Global stabilization for a class of nonlinear time-delay systems using linear output feedback

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.

Adaptive neural network tracking control for a class of unknown nonlinear time-delay systems

For a class of unknown nonlinear time-delay systems, an adaptive neural network （NN） control design approach is proposed. Backstepping, domination and adaptive bounding design technique are combined to construct a robust memoryless adaptive NN tracking controller. Unknown time-delay functions are approximated by NNs, such that the requirement on the nonlinear time-delay functions is relaxed. Based on Lyapunov-Krasoviskii functional, the sem-global uniformly ultimately boundedness （UUB） of all the signals in the closed-loop system is proved. The arbitrary output tracking accuracy is achieved by tuning the design parameters. The feasibility is investigated by an illustrative simulation example.

基于Memoryless算法的河道水下地形模型简化研究

模型简化技术能有效缓解海量数据分析与计算机资源不足之间的矛盾。首先根据河道水下地形模型的实际情况,对Memoryless算法进行了改进,使得简化后的模型能更好地规避狭长三角形;然后根据水文分析理论,在改进Memoryless简化算法中增加河道地性线约束,使得河道地形在地性线周围区域的细节能够得到较好的保持;最后通过两组不同来源的数据进行不同简化比率下的简化实验。结果表明,简化后的地形模型效果良好,误差在可接受范围内,并较好地保持了河道地形的几何细节特征。

Delay-dependent robust H-infinity control for discrete-time uncertain systems with time-varying state delays

This paper examines the delay-dependent H-infinity control problem for discrete-time linear systems with time-varying state delays and norm-bounded uncertainties. A new inequality for the finite sum of quadratic terms is first established. Then, some new delay-dependent criteria are derived by employing the new inequality to guarantee the robust stability of a closed-loop system with a prescribed H-infinity norm bound for all admissible uncertainties and bounded time-vary delays. A numerical example demonstrates that the proposed method is an improvement over existing ones.

基于Memoryless算法的三角网格简化方法

在研究三角网格模型简化的意义和技术的基础上,利用Memoryless边折叠简化算法对经典三维网格模型以及河道DEM网格模型进行简化,实验证明Memoryless算法能够很好地完成简化任务,同时相较传统方法,该方法能够较好地保持简化模型的边界、体积,确保模型在简化过程中不会出现明显退化。

改进的Memoryless简化算法研究

针对地形模型简化之后,模型的边界和细节特征存在一定变化的问题,该文提出了一种改进的Memoryless简化算法。该算法在原始Memoryless算法的面积和体积约束基础上,增加三角形形状约束条件,优化了简化算法。利用经典bunny三维网络模型,调整模型参数,从不同简化比率的角度与原始Memoryless算法简化效果进行对比。实验结果表明,本文提出的算法在较高简化比率下,模型的边界和几何细节特征保持都优于原始简化算法,能够更适用于地形模型的简化,较大限度地保留了原始地形形态。

A Concise Proof of the Relation between Poisson Process and Exponential Distribution

Using the memoryless property of the exponential distribution, we have proved again that the relation between the Poisson process and the exponential distribution, that is, the stochastic process {N(t), t≥0} is said to be a Poisson process with arrival rate λ(】0) if and only if the sequence of interarrival times {τ n,n≥1} are independent and identically distributed according to an exponential distribution with parameter λ, where N(t) denotes the arrival number in (0,t\].. It′s noting that the proof provided in this paper is concise and intuitive.

Derivation and Global Convergence for Memoryless Non-quasi-Newton Method

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.

Pathological basal ganglia oscillations with time delays:a memoryless feedback control strategy

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.