In this paper,we investigate the distributed estimation problem of continuous-time stochastic dynamic systems over sensor networks when both the system order and parameters are unknown.We propose a local information c...In this paper,we investigate the distributed estimation problem of continuous-time stochastic dynamic systems over sensor networks when both the system order and parameters are unknown.We propose a local information criterion(LIC)based on the L_(0)penalty term.By minimizing LIC at the diffusion time instant and utilizing the continuous-time diffusion least squares algorithm,we obtain a distributed estimation algorithm to simultaneously estimate the unknown order and the parameters of the system.By dealing with the effect of the system noises and the coupling relationship between estimation of system orders and parameters,we establish the almost sure convergence results of the proposed distributed estimation algorithm.Furthermore,we give a simulation example to verify the effectiveness of the distributed algorithm in estimating the system order and parameters.展开更多
This work presents a new design method based on differentialgeometry andthe nonlinear H∞approach which has verified thatthe H∞controlforthe feedback linearization system is equivalentto a nonlinear H∞control fort...This work presents a new design method based on differentialgeometry andthe nonlinear H∞approach which has verified thatthe H∞controlforthe feedback linearization system is equivalentto a nonlinear H∞control forthe primitive nonlinear controlsystem in the sense of differential game theory.In addition,this kind of design methodis usedfornonlinearrobust optimalexcitation controlofa multi machine system .The controllerconstructed isimplemented via purely local measurement. Moreover,itisindependent ofthe parameters of power networks. Simulations are performed on a single infinite system .It has been demonstrated thatthe nonlinear H∞excitation controlleris more effective than the other nonlinear excitation controllerin dynamic performance improvementfor variation of operationalstates and parametersin powersystems.展开更多
基金supported by the National Key R&D Program of China(No.2018YFA0703800)the Natural Science Foundation of China(No.T2293770)+1 种基金the Strategic Priority Research Program of Chinese Academy of Sciences(No.XDA27000000)the National Science Foundation of Shandong Province(No.ZR2020ZD26).
文摘In this paper,we investigate the distributed estimation problem of continuous-time stochastic dynamic systems over sensor networks when both the system order and parameters are unknown.We propose a local information criterion(LIC)based on the L_(0)penalty term.By minimizing LIC at the diffusion time instant and utilizing the continuous-time diffusion least squares algorithm,we obtain a distributed estimation algorithm to simultaneously estimate the unknown order and the parameters of the system.By dealing with the effect of the system noises and the coupling relationship between estimation of system orders and parameters,we establish the almost sure convergence results of the proposed distributed estimation algorithm.Furthermore,we give a simulation example to verify the effectiveness of the distributed algorithm in estimating the system order and parameters.
文摘This work presents a new design method based on differentialgeometry andthe nonlinear H∞approach which has verified thatthe H∞controlforthe feedback linearization system is equivalentto a nonlinear H∞control forthe primitive nonlinear controlsystem in the sense of differential game theory.In addition,this kind of design methodis usedfornonlinearrobust optimalexcitation controlofa multi machine system .The controllerconstructed isimplemented via purely local measurement. Moreover,itisindependent ofthe parameters of power networks. Simulations are performed on a single infinite system .It has been demonstrated thatthe nonlinear H∞excitation controlleris more effective than the other nonlinear excitation controllerin dynamic performance improvementfor variation of operationalstates and parametersin powersystems.
