Motivated by state estimation and adaptive control of large-scale complex power systems,this paper proposes a cascaded sliding-mode observer for high-order systems with lower-triangular structure and not necessarily i...Motivated by state estimation and adaptive control of large-scale complex power systems,this paper proposes a cascaded sliding-mode observer for high-order systems with lower-triangular structure and not necessarily in Byrnes-Isidori Normal Form.Key information about the known nonlinear terms of the system is integrated into different blocks of the proposed observer.Under appropriate parameter design rules,the states of the proposed observer will quickly reach and slide on the intersection of sliding surfaces.During this sliding phase,the estimation errors rapidly converge to negligibly small values,determined by a parameter of the observer.Compared with standard high-gain observers and classical high-gain parameter embedded sliding-mode observers,the proposed observer achieves similar estimation error convergence speed with smaller gain coefficients.Moreover,the peaking phenomenon of the proposed observer is less severe.Besides,the structure of the proposed observer is more flexible than that of some well-known cascaded high-gain observers as there is no restriction on the dimension of the blocks of the proposed observer.Simulation studies are carried out on a fifth-order nonlinear system and a 10-machine 48-bus power system to further demonstrate the features of the proposed observer and its application on adaptive transient stability control of wind farms penetrated power systems.展开更多
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
文摘Motivated by state estimation and adaptive control of large-scale complex power systems,this paper proposes a cascaded sliding-mode observer for high-order systems with lower-triangular structure and not necessarily in Byrnes-Isidori Normal Form.Key information about the known nonlinear terms of the system is integrated into different blocks of the proposed observer.Under appropriate parameter design rules,the states of the proposed observer will quickly reach and slide on the intersection of sliding surfaces.During this sliding phase,the estimation errors rapidly converge to negligibly small values,determined by a parameter of the observer.Compared with standard high-gain observers and classical high-gain parameter embedded sliding-mode observers,the proposed observer achieves similar estimation error convergence speed with smaller gain coefficients.Moreover,the peaking phenomenon of the proposed observer is less severe.Besides,the structure of the proposed observer is more flexible than that of some well-known cascaded high-gain observers as there is no restriction on the dimension of the blocks of the proposed observer.Simulation studies are carried out on a fifth-order nonlinear system and a 10-machine 48-bus power system to further demonstrate the features of the proposed observer and its application on adaptive transient stability control of wind farms penetrated power systems.
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.
