In this paper, an iterative learning control strategy is presented for a class of nonlinear time-varying systems, the timevarying parameters are expanded into Fourier series with bounded remainder term. The backsteppi...In this paper, an iterative learning control strategy is presented for a class of nonlinear time-varying systems, the timevarying parameters are expanded into Fourier series with bounded remainder term. The backstepping design technique is used to deal with system dynamics with non-global Lipschitz nonlinearities and the approach proposed in this paper solves the non-uniform trajectory tracking problem. Based on the Lyapunov-like synthesis, the proposed method shows that all signals in the closed-loop system remain bounded over a pre-specified time interval [0, T ]. And perfect non-uniform trajectory tracking of the system output is completed. A typical series is introduced in order to deal with the unknown bound of remainder term. Finally, a simulation example shows the feasibility and effectiveness of the approach.展开更多
Predicting the future information and recovering the missing data for time series are two vital tasks faced in various application fields.They are often subjected to big challenges,especially when the signal is nonlin...Predicting the future information and recovering the missing data for time series are two vital tasks faced in various application fields.They are often subjected to big challenges,especially when the signal is nonlinear and nonstationary which is common in practice.In this paper,we propose a hybrid 2-stage approach,named IF2FNN,to predict(including short-term and long-term predictions)and recover the general types of time series.In the first stage,we decompose the original non-stationary series into several“quasi stationary”intrinsic mode functions(IMFs)by the iterative filtering(IF)method.In the second stage,all of the IMFs are fed as the inputs to the factorization machine based neural network model to perform the prediction and recovery.We test the strategy on five datasets including an artificial constructed signal(ACS),and four real-world signals:the length of day(LOD),the northern hemisphere land-ocean temperature index(NHLTI),the troposphere monthly mean temperature(TMMT),and the national association of securities dealers automated quotations index(NASDAQ).The results are compared with those obtained from the other prevailing methods.Our experiments indicate that under the same conditions,the proposed method outperforms the others for prediction and recovery according to various metrics such as mean absolute error(MAE),root mean square error(RMSE),and mean absolute percentage error(MAPE).展开更多
An adaptive iterative learning control scheme is presented for a class of strict-feedback nonlinear time-delay systems, with unknown nonlinearly parameterised and time-varying disturbed functions of known periods. Rad...An adaptive iterative learning control scheme is presented for a class of strict-feedback nonlinear time-delay systems, with unknown nonlinearly parameterised and time-varying disturbed functions of known periods. Radial basis function neural network and Fourier series expansion （FSE） are combined into a new function approximator to model each suitable disturbed function in systems. The requirement of the traditional iterative learning control algorithm on the nonlinear functions （such as global Lipschitz condition） is relaxed. Furthermore, by using appropriate Lyapunov-Krasovskii functionals, all signs in the closed loop system are guaranteed to be semiglobally uniformly ultimately bounded, and the output of the system is proved to converge to the desired trajectory. A simulation example is provided to illustrate the effectiveness of the control scheme.展开更多
In this paper,we consider the double-satellite localization under the earth ellipsoid model of the Wideband Geodetic System(WGS-84)using the Time Difference of Arrival(TDOA)and the Angle-of-Arrival(AOA).Several closed...In this paper,we consider the double-satellite localization under the earth ellipsoid model of the Wideband Geodetic System(WGS-84)using the Time Difference of Arrival(TDOA)and the Angle-of-Arrival(AOA).Several closed-form solution algorithms via the pseudolinearization of the measurement equations are presented to efficiently estimate the location.These algorithms include the Weighted Least Squares(WLS),the Constrained Total Least Squares(CTLS),and the Taylor-Series Iteration(TSI).Performance comparison of the proposed methods with the Cramér-Rao Lower Bound(CRLB)in the simulation is shown to demonstrate that the proposed algorithms are feasible and have stable performance.展开更多
基金supported by National Natural Science Foundation of China(No.60974139)Fundamental Research Funds for the Central Universities(No.72103676)
文摘In this paper, an iterative learning control strategy is presented for a class of nonlinear time-varying systems, the timevarying parameters are expanded into Fourier series with bounded remainder term. The backstepping design technique is used to deal with system dynamics with non-global Lipschitz nonlinearities and the approach proposed in this paper solves the non-uniform trajectory tracking problem. Based on the Lyapunov-like synthesis, the proposed method shows that all signals in the closed-loop system remain bounded over a pre-specified time interval [0, T ]. And perfect non-uniform trajectory tracking of the system output is completed. A typical series is introduced in order to deal with the unknown bound of remainder term. Finally, a simulation example shows the feasibility and effectiveness of the approach.
基金the National Natural Science Foundation of China under Grant Nos.11771458,431015 and 61628203the National Science Foundation of US under Grant Nos.DMS-1620345 and DMS-1830225+3 种基金the Office of Naval Research(ONR)Award of US under Grant No.N00014-18-1-2852the Guangdong Youth Innovation Talent Project(Natural Sciences)under Grant No.2017KQNCX083the Guangdong Philosophy and Social Science Project of China under Grant No.GD15CGL11the Guangzhou Science and Technology Project of China under Grant No.201707010495.
文摘Predicting the future information and recovering the missing data for time series are two vital tasks faced in various application fields.They are often subjected to big challenges,especially when the signal is nonlinear and nonstationary which is common in practice.In this paper,we propose a hybrid 2-stage approach,named IF2FNN,to predict(including short-term and long-term predictions)and recover the general types of time series.In the first stage,we decompose the original non-stationary series into several“quasi stationary”intrinsic mode functions(IMFs)by the iterative filtering(IF)method.In the second stage,all of the IMFs are fed as the inputs to the factorization machine based neural network model to perform the prediction and recovery.We test the strategy on five datasets including an artificial constructed signal(ACS),and four real-world signals:the length of day(LOD),the northern hemisphere land-ocean temperature index(NHLTI),the troposphere monthly mean temperature(TMMT),and the national association of securities dealers automated quotations index(NASDAQ).The results are compared with those obtained from the other prevailing methods.Our experiments indicate that under the same conditions,the proposed method outperforms the others for prediction and recovery according to various metrics such as mean absolute error(MAE),root mean square error(RMSE),and mean absolute percentage error(MAPE).
基金supported by National Natural Science Foundation of China (No. 72103676)partially supported by the Fundamental Research Funds for the Central Universities
文摘An adaptive iterative learning control scheme is presented for a class of strict-feedback nonlinear time-delay systems, with unknown nonlinearly parameterised and time-varying disturbed functions of known periods. Radial basis function neural network and Fourier series expansion （FSE） are combined into a new function approximator to model each suitable disturbed function in systems. The requirement of the traditional iterative learning control algorithm on the nonlinear functions （such as global Lipschitz condition） is relaxed. Furthermore, by using appropriate Lyapunov-Krasovskii functionals, all signs in the closed loop system are guaranteed to be semiglobally uniformly ultimately bounded, and the output of the system is proved to converge to the desired trajectory. A simulation example is provided to illustrate the effectiveness of the control scheme.
基金supported by Meteorological information and Signal Processing Key Laboratory of Sichuan Higher Education Institutes of Chengdu University of Information Technology,China(No.QXXCSYS201702)
文摘In this paper,we consider the double-satellite localization under the earth ellipsoid model of the Wideband Geodetic System(WGS-84)using the Time Difference of Arrival(TDOA)and the Angle-of-Arrival(AOA).Several closed-form solution algorithms via the pseudolinearization of the measurement equations are presented to efficiently estimate the location.These algorithms include the Weighted Least Squares(WLS),the Constrained Total Least Squares(CTLS),and the Taylor-Series Iteration(TSI).Performance comparison of the proposed methods with the Cramér-Rao Lower Bound(CRLB)in the simulation is shown to demonstrate that the proposed algorithms are feasible and have stable performance.
