Pathological basal ganglia oscillations are associated with the hypokinetic motor symptoms of Parkinson’s disease.In this paper,a memoryless feedback control strategy is proposed to suppress pathological oscillations...Pathological basal ganglia oscillations are associated with the hypokinetic motor symptoms of Parkinson’s disease.In this paper,a memoryless feedback control strategy is proposed to suppress pathological oscillations in the basal ganglia.In the most of closed-loop control strategies,the excitatory subthalamic nucleus populations are both monitored and stimulated targets,neglecting the important contribution of the external globus pallidus populations in suppressing pathological oscillations.To this end,we transform the original model into a time-delay system with a lower-triangular structure,and construct a memoryless state feedback controller utilizing the gain scaling method.It is proved by the Lyapunov–Krasovskii functional method that all the signals of the resulting closed-loop system are bounded,and the system states converge to an adjustable region of the origin.In addition,the input delay in stimulating the target is considered and a corresponding controller is designed to achieve convergence of the states in the resulting closed-loop system with both state delays and input delay.Moreover,simulation tests are conducted to explore the performance of the control strategy.This paper further explores the intrinsic dynamics in the neural system,and provides an effective strategy for closed-loop deep brain stimulation control.展开更多
In this paper, a new class of memoryless non-quasi-Newton method for solving unconstrained optimization problems is proposed, and the global convergence of this method with inexact line search is proved. Furthermore, ...In this paper, a new class of memoryless non-quasi-Newton method for solving unconstrained optimization problems is proposed, and the global convergence of this method with inexact line search is proved. Furthermore, we propose a hybrid method that mixes both the memoryless non-quasi-Newton method and the memoryless Perry-Shanno quasi-Newton method. The global convergence of this hybrid memoryless method is proved under mild assumptions. The initial results show that these new methods are efficient for the given test problems. Especially the memoryless non-quasi-Newton method requires little storage and computation, so it is able to efficiently solve large scale optimization problems.展开更多
The stabilization problem via the linear output feedback controller is addressed for a class of nonlinear systems subject to time-delay.The uncertainty of the system satisfies the lower-triangular growth condition and...The stabilization problem via the linear output feedback controller is addressed for a class of nonlinear systems subject to time-delay.The uncertainty of the system satisfies the lower-triangular growth condition and it is affected by time-delay. A linear output feedback controller with a tunable scaling gain is constructed.By selecting an appropriate Lyapunov-Krasovskii functional the scaling gain can be adjusted to render the closed-loop system globally asymptotically stable.The results can also be extended to the non-triangular nonlinear time-delay systems. The proposed control law together with the observer is linear and memoryless in nature and therefore it is easy to implement in practice. Two computer simulations are conducted to illustrate the effectiveness of the proposed theoretical results.展开更多
基金supported by the Major Fundamental Research Program of the Natural Science Foundation of Shandong Province,China(No.ZR2020ZD25)the Autonomous Innovation Team Foundation for“20 Items of the New University”of Jinan City(No.202228087).
文摘Pathological basal ganglia oscillations are associated with the hypokinetic motor symptoms of Parkinson’s disease.In this paper,a memoryless feedback control strategy is proposed to suppress pathological oscillations in the basal ganglia.In the most of closed-loop control strategies,the excitatory subthalamic nucleus populations are both monitored and stimulated targets,neglecting the important contribution of the external globus pallidus populations in suppressing pathological oscillations.To this end,we transform the original model into a time-delay system with a lower-triangular structure,and construct a memoryless state feedback controller utilizing the gain scaling method.It is proved by the Lyapunov–Krasovskii functional method that all the signals of the resulting closed-loop system are bounded,and the system states converge to an adjustable region of the origin.In addition,the input delay in stimulating the target is considered and a corresponding controller is designed to achieve convergence of the states in the resulting closed-loop system with both state delays and input delay.Moreover,simulation tests are conducted to explore the performance of the control strategy.This paper further explores the intrinsic dynamics in the neural system,and provides an effective strategy for closed-loop deep brain stimulation control.
基金Foundation item： the National Natural Science Foundation of China （No. 60472071） the Science Foundation of Beijing Municipal Commission of Education （No. KM200710028001）.
文摘In this paper, a new class of memoryless non-quasi-Newton method for solving unconstrained optimization problems is proposed, and the global convergence of this method with inexact line search is proved. Furthermore, we propose a hybrid method that mixes both the memoryless non-quasi-Newton method and the memoryless Perry-Shanno quasi-Newton method. The global convergence of this hybrid memoryless method is proved under mild assumptions. The initial results show that these new methods are efficient for the given test problems. Especially the memoryless non-quasi-Newton method requires little storage and computation, so it is able to efficiently solve large scale optimization problems.
基金The National Natural Science Foundation of China(No.61273119,61174076,61004046,61374038)the Natural Science Foundation of Jiangsu Province(No.BK2011253)the Research Fund for the Doctoral Program of Higher Education of China(No.20110092110021)
文摘The stabilization problem via the linear output feedback controller is addressed for a class of nonlinear systems subject to time-delay.The uncertainty of the system satisfies the lower-triangular growth condition and it is affected by time-delay. A linear output feedback controller with a tunable scaling gain is constructed.By selecting an appropriate Lyapunov-Krasovskii functional the scaling gain can be adjusted to render the closed-loop system globally asymptotically stable.The results can also be extended to the non-triangular nonlinear time-delay systems. The proposed control law together with the observer is linear and memoryless in nature and therefore it is easy to implement in practice. Two computer simulations are conducted to illustrate the effectiveness of the proposed theoretical results.
