This paper studies the problem of robust H∞ output feedback controller via state-reset for linear uncertain discrete-time switched systems. Using multiple Lyapunov functions,we address an output feedback controller u...This paper studies the problem of robust H∞ output feedback controller via state-reset for linear uncertain discrete-time switched systems. Using multiple Lyapunov functions,we address an output feedback controller under arbitrary switching signals,in which an H∞ performance is required. The condition is shown in the form of linear matrix inequalities (LMI). Finally,a numerical example shows the feasibility of the designed controller and illustrates that the new sufficient condition has lower conservation and more optimized H∞ tfperformance.展开更多
This paper deals with the problem of switching between an open-loop estimator and a close-loop estimator for compensating transmission error and packet dropout of networked control systems. Switching impulse is consid...This paper deals with the problem of switching between an open-loop estimator and a close-loop estimator for compensating transmission error and packet dropout of networked control systems. Switching impulse is considered in order to reduce the error between theory and application, a sufficient condition for exponential stabilization of networked control systems under a given switching rule is presented by multiple Lyapunov-like functions. These results are presented for both continuous-time and discrete-time domains. Controllers are designed by means of linear matrix inequalities. Sim- ulation results show the feasibility and efficiency of the proposed method.展开更多
To identify systems with non-uniformly sampled input data, a recursive Bayesian identification algorithm with covariance resetting is proposed. Using estimated noise transfer function as a dynamic filter, the system w...To identify systems with non-uniformly sampled input data, a recursive Bayesian identification algorithm with covariance resetting is proposed. Using estimated noise transfer function as a dynamic filter, the system with colored noise is transformed into the system with white noise. In order to improve estimates, the estimated noise variance is employed as a weighting factor in the algorithm. Meanwhile, a modified covariance resetting method is also integrated in the proposed algorithm to increase the convergence rate. A numerical example and an industrial example validate the proposed algorithm.展开更多
文摘This paper studies the problem of robust H∞ output feedback controller via state-reset for linear uncertain discrete-time switched systems. Using multiple Lyapunov functions,we address an output feedback controller under arbitrary switching signals,in which an H∞ performance is required. The condition is shown in the form of linear matrix inequalities (LMI). Finally,a numerical example shows the feasibility of the designed controller and illustrates that the new sufficient condition has lower conservation and more optimized H∞ tfperformance.
基金supported by National Natural Science Foundation of China(61403118,61174073,61233002,11271106)the IAPI Fundamental Research Funds(2013ZCX03-01)+1 种基金the Natural Science Foundation of Hebei Province(F2015201088)the Science and Technology Foundation of Hebei Province(QN20131056)
基金This work was supported by the National Natural Science Foundation of China (No.60574013, 60274009), and the Natural Science Fundation ofLiaoning Province (No.20032020).
文摘This paper deals with the problem of switching between an open-loop estimator and a close-loop estimator for compensating transmission error and packet dropout of networked control systems. Switching impulse is considered in order to reduce the error between theory and application, a sufficient condition for exponential stabilization of networked control systems under a given switching rule is presented by multiple Lyapunov-like functions. These results are presented for both continuous-time and discrete-time domains. Controllers are designed by means of linear matrix inequalities. Sim- ulation results show the feasibility and efficiency of the proposed method.
基金supported by National Natural Science Foundation of China(Nos.61273142 and 51477070)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)Foundation for Six Talents by Jiangsu Province and Graduate Scientific Innovation Projects of Jiangsu University(No.KYXX_0003)
文摘To identify systems with non-uniformly sampled input data, a recursive Bayesian identification algorithm with covariance resetting is proposed. Using estimated noise transfer function as a dynamic filter, the system with colored noise is transformed into the system with white noise. In order to improve estimates, the estimated noise variance is employed as a weighting factor in the algorithm. Meanwhile, a modified covariance resetting method is also integrated in the proposed algorithm to increase the convergence rate. A numerical example and an industrial example validate the proposed algorithm.
