the proposed method.
{"title":"Deramping-Based Imaging Method for Bistatic Forward-Looking SAR","authors":"Wei Wang, G. Liao, Dong Li, Qing Xu, Ke Yang","doi":"10.3182/20130902-3-CN-3020.00110","DOIUrl":"https://doi.org/10.3182/20130902-3-CN-3020.00110","url":null,"abstract":"Abstract With the ability to resolve the ambiguity and higher azimuth resolution, bistatic forward-looking synthetic aperture radar (BFLSAR) imaging is strongly desirable in both navigation and safety landing for small platforms. However, due to the Doppler centroid variation along the range direction, azimuth spectrum will cross into the neighboring pulse repetition frequency (PRF) band causing severe azimuth ambiguity. Conventional frequency focusing method cannot directly used in this case. In this paper, deramping technique is employed to realize the coarse focusing and reduce the azimuth bandwidth, thus spectrum folding is mitigated. Incorporated secondary range compression, fine focusing is obtained. Simulation results validate the effectiveness of the proposed method.","PeriodicalId":90521,"journal":{"name":"IEEE International Conference on Systems Biology : [proceedings]. IEEE International Conference on Systems Biology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72941366","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}
Pub Date : 2013-01-01DOI: 10.3182/20130902-3-CN-3020.00118
Zhouhua Peng, Dan Wang, Gang Sun, Hao Wang, Wei Wang
Abstract Unlike the traditional tracking control of single marine vehicle, this paper considers the formation tracking control of multiple marine surface vehicles over a directed network in the presence of unknown dynamics, all seeking to maintain a formation relative to a time-varying trajectory. A cooperative dynamic surface control design approach is proposed to devise the formation tracking controllers, where nonlinearly-parameterized neural networks are used to compensate for the model uncertainties. It is proved that with the developed controllers, formation among vehicles can be reached if the trajectory has directed paths to all follower vehicles. Based on Lyapunov stability analysis, all signals in the closed-loop system are guaranteed to be uniformly ultimately bounded, and formation tracking errors converge to a small neighborhood of the origin. Simulation results are given to show the efficacy of the proposed method.
{"title":"Formation Tracking Control of Multiple Marine Surface Vehicles Over a Directed Network: A Cooperative Dynamic Surface Control Design","authors":"Zhouhua Peng, Dan Wang, Gang Sun, Hao Wang, Wei Wang","doi":"10.3182/20130902-3-CN-3020.00118","DOIUrl":"https://doi.org/10.3182/20130902-3-CN-3020.00118","url":null,"abstract":"Abstract Unlike the traditional tracking control of single marine vehicle, this paper considers the formation tracking control of multiple marine surface vehicles over a directed network in the presence of unknown dynamics, all seeking to maintain a formation relative to a time-varying trajectory. A cooperative dynamic surface control design approach is proposed to devise the formation tracking controllers, where nonlinearly-parameterized neural networks are used to compensate for the model uncertainties. It is proved that with the developed controllers, formation among vehicles can be reached if the trajectory has directed paths to all follower vehicles. Based on Lyapunov stability analysis, all signals in the closed-loop system are guaranteed to be uniformly ultimately bounded, and formation tracking errors converge to a small neighborhood of the origin. Simulation results are given to show the efficacy of the proposed method.","PeriodicalId":90521,"journal":{"name":"IEEE International Conference on Systems Biology : [proceedings]. IEEE International Conference on Systems Biology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88269129","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}
Pub Date : 2013-01-01DOI: 10.3182/20130902-3-CN-3020.00161
Hongbin Zhang, Liangliang Zhang, H. Luo, Yang-Qian Wu, Xiansheng Guo
Abstract This paper presents a stabilization approach for discrete-time nonlinear positive systems with delays via T-S fuzzy model which is developed for both state feedback and observer-based output feedback cases. The state feedback controller is designed based on trajectory approach. The observer-based controller design is obtained based on linear copositive Lyapunov functional and the trajectory approach. The proposed stabilization conditions are formulated in terms of linear programs(LPs), which can be solved efficiently by using existing optimization techniques.
{"title":"Observer-Based Control of Discrete-Time Fuzzy Positive Systems with Time Delays","authors":"Hongbin Zhang, Liangliang Zhang, H. Luo, Yang-Qian Wu, Xiansheng Guo","doi":"10.3182/20130902-3-CN-3020.00161","DOIUrl":"https://doi.org/10.3182/20130902-3-CN-3020.00161","url":null,"abstract":"Abstract This paper presents a stabilization approach for discrete-time nonlinear positive systems with delays via T-S fuzzy model which is developed for both state feedback and observer-based output feedback cases. The state feedback controller is designed based on trajectory approach. The observer-based controller design is obtained based on linear copositive Lyapunov functional and the trajectory approach. The proposed stabilization conditions are formulated in terms of linear programs(LPs), which can be solved efficiently by using existing optimization techniques.","PeriodicalId":90521,"journal":{"name":"IEEE International Conference on Systems Biology : [proceedings]. IEEE International Conference on Systems Biology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77878516","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}
Pub Date : 2013-01-01DOI: 10.3182/20130902-3-CN-3020.00194
X. Cai, Shaoyuan Li, Ning Li, Kang Li
Abstract This paper investigates variation of infinite horizon (IH) performance of Model Predictive Control (MPC) without constraints as the optimization horizon changes. By exploring properties of the Difference Riccati Equations (DRE), an upper bound and a lower bound of the ratio between variation of IH performance of MPC and finite horizon (FH) optimal cost are obtained. The result shows the dynamic behavior of IH performance of closed-loop MPC systems as the optimization horizon varies.
{"title":"On Variation of Infinite Horizon Performance of Model Predictive Control with Varying Receding Horizon","authors":"X. Cai, Shaoyuan Li, Ning Li, Kang Li","doi":"10.3182/20130902-3-CN-3020.00194","DOIUrl":"https://doi.org/10.3182/20130902-3-CN-3020.00194","url":null,"abstract":"Abstract This paper investigates variation of infinite horizon (IH) performance of Model Predictive Control (MPC) without constraints as the optimization horizon changes. By exploring properties of the Difference Riccati Equations (DRE), an upper bound and a lower bound of the ratio between variation of IH performance of MPC and finite horizon (FH) optimal cost are obtained. The result shows the dynamic behavior of IH performance of closed-loop MPC systems as the optimization horizon varies.","PeriodicalId":90521,"journal":{"name":"IEEE International Conference on Systems Biology : [proceedings]. IEEE International Conference on Systems Biology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84961291","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}
Pub Date : 2013-01-01DOI: 10.3182/20130902-3-CN-3020.00096
J. Na, Juan Yang, Xing Wu, Yu Guo
Abstract A novel adaptive identification framework is proposed for sinusoidal signals to estimate all unknown parameters (i.e. offset, amplitude, frequency and phase). The proposed identification is independent of any observer/predictor design and thus can be implemented in a simplified manner. The adaptive laws are driven by appropriate parameter error information derived by applying filter operations on the output measurements. Globally exponential convergence of the parameter estimation is proved. The proposed idea is further extended for multi-sinusoid signals and verified in terms of simulations.
{"title":"Adaptive Parameter Identification of Sinusoidal Signals","authors":"J. Na, Juan Yang, Xing Wu, Yu Guo","doi":"10.3182/20130902-3-CN-3020.00096","DOIUrl":"https://doi.org/10.3182/20130902-3-CN-3020.00096","url":null,"abstract":"Abstract A novel adaptive identification framework is proposed for sinusoidal signals to estimate all unknown parameters (i.e. offset, amplitude, frequency and phase). The proposed identification is independent of any observer/predictor design and thus can be implemented in a simplified manner. The adaptive laws are driven by appropriate parameter error information derived by applying filter operations on the output measurements. Globally exponential convergence of the parameter estimation is proved. The proposed idea is further extended for multi-sinusoid signals and verified in terms of simulations.","PeriodicalId":90521,"journal":{"name":"IEEE International Conference on Systems Biology : [proceedings]. IEEE International Conference on Systems Biology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88967787","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}
Pub Date : 2013-01-01DOI: 10.3182/20130902-3-CN-3020.00193
Shi‐Lu Dai, Min Wang, Cong Wang, Liejun Li
Abstract This paper studies the problem of learning from adaptive neural network (NN) control of a one-link robot manipulator including motor dynamics in uncertain dynamical environments. With the employment of a newly state transformation and a high-gain observer, the one-link robot system is transformed into a norm form, and then only one NN is employed to approximate the lumped uncertain system nonlinearity in the adaptive control design. Partial persistent excitation (PE) condition of radial basis function (RBF) NNs is satisfied during tracking control to a recurrent reference trajectory. Under the PE condition, the proposed adaptive NN control is shown to be capable of acquiring knowledge on the uncertain robot dynamics in the stable control process and of storing the learned knowledge in memory. Subsequently, a novel neural learning control technique exploiting the learned knowledge without readapting to the unknown robot dynamics is developed to achieve closed-loop stability and improved control performance. Simulation studies are performed to demonstrate the effectiveness of the proposed control technique.
{"title":"Learning from Adaptive Neural Output Feedback Control of Robot Manipulators","authors":"Shi‐Lu Dai, Min Wang, Cong Wang, Liejun Li","doi":"10.3182/20130902-3-CN-3020.00193","DOIUrl":"https://doi.org/10.3182/20130902-3-CN-3020.00193","url":null,"abstract":"Abstract This paper studies the problem of learning from adaptive neural network (NN) control of a one-link robot manipulator including motor dynamics in uncertain dynamical environments. With the employment of a newly state transformation and a high-gain observer, the one-link robot system is transformed into a norm form, and then only one NN is employed to approximate the lumped uncertain system nonlinearity in the adaptive control design. Partial persistent excitation (PE) condition of radial basis function (RBF) NNs is satisfied during tracking control to a recurrent reference trajectory. Under the PE condition, the proposed adaptive NN control is shown to be capable of acquiring knowledge on the uncertain robot dynamics in the stable control process and of storing the learned knowledge in memory. Subsequently, a novel neural learning control technique exploiting the learned knowledge without readapting to the unknown robot dynamics is developed to achieve closed-loop stability and improved control performance. Simulation studies are performed to demonstrate the effectiveness of the proposed control technique.","PeriodicalId":90521,"journal":{"name":"IEEE International Conference on Systems Biology : [proceedings]. IEEE International Conference on Systems Biology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80811495","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}
Pub Date : 2013-01-01DOI: 10.3182/20130902-3-CN-3020.00127
Yao Yao
Abstract In recent yeas, long-range accuracy cooperative navigation(CN) for multiple UUVs under complex marine environment is getting more and more attention. In this paper, firstly, the state-of-the-art of multiple UUV cooperation system is introduced. Then, the characteristic and system structure of multiple UUVs CN are analyzed, as well as the CN methods. Finally, based on the current development of CN system, the prospect of CN for multiple UUVs is brought forward in terms of the requirement, hardware, communication, and so on.
{"title":"Cooperative Navigation System for Multiple Unmanned Underwater Vehicles","authors":"Yao Yao","doi":"10.3182/20130902-3-CN-3020.00127","DOIUrl":"https://doi.org/10.3182/20130902-3-CN-3020.00127","url":null,"abstract":"Abstract In recent yeas, long-range accuracy cooperative navigation(CN) for multiple UUVs under complex marine environment is getting more and more attention. In this paper, firstly, the state-of-the-art of multiple UUV cooperation system is introduced. Then, the characteristic and system structure of multiple UUVs CN are analyzed, as well as the CN methods. Finally, based on the current development of CN system, the prospect of CN for multiple UUVs is brought forward in terms of the requirement, hardware, communication, and so on.","PeriodicalId":90521,"journal":{"name":"IEEE International Conference on Systems Biology : [proceedings]. IEEE International Conference on Systems Biology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83205054","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}
Pub Date : 2013-01-01DOI: 10.3182/20130902-3-CN-3020.00124
Hongliang Li, Derong Liu, Ding Wang
Abstract In this paper, a novel adaptive dynamic programming algorithm based on policy iteration is developed to solve online multi-player non-zero-sum differential game for continuous-time nonlinear systems. This algorithm is mathematically equivalent to the quasi-Newton's iteration in a Banach space. The implementation using neural networks is given, where a critic neural network is used to learn its value function, and an action neural network sharing the same parameters with the corresponding critic neural network is used to learn its optimal control policy for each player. All the critic and action neural networks are updated online in real-time and continuously. A simulation example is presented to demonstrate the effectiveness of the developed scheme.
{"title":"Adaptive Dynamic Programming for Solving Non-Zero-Sum Differential Games","authors":"Hongliang Li, Derong Liu, Ding Wang","doi":"10.3182/20130902-3-CN-3020.00124","DOIUrl":"https://doi.org/10.3182/20130902-3-CN-3020.00124","url":null,"abstract":"Abstract In this paper, a novel adaptive dynamic programming algorithm based on policy iteration is developed to solve online multi-player non-zero-sum differential game for continuous-time nonlinear systems. This algorithm is mathematically equivalent to the quasi-Newton's iteration in a Banach space. The implementation using neural networks is given, where a critic neural network is used to learn its value function, and an action neural network sharing the same parameters with the corresponding critic neural network is used to learn its optimal control policy for each player. All the critic and action neural networks are updated online in real-time and continuously. A simulation example is presented to demonstrate the effectiveness of the developed scheme.","PeriodicalId":90521,"journal":{"name":"IEEE International Conference on Systems Biology : [proceedings]. IEEE International Conference on Systems Biology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76798659","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}
Pub Date : 2013-01-01DOI: 10.3182/20130902-3-CN-3020.00170
Wei He, Shuang Zhang
Abstract In this paper, the control design and stability analysis are presented for a three-dimensional string system with the payload dynamics. A set of partial-ordinary differential equations (PDEs-ODEs) are developed by using the Hamilton's principle to describe the motion of the three-dimensional string system. The dynamic model considers the comprehensive effects of environmental loads, which are critical for the analysis of a string system. Based on the Lyapunov's direct method and the properties of the string system dynamics, three boundary control inputs are applied at the boundary to suppress the vibrations of the system under the external disturbances. Uniformly boundedness of the three-dimensional dynamics with the proposed control is achieved. Exponential stability is proved via the Lyapunov's direct method when there is no distributed disturbance. Simulation examples are provided by using the finite difference method, and some useful conclusions are drawn.
{"title":"Control of a Three-Dimensional String System","authors":"Wei He, Shuang Zhang","doi":"10.3182/20130902-3-CN-3020.00170","DOIUrl":"https://doi.org/10.3182/20130902-3-CN-3020.00170","url":null,"abstract":"Abstract In this paper, the control design and stability analysis are presented for a three-dimensional string system with the payload dynamics. A set of partial-ordinary differential equations (PDEs-ODEs) are developed by using the Hamilton's principle to describe the motion of the three-dimensional string system. The dynamic model considers the comprehensive effects of environmental loads, which are critical for the analysis of a string system. Based on the Lyapunov's direct method and the properties of the string system dynamics, three boundary control inputs are applied at the boundary to suppress the vibrations of the system under the external disturbances. Uniformly boundedness of the three-dimensional dynamics with the proposed control is achieved. Exponential stability is proved via the Lyapunov's direct method when there is no distributed disturbance. Simulation examples are provided by using the finite difference method, and some useful conclusions are drawn.","PeriodicalId":90521,"journal":{"name":"IEEE International Conference on Systems Biology : [proceedings]. IEEE International Conference on Systems Biology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89738141","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}
Pub Date : 2013-01-01DOI: 10.3182/20130902-3-CN-3020.00111
M. Zhou, Z. Zhong, Xinpeng Fang
Abstract In this paper, the influence of sensor-target relative geometry on the potential performance of underwater target localization with hybrid bearing/range sensors is investigated. The optimality criterion function is built on the knowledge of Fisher information matrix (FIM), and another analysis on the mean squared error (MSE) is also presented. For a fixed distance between the sensors to the underwater target, the MSE is minimized if and only if the determinant of the FIM is maximized. The main contribution in this paper is the dependence of the range measurement error on the acoustic propagation distance because of the complex underwater environment, which result in a different FIM expression compared to the ideal assumption case. Simulation results are provided to show the effectiveness of the algorithms presented.
{"title":"Sensor-Target Geometry for Hybrid Bearing/Range Underwater Localization","authors":"M. Zhou, Z. Zhong, Xinpeng Fang","doi":"10.3182/20130902-3-CN-3020.00111","DOIUrl":"https://doi.org/10.3182/20130902-3-CN-3020.00111","url":null,"abstract":"Abstract In this paper, the influence of sensor-target relative geometry on the potential performance of underwater target localization with hybrid bearing/range sensors is investigated. The optimality criterion function is built on the knowledge of Fisher information matrix (FIM), and another analysis on the mean squared error (MSE) is also presented. For a fixed distance between the sensors to the underwater target, the MSE is minimized if and only if the determinant of the FIM is maximized. The main contribution in this paper is the dependence of the range measurement error on the acoustic propagation distance because of the complex underwater environment, which result in a different FIM expression compared to the ideal assumption case. Simulation results are provided to show the effectiveness of the algorithms presented.","PeriodicalId":90521,"journal":{"name":"IEEE International Conference on Systems Biology : [proceedings]. IEEE International Conference on Systems Biology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77850226","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}
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