The paper forwards a new model describing liquidity risk basing on trading data during one day, and applies it to the experimental analysis of Chinese stock market.
This paper proposes a novel neural adaptive performance-constrained synchronization tracking control algorithm for multiple hypersonic flight vehicles(HFVs),which are subject to actuator faults and full-state constrai...This paper proposes a novel neural adaptive performance-constrained synchronization tracking control algorithm for multiple hypersonic flight vehicles(HFVs),which are subject to actuator faults and full-state constraints.The proposed method is based on advanced Lyapunov finite-time stability theory and a sophisticated backstepping design scheme.The longitudinal model of HFV is converted into velocity and altitude subsystems through functional decomposition.Our method presents three significant contributions over the existing state-of-the-art approaches:(a)ensuring finite-time convergence of HFVs systems by guaranteeing that the setting time is lower bounded by a positive constant that is related to the initial states;(b)utilizing a tan-type Barrier Lyapunov function(BLF)to ensure that the synchronization tracking errors of velocity,altitude,flight path angle,angle of attack,and pitch angle rate are maintained within certain performance bounds;and(c)designing a neural adaptive control algorithm and adaptive parameter laws by combining the backstepping design technique and radial basisfunction neural networks(RBFNNs)to handle unknown actuator faults and modeling uncer-tainties.Finally,comparative simulations are conducted to validate the efficacy of the proposed scheme.展开更多
文摘The paper forwards a new model describing liquidity risk basing on trading data during one day, and applies it to the experimental analysis of Chinese stock market.
文摘This paper proposes a novel neural adaptive performance-constrained synchronization tracking control algorithm for multiple hypersonic flight vehicles(HFVs),which are subject to actuator faults and full-state constraints.The proposed method is based on advanced Lyapunov finite-time stability theory and a sophisticated backstepping design scheme.The longitudinal model of HFV is converted into velocity and altitude subsystems through functional decomposition.Our method presents three significant contributions over the existing state-of-the-art approaches:(a)ensuring finite-time convergence of HFVs systems by guaranteeing that the setting time is lower bounded by a positive constant that is related to the initial states;(b)utilizing a tan-type Barrier Lyapunov function(BLF)to ensure that the synchronization tracking errors of velocity,altitude,flight path angle,angle of attack,and pitch angle rate are maintained within certain performance bounds;and(c)designing a neural adaptive control algorithm and adaptive parameter laws by combining the backstepping design technique and radial basisfunction neural networks(RBFNNs)to handle unknown actuator faults and modeling uncer-tainties.Finally,comparative simulations are conducted to validate the efficacy of the proposed scheme.
