In this paper, we consider the problem of delay-dependent stability for state estimation of neural networks with two additive time–varying delay components via sampleddata control. By constructing a suitable Lyapunov...In this paper, we consider the problem of delay-dependent stability for state estimation of neural networks with two additive time–varying delay components via sampleddata control. By constructing a suitable Lyapunov–Krasovskii functional with triple and four integral terms and by using Jensen's inequality, a new delay-dependent stability criterion is derived in terms of linear matrix inequalities(LMIs) to ensure the asymptotic stability of the equilibrium point of the considered neural networks. Instead of the continuous measurement,the sampled measurement is used to estimate the neuron states, and a sampled-data estimator is constructed. Due to the delay-dependent method, a significant source of conservativeness that could be further reduced lies in the calculation of the time-derivative of the Lyapunov functional. The relationship between the time-varying delay and its upper bound is taken into account when estimating the upper bound of the derivative of Lyapunov functional. As a result, some less conservative stability criteria are established for systems with two successive delay components. Finally, numerical example is given to show the superiority of proposed method.展开更多
In this paper,we investigate the semi-global robust output regulation problem of a class of nonlinear networked control systems.By the emulation approach,we propose a class of sampled-data output feedback control laws...In this paper,we investigate the semi-global robust output regulation problem of a class of nonlinear networked control systems.By the emulation approach,we propose a class of sampled-data output feedback control laws to solve this problem.In particular,we first develop a general sampled-data dynamic output feedback control law and characterize the closed-loop system by a hybrid system.Then,we design the internal model based on the sampled error output of the system.Based on the internal model principle,we convert the semi-global robust output regulation problem into a semi-global robust stabilization problem of an augmented hybrid system composed of the internal model and the original system.By proposing the sampled error output feedback control law and by means of Lyapunov analysis,we obtain the maximum allowable transmission interval for sampling and show that semi-global robust stabilization of the augmented hybrid system can be achieved by the proposed sampled-data control law and thus leading to the solution of the semi-global robust output regulation problem.Finally,we apply the proposed control approach to two practical applications to verify the effectiveness of the proposed control approach.展开更多
文摘In this paper, we consider the problem of delay-dependent stability for state estimation of neural networks with two additive time–varying delay components via sampleddata control. By constructing a suitable Lyapunov–Krasovskii functional with triple and four integral terms and by using Jensen's inequality, a new delay-dependent stability criterion is derived in terms of linear matrix inequalities(LMIs) to ensure the asymptotic stability of the equilibrium point of the considered neural networks. Instead of the continuous measurement,the sampled measurement is used to estimate the neuron states, and a sampled-data estimator is constructed. Due to the delay-dependent method, a significant source of conservativeness that could be further reduced lies in the calculation of the time-derivative of the Lyapunov functional. The relationship between the time-varying delay and its upper bound is taken into account when estimating the upper bound of the derivative of Lyapunov functional. As a result, some less conservative stability criteria are established for systems with two successive delay components. Finally, numerical example is given to show the superiority of proposed method.
基金supported in part by the National Science and Technology Major Project(2021ZD0112600)the National Natural Science Foundation of China(62373058),Beijing Natural Science Foundation(L233003)+1 种基金the National Science Fund for Distinguished Young Scholars of China(62025301)the Basic Science Center Programs of National Nature Science Foundation of China(62088101).
文摘In this paper,we investigate the semi-global robust output regulation problem of a class of nonlinear networked control systems.By the emulation approach,we propose a class of sampled-data output feedback control laws to solve this problem.In particular,we first develop a general sampled-data dynamic output feedback control law and characterize the closed-loop system by a hybrid system.Then,we design the internal model based on the sampled error output of the system.Based on the internal model principle,we convert the semi-global robust output regulation problem into a semi-global robust stabilization problem of an augmented hybrid system composed of the internal model and the original system.By proposing the sampled error output feedback control law and by means of Lyapunov analysis,we obtain the maximum allowable transmission interval for sampling and show that semi-global robust stabilization of the augmented hybrid system can be achieved by the proposed sampled-data control law and thus leading to the solution of the semi-global robust output regulation problem.Finally,we apply the proposed control approach to two practical applications to verify the effectiveness of the proposed control approach.
