An Exploration on Adaptive Iterative Learning Control for a Class of Commensurate High-order Uncertain Nonlinear Fractional Order Systems

This paper explores the adaptive iterative learning control method in the control of fractional order systems for the first time. An adaptive iterative learning control(AILC) scheme is presented for a class of commensurate high-order uncertain nonlinear fractional order systems in the presence of disturbance.To facilitate the controller design, a sliding mode surface of tracking errors is designed by using sufficient conditions of linear fractional order systems. To relax the assumption of the identical initial condition in iterative learning control(ILC), a new boundary layer function is proposed by employing MittagLeffler function. The uncertainty in the system is compensated for by utilizing radial basis function neural network. Fractional order differential type updating laws and difference type learning law are designed to estimate unknown constant parameters and time-varying parameter, respectively. The hyperbolic tangent function and a convergent series sequence are used to design robust control term for neural network approximation error and bounded disturbance, simultaneously guaranteeing the learning convergence along iteration. The system output is proved to converge to a small neighborhood of the desired trajectory by constructing Lyapnov-like composite energy function(CEF)containing new integral type Lyapunov function, while keeping all the closed-loop signals bounded. Finally, a simulation example is presented to verify the effectiveness of the proposed approach.

Adaptive Iterative Learning Control for a Class of Nonlinear Time-varying Systems with Unknown Delays and Input Dead-zone

This paper presents an adaptive iterative learning control(AILC) scheme for a class of nonlinear systems with unknown time-varying delays and unknown input dead-zone.A novel nonlinear form of dead-zone nonlinearity is presented.The assumption of identical initial condition for iterative learning control(ILC) is removed by introducing boundary layer function.The uncertainties with time-varying delays are compensated for by using appropriate Lyapunov-Krasovskii functional and Young’s inequality.Radial basis function neural networks are used to model the time-varying uncertainties.The hyperbolic tangent function is employed to avoid the problem of singularity.According to the property of hyperbolic tangent function,the system output is proved to converge to a small neighborhood of the desired trajectory by constructing Lyapunov-like composite energy function(CEF) in two cases,while keeping all the closedloop signals bounded.Finally,a simulation example is presented to verify the effectiveness of the proposed approach.

Efficient Multi-User for Task Offloading and Server Allocation in Mobile Edge Computing Systems

Mobile edge computing has emerged as a new paradigm to enhance computing capabilities by offloading complicated tasks to nearby cloud server.To conserve energy as well as maintain quality of service,low time complexity algorithm is proposed to complete task offloading and server allocation.In this paper,a multi-user with multiple tasks and single server scenario is considered for small network,taking full account of factors including data size,bandwidth,channel state information.Furthermore,we consider a multi-server scenario for bigger network,where the influence of task priority is taken into consideration.To jointly minimize delay and energy cost,we propose a distributed unsupervised learning-based offloading framework for task offloading and server allocation.We exploit a memory pool to store input data and corresponding decisions as key-value pairs for model to learn to solve optimization problems.To further reduce time cost and achieve near-optimal performance,we use convolutional neural networks to process mass data based on fully connected networks.Numerical results show that the proposed algorithm performs better than other offloading schemes,which can generate near-optimal offloading decision timely.

Adaptive repetitive learning control for trajectory-keeping of satellite formation flying

An adaptive repetitive control scheme is proposed for trajectory-keeping of satellite formation flying in the leader–follower mode which is described by Lawden equation.The system is parameterised by power series approximation and the unknown timevarying parameters are estimated by adaptive repetitive learning law.Through rigorous analysis by constructing a Lyapunov-like composite energy function(CEF),the stability of the closed-loop system is proved.Finally,a simulation example is provided to illustrate the effectiveness of the control algorithms proposed in this paper.

奥帕膦酸钠的药物合成及活性研究

骨质疏松症是多种病因引起的一种全身代谢性疾病,以骨量减少、骨组织微观结构退化和骨的力学性能下降为特征,伴随着骨脆性和骨折风险的增加,是一种常见的老年性疾病。在中国,骨质疏松症已成常见病、多发病,发病率呈上升趋势。双磷酸盐作为抑制骨吸收药物在这一领域受到广泛关注,奥帕膦酸钠属于第三代双膦酸盐。本实验研究了奥帕膦酸钠的合成工艺和药理活性。以3-(N,N-二甲氨基)-丙腈为起始原料,对合成工艺进行了改进,降低了成本,反应总收率为49.7%,比文献报道提高了24.0%。药效学实验表明奥帕膦酸钠对骨质疏松症具有明显的治疗作用;毒理学实验显示其无明显的毒副作用。奥帕膦酸钠具有很好的开发前景。