This paper is concerned with state estimation problem for Markov jump linear systems where the disturbances involved in the systems equations and measurement equations are assumed to be Gaussian noise sequences.Based ...This paper is concerned with state estimation problem for Markov jump linear systems where the disturbances involved in the systems equations and measurement equations are assumed to be Gaussian noise sequences.Based on two properties of conditional expectation,orthogonal projective theorem is applied to the state estimation problem of the considered systems so that a novel suboptimal algorithm is obtained.The novelty of the algorithm lies in using orthogonal projective theorem instead of Kalman filters to estimate the state.A numerical comparison of the algorithm with the interacting multiple model algorithm is given to illustrate the effectiveness of the proposed algorithm.展开更多
The receding horizon control（RHC） problem is considered for nonlinear Markov jump systems which can be represented by Takagi-Sugeno fuzzy models subject to constraints both on control inputs and on observe outputs.I...The receding horizon control（RHC） problem is considered for nonlinear Markov jump systems which can be represented by Takagi-Sugeno fuzzy models subject to constraints both on control inputs and on observe outputs.In the given receding horizon,for each mode sequence of the T-S modeled nonlinear system with Markov jump parameter,the cost function is optimized by constraints on state trajectories,so that the optimization control input sequences are obtained in order to make the state into a terminal invariant set.Out of the receding horizon,the stability is guaranteed by searching a state feedback control law.Based on such stability analysis,a linear matrix inequality approach for designing receding horizon predictive controller for nonlinear systems subject to constraints both on the inputs and on the outputs is developed.The simulation shows the validity of this method.展开更多
In this paper, global exponential stochastic stability based continuous gain-scheduled robust L-two-L- infinity filtering problem is studied for a class of stochastic neutral systems subject to time-varying parameters...In this paper, global exponential stochastic stability based continuous gain-scheduled robust L-two-L- infinity filtering problem is studied for a class of stochastic neutral systems subject to time-varying parameters. First, the stochastic time-varying neutral systems are described by a series of stochastic time-constant systems at some selected time points, then based on stochastic Lyapunov-Krasovskii functional approach, a new globally exponentially stochastically stabilizable criterion is derived for each of the jumping system by means of linear matrix inequalities. Subsequently, L- two-L-infinity filtering systems are designed for such linear jump systems. Finally; continuous gain-scheduled approach is employed to design time-varying filter systems for the whole working region. A simulation example shows the effectiveness and potential of the developed techniques.展开更多
Markov jump linear systems are defined as a family of linear systems with randomly Markov jumping parameters and are used to model systems subject to failures or changes in structure. The robust stabilization problem ...Markov jump linear systems are defined as a family of linear systems with randomly Markov jumping parameters and are used to model systems subject to failures or changes in structure. The robust stabilization problem of jump linear delay system with umcerratnty was studied. By using of linear matrix inequalities, the existence conditions of robust stabilizing and the state feedback controller designing methods are also presented and proved. Finally, an illustrated example shows the effectiveness of this approach.展开更多
基金supported by the National Natural Science Foundation of China (No. 50977008,60521003,60774048)
文摘This paper is concerned with state estimation problem for Markov jump linear systems where the disturbances involved in the systems equations and measurement equations are assumed to be Gaussian noise sequences.Based on two properties of conditional expectation,orthogonal projective theorem is applied to the state estimation problem of the considered systems so that a novel suboptimal algorithm is obtained.The novelty of the algorithm lies in using orthogonal projective theorem instead of Kalman filters to estimate the state.A numerical comparison of the algorithm with the interacting multiple model algorithm is given to illustrate the effectiveness of the proposed algorithm.
基金supported by the National Natural Science Foundation of China (6097400160904045)+1 种基金National Natural Science Foundation of Jiangsu Province (BK2009068)Six Projects Sponsoring Talent Summits of Jiangsu Province
文摘The receding horizon control（RHC） problem is considered for nonlinear Markov jump systems which can be represented by Takagi-Sugeno fuzzy models subject to constraints both on control inputs and on observe outputs.In the given receding horizon,for each mode sequence of the T-S modeled nonlinear system with Markov jump parameter,the cost function is optimized by constraints on state trajectories,so that the optimization control input sequences are obtained in order to make the state into a terminal invariant set.Out of the receding horizon,the stability is guaranteed by searching a state feedback control law.Based on such stability analysis,a linear matrix inequality approach for designing receding horizon predictive controller for nonlinear systems subject to constraints both on the inputs and on the outputs is developed.The simulation shows the validity of this method.
基金supported by the National Natural Science Foundation of China (No. 60974001)
文摘In this paper, global exponential stochastic stability based continuous gain-scheduled robust L-two-L- infinity filtering problem is studied for a class of stochastic neutral systems subject to time-varying parameters. First, the stochastic time-varying neutral systems are described by a series of stochastic time-constant systems at some selected time points, then based on stochastic Lyapunov-Krasovskii functional approach, a new globally exponentially stochastically stabilizable criterion is derived for each of the jumping system by means of linear matrix inequalities. Subsequently, L- two-L-infinity filtering systems are designed for such linear jump systems. Finally; continuous gain-scheduled approach is employed to design time-varying filter systems for the whole working region. A simulation example shows the effectiveness and potential of the developed techniques.
文摘Markov jump linear systems are defined as a family of linear systems with randomly Markov jumping parameters and are used to model systems subject to failures or changes in structure. The robust stabilization problem of jump linear delay system with umcerratnty was studied. By using of linear matrix inequalities, the existence conditions of robust stabilizing and the state feedback controller designing methods are also presented and proved. Finally, an illustrated example shows the effectiveness of this approach.
